In [946]:
# coding = utf-8
'''
本次数据处理用于财务数据是否舞弊的预测分类，整个处理过程分为2部分：
    1、数据清洗。包括样本的筛选、统计描述、缺失值的处理以及少量统计可视化。
    2、分类器模型训练。包括输入输出的规整(reshape)、假设检验、特征选择、模型训练和分类器性能评估。                       
'''
Out[946]:
'\n本次数据处理用于财务数据是否舞弊的预测分类，整个处理过程分为2部分：\n    1、数据清洗。包括样本的筛选、统计描述、缺失值的处理以及少量统计可视化。\n    2、分类器模型训练。包括输入输出的规整(reshape)、假设检验、特征选择、模型训练和分类器性能评估。                       \n'
In [328]:
import warnings
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import scipy as sc
import pdb
In [329]:
warnings.filterwarnings('ignore')    # "error", "ignore", "always", "default", "module", "once"
In [330]:
# 定义源数据文件名
title = ["debt", "dev", "risk", "stock", "op", "manage", "audit", "violate", "cash", "nps", "net", "govern", "governother"]
title_chinese = ["偿债能力", "发展能力", "风险水平", "股本结构", "经营能力", "内控是否有效", "审计意见", "违规处理公司", "现金流量",
                 "现金流量---每股现金净流量和每股经营", "盈利能力", "治理结构--会议情况", "治理结构其余数据"]
title_check = list(zip(title, title_chinese))

# 用字典定义文件目录路径
filespath = {title[i] : "Raw Data/" + title_chinese[i] + "/" for i in range(len(title))}
filespath
Out[330]:
{'audit': 'Raw Data/审计意见/',
 'cash': 'Raw Data/现金流量/',
 'debt': 'Raw Data/偿债能力/',
 'dev': 'Raw Data/发展能力/',
 'govern': 'Raw Data/治理结构--会议情况/',
 'governother': 'Raw Data/治理结构其余数据/',
 'manage': 'Raw Data/内控是否有效/',
 'net': 'Raw Data/盈利能力/',
 'nps': 'Raw Data/现金流量---每股现金净流量和每股经营/',
 'op': 'Raw Data/经营能力/',
 'risk': 'Raw Data/风险水平/',
 'stock': 'Raw Data/股本结构/',
 'violate': 'Raw Data/违规处理公司/'}
In [331]:
# 用字典定义各文件目录下的数据文件
filesset = {}
filesset[title[0]] = ["FI_T1", "FI_T11", "FI_T12", "FI_T13"]
filesset[title[1]] = ["FI_T8", "FI_T81", "FI_T82", "FI_T83"]
filesset[title[2]] = ["FI_T7", "FI_T71", "FI_T72", "FI_T73"]
filesset[title[3]] = ["HLD_Capstru"]
filesset[title[4]] = ["FI_T4", "FI_T41","FI_T42", "FI_T43"]
filesset[title[5]] = ["IC_EvaluationRepInfo"]
filesset[title[6]] = ["FIN_Audit"]
filesset[title[7]] = ["STK_Violation_Main"]
filesset[title[8]] = ["FI_T6", "FI_T61", "FI_T62", "FI_T63"]
filesset[title[9]] = ["FI_T9", "FI_T91", "FI_T92", "FI_T93"]
filesset[title[10]] = ["FI_T5", "FI_T51", "FI_T52", "FI_T53"]
filesset[title[11]] = ["CG_Agm"]
filesset[title[12]] = ["CG_Ybasic"]

# 生成各财务主题目录下的文件路径
filespath = {key : [filespath[key] + filesset[key][i] + ".csv" for i in range(len(filesset[key]))] for key in filespath}
filespath
Out[331]:
{'audit': ['Raw Data/审计意见/FIN_Audit.csv'],
 'cash': ['Raw Data/现金流量/FI_T6.csv',
  'Raw Data/现金流量/FI_T61.csv',
  'Raw Data/现金流量/FI_T62.csv',
  'Raw Data/现金流量/FI_T63.csv'],
 'debt': ['Raw Data/偿债能力/FI_T1.csv',
  'Raw Data/偿债能力/FI_T11.csv',
  'Raw Data/偿债能力/FI_T12.csv',
  'Raw Data/偿债能力/FI_T13.csv'],
 'dev': ['Raw Data/发展能力/FI_T8.csv',
  'Raw Data/发展能力/FI_T81.csv',
  'Raw Data/发展能力/FI_T82.csv',
  'Raw Data/发展能力/FI_T83.csv'],
 'govern': ['Raw Data/治理结构--会议情况/CG_Agm.csv'],
 'governother': ['Raw Data/治理结构其余数据/CG_Ybasic.csv'],
 'manage': ['Raw Data/内控是否有效/IC_EvaluationRepInfo.csv'],
 'net': ['Raw Data/盈利能力/FI_T5.csv',
  'Raw Data/盈利能力/FI_T51.csv',
  'Raw Data/盈利能力/FI_T52.csv',
  'Raw Data/盈利能力/FI_T53.csv'],
 'nps': ['Raw Data/现金流量---每股现金净流量和每股经营/FI_T9.csv',
  'Raw Data/现金流量---每股现金净流量和每股经营/FI_T91.csv',
  'Raw Data/现金流量---每股现金净流量和每股经营/FI_T92.csv',
  'Raw Data/现金流量---每股现金净流量和每股经营/FI_T93.csv'],
 'op': ['Raw Data/经营能力/FI_T4.csv',
  'Raw Data/经营能力/FI_T41.csv',
  'Raw Data/经营能力/FI_T42.csv',
  'Raw Data/经营能力/FI_T43.csv'],
 'risk': ['Raw Data/风险水平/FI_T7.csv',
  'Raw Data/风险水平/FI_T71.csv',
  'Raw Data/风险水平/FI_T72.csv',
  'Raw Data/风险水平/FI_T73.csv'],
 'stock': ['Raw Data/股本结构/HLD_Capstru.csv'],
 'violate': ['Raw Data/违规处理公司/STK_Violation_Main.csv']}
In [332]:
# 用字典推导式生成字典结构的TextFileReader iterable object。
files_reader = {key : pd.concat(pd.read_csv(filepath, iterator=True, chunksize=10000, delimiter=","), ignore_index=True) for key in filespath 
         for filepath in filespath[key]}

# 报表类型编码A表示合并报表，B表示母公司报表。一般财务考察合并报表。
files_reader["debt"].head()
Out[332]:
Stkcd Accper Typrep Indcd F010101A F010201A F010301A F010401A F010702B F011201A F011601A F011701A F011801A F012601B
0 股票代码 截止日期 报表类型编码 行业代码 流动比率 速动比率 保守速动比率 现金比率 利息保障倍数B 资产负债率 权益乘数 产权比率 权益对负债比率 有形净值债务率
1 没有单位 没有单位 没有单位 没有单位 没有单位 没有单位 没有单位 没有单位 没有单位 没有单位 没有单位 没有单位 没有单位 没有单位
2 600675 2005/6/30 A K70 1.389744 0.371053 0.294101 NaN 4.612133 0.697352 3.304172 2.304172 0.433995 2.325622
3 600675 2005/6/30 B K70 0.789221 0.689505 0.623871 0.122484 6.461849 0.540862 2.177996 1.177996 0.848899 NaN
4 600675 2005/9/30 A K70 1.572825 0.575876 0.4889 NaN 6.23885 0.674823 3.075247 2.075247 0.48187 2.092604
In [333]:
# 对存在报表类型编码的dataframe分别筛选出A和B类型。A为合并报表，是财务上主要考察的报表。B报表是母公司报表，相对次要。
areport = {key : files_reader[key][(files_reader[key]["Typrep"] == "A")] 
           if "Typrep" in files_reader[key].columns else files_reader[key] for key in title}
breport = {key : files_reader[key][(files_reader[key].Typrep == "B")] 
           if "Typrep" in files_reader[key].columns else files_reader[key] for key in title}

# title = ["debt", "dev", "risk", "stock", "op", "manage", "audit", "violate", "cash", "nps", "net", "govern", "governother"]
areport["debt"].head()
Out[333]:
Stkcd Accper Typrep Indcd F010101A F010201A F010301A F010401A F010702B F011201A F011601A F011701A F011801A F012601B
2 600675 2005/6/30 A K70 1.389744 0.371053 0.294101 NaN 4.612133 0.697352 3.304172 2.304172 0.433995 2.325622
4 600675 2005/9/30 A K70 1.572825 0.575876 0.4889 NaN 6.23885 0.674823 3.075247 2.075247 0.48187 2.092604
6 600675 2005/12/31 A K70 1.448779 0.451555 0.434195 NaN 15.877637 0.666081 2.994737 1.994737 0.501319 2.01045
9 600675 2006/3/31 A K70 1.464401 0.505022 0.489321 NaN 4.838706 0.689245 3.217973 2.217973 0.450862 2.235172
10 600675 2006/6/30 A K70 1.384693 0.553543 0.5368 NaN 6.631475 0.681799 3.142667 2.142667 0.466708 2.159064
In [334]:
# 修改aresult和bresult的columns name。
for key in title:
    areport[key].columns = files_reader[key].loc[0]
    breport[key].columns = files_reader[key].loc[0]
    
# 对出现缺失值的列名，进行填充。
areport["violate"].columns = areport["violate"].columns.fillna("违规年度")
breport["violate"].columns = breport["violate"].columns.fillna("违规年度")

for key in title:
    print("aresult[%s]的columns name is\n"%key, areport[key].columns)
    print("bresult[%s]的columns name is\n"%key, areport[key].columns)

aresult[debt]的columns name is
 Index(['股票代码', '截止日期', '报表类型编码', '行业代码', '流动比率', '速动比率', '保守速动比率', '现金比率',
       '利息保障倍数B', '资产负债率', '权益乘数', '产权比率', '权益对负债比率', '有形净值债务率'],
      dtype='object', name=0)
bresult[debt]的columns name is
 Index(['股票代码', '截止日期', '报表类型编码', '行业代码', '流动比率', '速动比率', '保守速动比率', '现金比率',
       '利息保障倍数B', '资产负债率', '权益乘数', '产权比率', '权益对负债比率', '有形净值债务率'],
      dtype='object', name=0)
aresult[dev]的columns name is
 Index(['股票代码', '会计年度', '报表类型编码', '行业代码', '固定资产增长率B', '总资产增长率B', '净资产收益率增长率B',
       '净利润增长率B', '利润总额增长率B', '营业利润增长率B', '营业收入增长率B', '所有者权益增长率B'],
      dtype='object', name=0)
bresult[dev]的columns name is
 Index(['股票代码', '会计年度', '报表类型编码', '行业代码', '固定资产增长率B', '总资产增长率B', '净资产收益率增长率B',
       '净利润增长率B', '利润总额增长率B', '营业利润增长率B', '营业收入增长率B', '所有者权益增长率B'],
      dtype='object', name=0)
aresult[risk]的columns name is
 Index(['股票代码', '截止日期', '报表类型编码', '行业代码', '财务杠杆', '经营杠杆', '综合杠杆'], dtype='object', name=0)
bresult[risk]的columns name is
 Index(['股票代码', '截止日期', '报表类型编码', '行业代码', '财务杠杆', '经营杠杆', '综合杠杆'], dtype='object', name=0)
aresult[stock]的columns name is
 Index(['证券代码', '统计截止日期', '股本总数', '其中：国有股', '其中：一般法人配售', '已流通股份'], dtype='object', name=0)
bresult[stock]的columns name is
 Index(['证券代码', '统计截止日期', '股本总数', '其中：国有股', '其中：一般法人配售', '已流通股份'], dtype='object', name=0)
aresult[op]的columns name is
 Index(['股票代码', '截止日期', '报表类型编码', '行业代码', '应收账款与收入比', '应收账款周转率B', '存货与收入比',
       '存货周转率B', '应付账款周转率B', '营运资金（资本）周转率B', '现金及现金等价物周转率B', '流动资产与收入比',
       '流动资产周转率B', '固定资产与收入比', '固定资产周转率B', '非流动资产周转率B', '总资产周转率B', '股东权益周转率B'],
      dtype='object', name=0)
bresult[op]的columns name is
 Index(['股票代码', '截止日期', '报表类型编码', '行业代码', '应收账款与收入比', '应收账款周转率B', '存货与收入比',
       '存货周转率B', '应付账款周转率B', '营运资金（资本）周转率B', '现金及现金等价物周转率B', '流动资产与收入比',
       '流动资产周转率B', '固定资产与收入比', '固定资产周转率B', '非流动资产周转率B', '总资产周转率B', '股东权益周转率B'],
      dtype='object', name=0)
aresult[manage]的columns name is
 Index(['股票代码', '统计截止日期', '所属省份', '行业代码', '行业名称', '是否披露内控评价报告', '是否出具内控评价报告结论',
       '内部控制是否有效', '内部控制是否存在缺陷', '是否采取整改措施', '评价结论内容'],
      dtype='object', name=0)
bresult[manage]的columns name is
 Index(['股票代码', '统计截止日期', '所属省份', '行业代码', '行业名称', '是否披露内控评价报告', '是否出具内控评价报告结论',
       '内部控制是否有效', '内部控制是否存在缺陷', '是否采取整改措施', '评价结论内容'],
      dtype='object', name=0)
aresult[audit]的columns name is
 Index(['证券代码', '证券简称', '会计截止日期', '审计日期', '审计意见类型', '审计师', '境内审计事务所', '境外审计事务所',
       '境内审计费用金额', '境内审计费用货币单位', '境外审计费用金额', '境外审计费用货币单位', '其他相关费用金额',
       '其他相关费用货币单位', '审计费用合计', '合计审计费用货币单位', '审计报告', '审计费用说明'],
      dtype='object', name=0)
bresult[audit]的columns name is
 Index(['证券代码', '证券简称', '会计截止日期', '审计日期', '审计意见类型', '审计师', '境内审计事务所', '境外审计事务所',
       '境内审计费用金额', '境内审计费用货币单位', '境外审计费用金额', '境外审计费用货币单位', '其他相关费用金额',
       '其他相关费用货币单位', '审计费用合计', '合计审计费用货币单位', '审计报告', '审计费用说明'],
      dtype='object', name=0)
aresult[violate]的columns name is
 Index(['违规事件ID', '证券代码', '处理文件日期', '公告日期', '公告发布机构', '公告文件名称', '处理文件编号',
       '处理单位', '违规类型', '违规类型编码', '违反的法律法规', '违规年度', '违规年度', '违规年度', '违规年度',
       '违规年度', '违规年度', '违规年度', '违规年度', '违规年度', '违规年度', '违规年度', '违规年度', '违规年度',
       '违规年度', '违规年度', '违规年度', '违规行为', '处分措施', '处罚总金额', '上市公司是否违规',
       '处罚方式-上市公司', '处罚方式编码-上市公司', '处罚金额-上市公司'],
      dtype='object', name=0)
bresult[violate]的columns name is
 Index(['违规事件ID', '证券代码', '处理文件日期', '公告日期', '公告发布机构', '公告文件名称', '处理文件编号',
       '处理单位', '违规类型', '违规类型编码', '违反的法律法规', '违规年度', '违规年度', '违规年度', '违规年度',
       '违规年度', '违规年度', '违规年度', '违规年度', '违规年度', '违规年度', '违规年度', '违规年度', '违规年度',
       '违规年度', '违规年度', '违规年度', '违规行为', '处分措施', '处罚总金额', '上市公司是否违规',
       '处罚方式-上市公司', '处罚方式编码-上市公司', '处罚金额-上市公司'],
      dtype='object', name=0)
aresult[cash]的columns name is
 Index(['股票代码', '截止日期', '报表类型编码', '行业代码', '营业收入现金净含量', '营业利润现金净含量', '营运指数'], dtype='object', name=0)
bresult[cash]的columns name is
 Index(['股票代码', '截止日期', '报表类型编码', '行业代码', '营业收入现金净含量', '营业利润现金净含量', '营运指数'], dtype='object', name=0)
aresult[nps]的columns name is
 Index(['每股现金净流量TTM2', '每股经营活动产生的现金流量净额TTM2'], dtype='object', name=0)
bresult[nps]的columns name is
 Index(['每股现金净流量TTM2', '每股经营活动产生的现金流量净额TTM2'], dtype='object', name=0)
aresult[net]的columns name is
 Index(['股票代码', '截止日期', '报表类型编码', '行业代码', '资产报酬率B', '总资产净利润率（ROA）B', '净资产收益率B',
       '营业利润率'],
      dtype='object', name=0)
bresult[net]的columns name is
 Index(['股票代码', '截止日期', '报表类型编码', '行业代码', '资产报酬率B', '总资产净利润率（ROA）B', '净资产收益率B',
       '营业利润率'],
      dtype='object', name=0)
aresult[govern]的columns name is
 Index(['证券代码', '统计截止日期', '董事会会议次数', '监事会会议次数', '备注', '股东大会召开次数'], dtype='object', name=0)
bresult[govern]的columns name is
 Index(['证券代码', '统计截止日期', '董事会会议次数', '监事会会议次数', '备注', '股东大会召开次数'], dtype='object', name=0)
aresult[governother]的columns name is
 Index(['证券代码', '统计截止日期', '股本结构是否变化', '董事长与总经理兼任情况', '董事人数', '其中：独立董事人数',
       '监事总规模'],
      dtype='object', name=0)
bresult[governother]的columns name is
 Index(['证券代码', '统计截止日期', '股本结构是否变化', '董事长与总经理兼任情况', '董事人数', '其中：独立董事人数',
       '监事总规模'],
      dtype='object', name=0)
In [335]:
# 以运营科目的财报内容为例展示源数据内容
areport["op"].head()
Out[335]:
股票代码 截止日期 报表类型编码 行业代码 应收账款与收入比 应收账款周转率B 存货与收入比 存货周转率B 应付账款周转率B 营运资金（资本）周转率B 现金及现金等价物周转率B 流动资产与收入比 流动资产周转率B 固定资产与收入比 固定资产周转率B 非流动资产周转率B 总资产周转率B 股东权益周转率B
2 600717 2007/6/30 A G59 0.114394 10.043208 0.01013 59.662912 14.993807 -4.007609 1.16887 1.161457 0.826862 3.327115 0.297336 0.209558 0.167186 0.248113
5 600717 2007/9/30 A G59 0.069617 16.312664 0.019036 45.55388 21.829519 16.700731 1.123689 1.509898 0.871064 2.095157 0.47387 0.317013 0.232425 0.367831
6 600717 2007/12/31 A G59 0.044992 24.225777 0.011198 76.852855 35.382099 46.271628 2.4801 1.082133 1.219918 1.487035 0.668048 0.420121 0.312501 0.484882
9 600717 2008/3/31 A G59 0.22405 5.175955 0.040047 14.891194 2.068903 1.166469 0.754384 3.64115 0.265042 8.200662 0.147421 0.092819 0.068744 0.111194
10 600717 2008/6/30 A G59 0.312123 3.624538 0.063661 13.932489 2.477918 -86.44711 3.074194 0.815366 1.118961 1.536829 0.766725 0.438084 0.314826 0.544186
In [336]:
# 准备好了源数据。下面开始对源数据清洗。先检查并删除重复行。

# 观察所有样本集，发现审计样本集的字段名称与其它样本集不同。所以先将审计样本集的“证券简称”列删除。方便后续步骤。
try:
    areport['audit'].drop(labels=["证券简称"], axis=1, inplace=True)
except KeyError:
    pass

areport_drop_duplicates = {key : areport[key].drop_duplicates([areport[key].columns[0], areport[key].columns[1]]) 
                            for key in areport}
breport_drop_duplicates = {key : breport[key].drop_duplicates([breport[key].columns[0], areport[key].columns[1]]) 
                            for key in breport}

# 展示偿债能力相关的财报内容
areport_drop_duplicates["audit"].shape
Out[336]:
(38906, 17)
In [337]:
# 后面只使用合并报表，即报表类型A的数据。考虑到尽量使用全部财务指标，故删除areport的所有空行记录。
areport_dropna = {key : areport_drop_duplicates[key].dropna() for key in title}
# 查看偿债能力相关内容的财报的数据类型。
areport_drop_duplicates["debt"].dtypes
Out[337]:
0
股票代码       object
截止日期       object
报表类型编码     object
行业代码       object
流动比率       object
速动比率       object
保守速动比率     object
现金比率       object
利息保障倍数B    object
资产负债率      object
权益乘数       object
产权比率       object
权益对负债比率    object
有形净值债务率    object
dtype: object
In [338]:
# 偿债能力相关财报的字段名：股票代码，截止日期，报表类型编码，行业代码，流动比率，速动比率，保守速动比率，现金比率，利息保障倍数B，
# 资产负债率，权益乘数，产权比率，权益对负债比率，有形净值债务率
# 删除重复样本的数据集名：areport_drop_duplicates
# title = ["debt", "dev", "risk", "stock", "op", "manage", "audit", "violate", "cash", "nps", "net", "govern", "governother"]
areport_drop_duplicates["debt"]["截止日期"] = pd.to_datetime(areport_drop_duplicates["debt"]["截止日期"])

areport_astype_debt = areport_drop_duplicates["debt"].astype({"股票代码":"int64", "报表类型编码":"category", 
                                                 "行业代码":"category", "流动比率":"float64", "速动比率":"float64", 
                                                 "保守速动比率":"float64", "现金比率":"float64", "利息保障倍数B":"float64", 
                                                 "资产负债率":"float64", "权益乘数":"float64", "产权比率":"float64", 
                                                 "权益对负债比率":"float64", "有形净值债务率":"float64"})

# 继续对其它财务主题的源数据报表做预处理。预处理包括：统一字段名称，修改字段数据类型。
# 先将与其它表不一致的固定列名修改为一致。即将会计年度改为截止日期。
# 发展能力相关财报源数据的字段名：股票代码, 截止日期, 报表类型编码, 行业代码, 固定资产增长率B, 总资产增长率B, 净资产收益率增长率B, 
# 净利润增长率B, 
# 利润总额增长率B, 营业利润增长率B, 营业收入增长率B, 所有者权益增长率B
areport_drop_duplicates["dev"].rename(columns = {"会计年度":"截止日期"}, inplace=True)

# 将截止日期列的数据类型改为detetime64[ns]
areport_drop_duplicates["dev"]["截止日期"] = pd.to_datetime(areport_drop_duplicates["dev"]["截止日期"])

areport_astype_dev = areport_drop_duplicates["dev"].astype({"股票代码":"int64", "报表类型编码":"category", "行业代码":"category", 
                                                           "固定资产增长率B":"float64", "总资产增长率B":"float64", 
                                                            "净资产收益率增长率B":"float64","净利润增长率B":"float64", 
                                                            "利润总额增长率B":"float64", "营业利润增长率B":"float64",
                                                           "营业收入增长率B":"float64", "所有者权益增长率B":"float64",})

# 风险相关的财报字段名：股票代码, 截止日期, 报表类型编码, 行业代码, 财务杠杆, 经营杠杆, 综合杠杆。
areport_drop_duplicates["risk"]["截止日期"] = pd.to_datetime(areport_drop_duplicates["risk"]["截止日期"])

areport_astype_risk = areport_drop_duplicates["risk"].astype({"股票代码":"int64", "报表类型编码":"category", "行业代码":"category", 
                                                              "财务杠杆":"float64", "经营杠杆":"float64", "综合杠杆":"float64"})

# 股权主题相关的财报字段：证券代码, 统计截止日期, 股本总数, 其中：国有股, 其中：一般法人配售, 已流通股份
# 修改列名
areport_drop_duplicates["stock"].rename(columns={"证券代码":"股票代码", "统计截止日期":"截止日期", "其中：国有股":"国有股", 
                                        "其中：一般法人配售":"一般法人配售"}, inplace=True)
# 删除错误的第1行
try:
    areport_drop_duplicates["stock"].drop([0,1], inplace=True)
except KeyError:
    pass

# 修改数据类型
areport_drop_duplicates["stock"]["截止日期"] = pd.to_datetime(areport_drop_duplicates["stock"]["截止日期"])
areport_astype_stock = areport_drop_duplicates["stock"].astype({"股票代码":"int64", "股本总数":"int64", "国有股":"int64",
                                                               "一般法人配售":"int64", "已流通股份":"int64"})

# 运营主题相关的财报字段：股票代码, 截止日期, 报表类型编码, 行业代码, 应收账款与收入比, 应收账款周转率B, 存货与收入比, 存货周转率B, 
# 应付账款周转率B, 营运资金（资本）周转率B, 现金及现金等价物周转率B, 流动资产与收入比, 流动资产周转率B, 固定资产与收入比, 
# 固定资产周转率B, 非流动资产周转率B, 总资产周转率B, 股东权益周转率B
# 修改字段数据类型
areport_drop_duplicates["op"]["截止日期"] = pd.to_datetime(areport_drop_duplicates["op"]["截止日期"])
areport_astype_op = areport_drop_duplicates["op"].astype({"股票代码":"int64", "报表类型编码":"category", "行业代码":"category", 
                                                         "应收账款与收入比":"float64", "应收账款周转率B":"float64", 
                                                          "存货与收入比":"float64", "存货周转率B":"float64", "应付账款周转率B":"float64",
                                                          "营运资金（资本）周转率B":"float64", "现金及现金等价物周转率B":"float64", 
                                                          "流动资产与收入比":"float64", "流动资产周转率B":"float64", 
                                                         "固定资产与收入比":"float64", "固定资产周转率B":"float64", 
                                                          "非流动资产周转率B":"float64", "总资产周转率B":"float64", 
                                                          "股东权益周转率B":"float64"})

# 管控主题相关的财报字段：股票代码, 截止日期, 所属省份, 行业代码, 行业名称, 是否披露内控评价报告, 是否出具内控评价报告结论, 
# 内部控制是否有效, 内部控制是否存在缺陷, 是否采取整改措施, 评价结论内容
try:
    areport_drop_duplicates["manage"].drop([0,1], inplace=True)
except KeyError:
    pass
# 修改固定列名与其它数据集一致。
areport_drop_duplicates["manage"].rename(columns={"统计截止日期":"截止日期"}, inplace=True)
areport_drop_duplicates["manage"]["截止日期"] = pd.to_datetime(areport_drop_duplicates["manage"]["截止日期"])
areport_astype_manage = areport_drop_duplicates["manage"].astype({"股票代码":"int64", "行业代码":"category", 
                                                                 "是否披露内控评价报告":"category", 
                                                                  "是否出具内控评价报告结论":"category", "内部控制是否有效":"category", 
                                                                 "内部控制是否存在缺陷":"category", "是否采取整改措施":"category", 
                                                                  })

# 对管控财报的字段观察后发现，"是否披露内控评价报告", "是否出具内控评价报告结论", "内部控制是否有效", "内部控制是否存在缺陷", 
# "是否采取整改措施" 这几个字段对分析没有用处，故删除这些字段。
try:
    areport_astype_manage.drop(labels=["是否披露内控评价报告", "是否出具内控评价报告结论", "内部控制是否有效", "内部控制是否存在缺陷", 
                                       "是否采取整改措施", "评价结论内容", "行业代码"], axis=1, inplace=True)
except KeyError:
    pass


# 审计相关的财报字段：股票代码, 证券简称, 截止日期, 审计日期, 审计意见类型, 审计师, 境内审计事务所, 境外审计事务所, 境内审计费用金额, 
# 境内审计费用货币单位, 境外审计费用金额, 境外审计费用货币单位, 其他相关费用金额, 其他相关费用货币单位, 审计费用合计, 
# 合计审计费用货币单位, 审计报告, 审计费用说明
# 修改列名。
areport_drop_duplicates["audit"].rename(columns={"证券代码":"股票代码", "会计截止日期":"截止日期"}, inplace=True)
# 删除第0，1行。
try:
    areport_drop_duplicates["audit"].drop([0,1], inplace=True)
except KeyError:
    pass

# 修改数据类型
areport_drop_duplicates["audit"]["截止日期"] = pd.to_datetime(areport_drop_duplicates["audit"]["截止日期"])
areport_drop_duplicates["audit"]["审计日期"] = pd.to_datetime(areport_drop_duplicates["audit"]["审计日期"], errors = "coerce")
areport_astype_audit = areport_drop_duplicates["audit"].astype({"股票代码":"int64", "境内审计费用金额":"float64", 
                                        "境内审计费用货币单位":"category", "境外审计费用金额":"float64", 
                                        "境外审计费用货币单位":"category", "其他相关费用金额":"float64", 
                                        "其他相关费用货币单位":"category", "审计费用合计":"float64", "合计审计费用货币单位":"category", })

# 对审计源数据观察后发现，我们只需要使用“股票代码”，“截止日期”和“审计意见类型”三个字段，其它字段无用，故删除。
try:
    areport_astype_audit.drop(labels=["审计日期", "审计师", "境内审计事务所", "境外审计事务所", "境内审计费用金额", 
    "境内审计费用货币单位", "境外审计费用金额", "境外审计费用货币单位", "其他相关费用金额", "其他相关费用货币单位", "审计费用合计", 
    "合计审计费用货币单位", "审计报告", "审计费用说明"], axis=1, inplace=True)
except KeyError:
    pass

# 违规相关的财报字段：违规事件ID, 股票代码, 处理文件日期, 公告日期, 公告发布机构, 公告文件名称, 处理文件编号, 处理单位, 违规类型, 
# 违规类型编码, 违反的法律法规, 违规年度, 违规行为, 处分措施, 处罚总金额, 上市公司是否违规, 处罚方式-上市公司, 处罚方式编码-上市公司, 
# 处罚金额-上市公司
# 修改数据集的属性名称
areport_drop_duplicates["violate"].rename(columns={"证券代码":"股票代码"}, inplace=True)
# 删除数据集中的第0，1行。
try:
    areport_drop_duplicates["violate"].drop([0,1], inplace=True)
except KeyError:
    pass

# 这个数据集的清洗比较复杂，因为各属性有大量缺失值，需要一一填充，以方便数据类型的转换。注意，NaN的类型为float，所以如果要将NaN转换为int，
# 需要先把该列fillna(int)，再astype。
areport_drop_duplicates["violate"]["违规年度"] = areport_drop_duplicates["violate"]["违规年度"].fillna(0)

areport_drop_duplicates["violate"]["处罚方式-上市公司"] = areport_drop_duplicates["violate"]["处罚方式-上市公司"].fillna("无")

areport_drop_duplicates["violate"]["处理文件日期"] = pd.to_datetime(areport_drop_duplicates["violate"]["处理文件日期"])
areport_drop_duplicates["violate"]["公告日期"] = pd.to_datetime(areport_drop_duplicates["violate"]["公告日期"], errors="coerce")

areport_drop_duplicates["violate"]

areport_astype_violate = areport_drop_duplicates["violate"].astype({"违规事件ID":"int64", "股票代码":"int64", "公告发布机构":"category", 
                                                                   "处理单位":"category", "违规类型":"category", "违规类型编码":"category"
                                                                    , "违规年度":"int32", "处罚总金额":"float64", 
                                                                   "上市公司是否违规":"category", "处罚方式-上市公司":"category", 
                                                                   "处罚金额-上市公司":"float64" })

# 现金流相关的财报字段：股票代码, 截止日期, 报表类型编码, 行业代码, 营业收入现金净含量, 营业利润现金净含量, 营运指数
# 修改数据类型
areport_drop_duplicates["cash"]["截止日期"] = pd.to_datetime(areport_drop_duplicates["cash"]["截止日期"], format="%Y-%m-%d")
areport_astype_cash = areport_drop_duplicates["cash"].astype({"股票代码":"int64", "报表类型编码":"category", "行业代码":"category",  
                                                             "营业收入现金净含量":"float64", "营业利润现金净含量":"float64", 
                                                              "营运指数":"float64"})

# 净利润相关的财报字段：股票代码, 截止日期, 报表类型编码, 行业代码, 资产报酬率B, 总资产净利润率（ROA）B, 净资产收益率B, 营业利润率
# 修改数据类型
areport_drop_duplicates["net"]["截止日期"] = pd.to_datetime(areport_drop_duplicates["net"]["截止日期"])
areport_astype_net = areport_drop_duplicates["net"].astype({"股票代码":"int64", "报表类型编码":"category", "行业代码":"category", 
                                                           "资产报酬率B":"float64", "总资产净利润率（ROA）B":"float64", 
                                                           "净资产收益率B":"float64", "营业利润率":"float64"})

# 治理结构相关字段：股票代码, 截止日期, 董事会会议次数, 监事会会议次数, 备注, 股东大会召开次数
# 修改数据类型
areport_drop_duplicates["govern"].rename(columns={"证券代码":"股票代码", "统计截止日期":"截止日期"}, inplace=True)

try:
    areport_drop_duplicates["govern"] = areport_drop_duplicates["govern"].drop([0,1])    # 删除第0，1行
    areport_drop_duplicates["govern"].drop(["备注"], axis=1, inplace=True)    # 删除备注列。
except KeyError:
    pass

areport_drop_duplicates["govern"]["截止日期"] = pd.to_datetime(areport_drop_duplicates["govern"]["截止日期"])

# 对缺失次数填充0
areport_drop_duplicates["govern"]["董事会会议次数"].fillna(0, inplace=True)
areport_drop_duplicates["govern"]["监事会会议次数"].fillna(0, inplace=True)
areport_drop_duplicates["govern"]["股东大会召开次数"].fillna(0, inplace=True)
areport_astype_govern = areport_drop_duplicates["govern"].astype({"股票代码":"int64", "董事会会议次数":"int32", 
                                                                  "监事会会议次数":"int32", "股东大会召开次数":"int32" })

# 治理结构（其余）相关的字段：股票代码, 截止日期, 股本结构是否变化, 董事长与总经理兼任情况, 董事人数, 其中：独立董事人数, 监事总规模
# 修改数据集的列名
areport_drop_duplicates["governother"].rename(columns={"证券代码":"股票代码", "统计截止日期":"截止日期"}, inplace=True)

# 删除第0，1行
try:
    areport_drop_duplicates["governother"].drop([0,1], inplace=True)
except KeyError:
    pass

# 填充缺失值为0
areport_drop_duplicates["governother"].fillna(0, inplace=True)
# 修改数据集的数据类型
areport_drop_duplicates["governother"]["截止日期"] = pd.to_datetime(areport_drop_duplicates["governother"]["截止日期"])
areport_astype_governother = areport_drop_duplicates["governother"].astype({"股票代码":"int64", "股本结构是否变化":"category", 
                                                                           "董事长与总经理兼任情况":"category", "董事人数":"int32", 
                                                                           "其中：独立董事人数":"int32", "监事总规模":"int32" })

# 观察字段，发现'股本结构是否变化', '董事长与总经理兼任情况'两个字段可以删除。
try:
    areport_astype_governother.drop(labels=['股本结构是否变化', '董事长与总经理兼任情况'], axis=1, inplace=True)
except KeyError:
    pass
In [339]:
areport_astype_audit
Out[339]:
股票代码 截止日期 审计意见类型
2 1 2000-12-31 标准无保留意见
3 1 2001-06-30 标准无保留意见
4 1 2001-12-31 标准无保留意见
5 1 2002-06-30 标准无保留意见
6 1 2002-12-31 标准无保留意见
7 1 2003-06-30 标准无保留意见
8 1 2003-12-31 标准无保留意见
9 1 2004-06-30 标准无保留意见
10 1 2004-12-31 无保留意见加事项段
11 1 1992-12-31 标准无保留意见
12 1 1993-12-31 标准无保留意见
13 1 1994-12-31 标准无保留意见
14 1 1995-12-31 标准无保留意见
15 1 1996-06-30 标准无保留意见
16 1 1996-12-31 标准无保留意见
17 1 1997-06-30 标准无保留意见
18 1 1997-12-31 标准无保留意见
19 1 1998-06-30 标准无保留意见
20 1 1998-12-31 标准无保留意见
21 1 1999-12-31 标准无保留意见
22 1 2011-12-31 标准无保留意见
23 1 2012-06-30 标准无保留意见
24 1 2000-06-30 标准无保留意见
25 1 2005-06-30 标准无保留意见
26 1 2005-12-31 标准无保留意见
27 1 2006-06-30 标准无保留意见
28 1 2006-12-31 标准无保留意见
29 1 2007-06-30 标准无保留意见
30 1 2007-12-31 标准无保留意见
31 1 2008-06-30 标准无保留意见
... ... ... ...
38937 900956 2005-12-31 标准无保留意见
38938 900956 2006-12-31 标准无保留意见
38939 900956 2007-12-31 标准无保留意见
38940 900956 2008-12-31 标准无保留意见
38941 900956 2009-12-31 标准无保留意见
38942 900956 2010-12-31 标准无保留意见
38943 900956 2000-12-31 标准无保留意见
38944 900956 2011-12-31 标准无保留意见
38945 900956 2012-12-31 标准无保留意见
38946 900956 2013-12-31 标准无保留意见
38947 900956 2014-12-31 标准无保留意见
38948 900956 2015-12-31 标准无保留意见
38949 900956 2016-12-31 标准无保留意见
38950 900957 2000-12-31 标准无保留意见
38951 900957 2001-12-31 标准无保留意见
38952 900957 2011-12-31 标准无保留意见
38953 900957 2002-12-31 保留意见
38954 900957 2003-12-31 标准无保留意见
38955 900957 2004-12-31 标准无保留意见
38956 900957 2005-12-31 无保留意见加事项段
38957 900957 2006-12-31 标准无保留意见
38958 900957 2007-12-31 标准无保留意见
38959 900957 2008-12-31 标准无保留意见
38960 900957 2009-12-31 标准无保留意见
38961 900957 2010-12-31 标准无保留意见
38962 900957 2012-12-31 标准无保留意见
38963 900957 2013-12-31 标准无保留意见
38964 900957 2014-12-31 标准无保留意见
38965 900957 2015-12-31 标准无保留意见
38966 900957 2016-12-31 标准无保留意见

38904 rows × 3 columns

In [340]:
areport_astype_manage.head()
Out[340]:
股票代码 截止日期 所属省份 行业名称
2 1 2007-12-31 广东省 货币金融服务
3 1 2008-12-31 广东省 货币金融服务
4 1 2009-12-31 广东省 货币金融服务
5 1 2010-12-31 广东省 货币金融服务
6 1 2011-12-31 广东省 货币金融服务
In [341]:
# 只删除所有属性为NaN缺失值的样本。
# title = ["debt", "dev", "risk", "stock", "op", "manage", "audit", "violate", "cash", "nps", "net", "govern", "governother"]
areport_astype_debt.dropna(how="all", subset=["流动比率", "速动比率", "保守速动比率", "现金比率", "利息保障倍数B", "资产负债率", 
                                              "权益乘数", "产权比率", "权益对负债比率", "有形净值债务率"], inplace=True)
areport_astype_dev.dropna(how="all", subset=["固定资产增长率B","总资产增长率B","净资产收益率增长率B","净利润增长率B","利润总额增长率B",
                                             "营业利润增长率B","营业收入增长率B","所有者权益增长率B"], inplace=True)
areport_astype_risk.dropna(how="all", subset=["财务杠杆","经营杠杆","综合杠杆"], inplace=True)
areport_astype_stock.dropna(how="all", subset=["股本总数","国有股","一般法人配售","已流通股份"], inplace=True)
areport_astype_op.dropna(how="all", subset=["应收账款与收入比","应收账款周转率B","存货与收入比","存货周转率B","应付账款周转率B",
                                            "营运资金（资本）周转率B","现金及现金等价物周转率B","流动资产与收入比","流动资产周转率B",
                                            "固定资产与收入比","固定资产周转率B","非流动资产周转率B","总资产周转率B",
                                            "股东权益周转率B"], inplace=True)
areport_astype_manage.dropna(how="all", inplace=True)

areport_astype_audit.dropna(how="all", subset=["审计意见类型"], inplace=True)
areport_astype_violate.dropna(how="all", subset=["违规事件ID"], inplace=True)
areport_astype_cash.dropna(how="all", subset=["营业收入现金净含量","营业利润现金净含量","营运指数"], inplace=True)
areport_astype_net.dropna(how="all", subset=["资产报酬率B","总资产净利润率（ROA）B","净资产收益率B","营业利润率"], inplace=True)
areport_astype_govern.dropna(how="all", subset=["董事会会议次数","监事会会议次数","股东大会召开次数"], inplace=True)
areport_astype_governother.dropna(how="all", subset=["董事人数","其中：独立董事人数","监事总规模"], inplace=True)
In [342]:
# 下面开始连接数据集，主键是股票代码，截止日期。
# 通过对数据进行观察，考虑到：我们想保留全部的财务指标。故选择inner连接的结果。所以我们采用并且再加一步操作：删除inner连接后，
# 存在空值的样本。
merge_debt_dev = pd.merge(areport_astype_debt, areport_astype_dev, on=["股票代码", "截止日期"])
merge_debt_dev_risk = pd.merge(merge_debt_dev, areport_astype_risk, on = ["股票代码", "截止日期"])
merge_debt_dev_risk_stock = pd.merge(merge_debt_dev_risk, areport_astype_stock, on = ["股票代码", "截止日期"])
merge_debt_dev_risk_stock_op = pd.merge(merge_debt_dev_risk_stock, areport_astype_op, on = ["股票代码", "截止日期"])
merge_debt_dev_risk_stock_op_magange= pd.merge(merge_debt_dev_risk_stock_op, areport_astype_manage, on = ["股票代码", "截止日期"])
merge_debt_dev_risk_stock_op_magange_cash = pd.merge(merge_debt_dev_risk_stock_op_magange, 
                                                  areport_astype_cash, on = ["股票代码", "截止日期"])
merge_debt_dev_risk_stock_op_magange_cash_net = pd.merge(merge_debt_dev_risk_stock_op_magange_cash, 
                                                  areport_astype_net, on = ["股票代码", "截止日期"])
merge_debt_dev_risk_stock_op_magange_cash_net_govern = pd.merge(merge_debt_dev_risk_stock_op_magange_cash_net, 
                                                  areport_astype_govern, on = ["股票代码", "截止日期"])
merge_debt_dev_risk_stock_op_magange_cash_net_govern_governother = pd.merge(merge_debt_dev_risk_stock_op_magange_cash_net_govern, 
                                                  areport_astype_governother, on = ["股票代码", "截止日期"])
In [343]:
# 最终获得一个inner连接的数据集。
print("inner样本集全部记录有",merge_debt_dev_risk_stock_op_magange_cash_net_govern_governother["股票代码"].count(),"条")
print("实际有",merge_debt_dev_risk_stock_op_magange_cash_net_govern_governother["股票代码"].drop_duplicates().count(), "家公司。")
print("最初debt样本集中有", areport_astype_debt["股票代码"].drop_duplicates().count(), "家公司。")
print("得到的样本一共有",len(merge_debt_dev_risk_stock_op_magange_cash_net_govern_governother.columns),"个字段。")
print("样本字段有：\n",merge_debt_dev_risk_stock_op_magange_cash_net_govern_governother.columns)

inner样本集全部记录有 3111 条
实际有 494 家公司。
最初debt样本集中有 722 家公司。
得到的样本一共有 68 个字段。
样本字段有：
 Index(['股票代码', '截止日期', '报表类型编码_x', '行业代码_x', '流动比率', '速动比率', '保守速动比率', '现金比率',
       '利息保障倍数B', '资产负债率', '权益乘数', '产权比率', '权益对负债比率', '有形净值债务率', '报表类型编码_y',
       '行业代码_y', '固定资产增长率B', '总资产增长率B', '净资产收益率增长率B', '净利润增长率B', '利润总额增长率B',
       '营业利润增长率B', '营业收入增长率B', '所有者权益增长率B', '报表类型编码_x', '行业代码_x', '财务杠杆',
       '经营杠杆', '综合杠杆', '股本总数', '国有股', '一般法人配售', '已流通股份', '报表类型编码_y', '行业代码_y',
       '应收账款与收入比', '应收账款周转率B', '存货与收入比', '存货周转率B', '应付账款周转率B', '营运资金（资本）周转率B',
       '现金及现金等价物周转率B', '流动资产与收入比', '流动资产周转率B', '固定资产与收入比', '固定资产周转率B',
       '非流动资产周转率B', '总资产周转率B', '股东权益周转率B', '所属省份', '行业名称', '报表类型编码_x',
       '行业代码_x', '营业收入现金净含量', '营业利润现金净含量', '营运指数', '报表类型编码_y', '行业代码_y',
       '资产报酬率B', '总资产净利润率（ROA）B', '净资产收益率B', '营业利润率', '董事会会议次数', '监事会会议次数',
       '股东大会召开次数', '董事人数', '其中：独立董事人数', '监事总规模'],
      dtype='object', name=0)
In [344]:
# 删除重复列
try:
    merge_debt_dev_risk_stock_op_magange_cash_net_govern_governother.drop(["报表类型编码_x", "行业代码_x", "报表类型编码_y",
                                                                           "行业代码_y"], axis=1, inplace=True)
except ValueError:
    pass

print("一共有",len(merge_debt_dev_risk_stock_op_magange_cash_net_govern_governother.columns),"个字段。")
print("删除重复列后，样本字段有：\n",merge_debt_dev_risk_stock_op_magange_cash_net_govern_governother.columns)

一共有 56 个字段。
删除重复列后，样本字段有：
 Index(['股票代码', '截止日期', '流动比率', '速动比率', '保守速动比率', '现金比率', '利息保障倍数B', '资产负债率',
       '权益乘数', '产权比率', '权益对负债比率', '有形净值债务率', '固定资产增长率B', '总资产增长率B',
       '净资产收益率增长率B', '净利润增长率B', '利润总额增长率B', '营业利润增长率B', '营业收入增长率B',
       '所有者权益增长率B', '财务杠杆', '经营杠杆', '综合杠杆', '股本总数', '国有股', '一般法人配售', '已流通股份',
       '应收账款与收入比', '应收账款周转率B', '存货与收入比', '存货周转率B', '应付账款周转率B', '营运资金（资本）周转率B',
       '现金及现金等价物周转率B', '流动资产与收入比', '流动资产周转率B', '固定资产与收入比', '固定资产周转率B',
       '非流动资产周转率B', '总资产周转率B', '股东权益周转率B', '所属省份', '行业名称', '营业收入现金净含量',
       '营业利润现金净含量', '营运指数', '资产报酬率B', '总资产净利润率（ROA）B', '净资产收益率B', '营业利润率',
       '董事会会议次数', '监事会会议次数', '股东大会召开次数', '董事人数', '其中：独立董事人数', '监事总规模'],
      dtype='object', name=0)
In [345]:
# 舞弊财报的选择标准:
# 1、审计结果意见为：拒绝表示意见，否定意见，无法表示意见的作为舞弊样本。
# 2、违规记录中有记录的，其中违规类型是：虚构利润，或虚列资产，且被罚款的，作为舞弊样本。被罚款意味着情节相对严重，以此作为标注舞弊样本
#    的依据。其它做删除处理。因为相关公司有舞弊动机的嫌疑但又没有足够证据。
# 3、时间不早于1999年。
# 4、其它的审计意见，如：有保留意见，或带有解释说明段的，定为无法确定是否舞弊的财报，即既不标注为舞弊，但也不标注为非舞弊。
# 5、对于审计意见为：标准无保留意见。另外参考财务数据齐全，利润*现金流>0的，定义为非舞弊样本。由于现在现金流为正，利润为负的经营模式
#    并非不存在，故如果这种样本数量并不占多数，则不必做删除处理，依然认为是非舞弊样本，但利润为正数，现金流为负数的项目除外需要删除。
# 6、将净资产收益率作为一个敏感指标，作为筛选财报是否舞弊的参考之一。将符合样本均值的99%置信区间范围内的样本作为非舞弊样本。
In [346]:
# 首先对净资产收益率B的统计描述
print("查看净资产收益率的统计描述：\n", merge_debt_dev_risk_stock_op_magange_cash_net_govern_governother["净资产收益率B"].describe())

# 我们用置信区间来考察净资产收益率的99%置信区间
count = merge_debt_dev_risk_stock_op_magange_cash_net_govern_governother["净资产收益率B"].count()
ave = merge_debt_dev_risk_stock_op_magange_cash_net_govern_governother["净资产收益率B"].mean()
var = merge_debt_dev_risk_stock_op_magange_cash_net_govern_governother["净资产收益率B"].var()
print("\n净资产收益率样本数；", count)
print("净资产收益率均值；", ave)
print("净资产收益率方差；", var)
min = merge_debt_dev_risk_stock_op_magange_cash_net_govern_governother["净资产收益率B"].min()    # 净资产收益率最小值
max = merge_debt_dev_risk_stock_op_magange_cash_net_govern_governother["净资产收益率B"].max()    # 净资产收益率最大值

# 99%的置信区间，双边t_0.01/2=2.5758
import math
t_005 = 2.5758
lower_99 = ave - t_005 * math.sqrt(var/count)
upper_99 = ave + t_005 * math.sqrt(var/count)
print("\n净资产收益率的99%置信区间是：", lower_99, upper_99)
print("\n在99%置信区间中的净资产收益率样本数：",merge_debt_dev_risk_stock_op_magange_cash_net_govern_governother["净资产收益率B"][
    (merge_debt_dev_risk_stock_op_magange_cash_net_govern_governother["净资产收益率B"] >= lower_99) & 
(merge_debt_dev_risk_stock_op_magange_cash_net_govern_governother["净资产收益率B"] <= upper_99)].count())
print("净资产收益率在-5至+5的样本数是：", merge_debt_dev_risk_stock_op_magange_cash_net_govern_governother["净资产收益率B"][
    (merge_debt_dev_risk_stock_op_magange_cash_net_govern_governother["净资产收益率B"] >= -5) & 
    (merge_debt_dev_risk_stock_op_magange_cash_net_govern_governother["净资产收益率B"] <= 5)
].count())
print("净资产收益率在-1至+1的样本数是：", merge_debt_dev_risk_stock_op_magange_cash_net_govern_governother["净资产收益率B"][
    (merge_debt_dev_risk_stock_op_magange_cash_net_govern_governother["净资产收益率B"] >= -1) & 
    (merge_debt_dev_risk_stock_op_magange_cash_net_govern_governother["净资产收益率B"] <= 1)
].count())

print("\n分割线***************************************************************************************************************分割线\n")

# 绘制净资产收益率B直方图
import matplotlib as mpl
mpl.rcParams['font.sans-serif']=['SimHei']   # 添加黑体字体
mpl.rcParams['axes.unicode_minus']=False    # 显示负号
plt.figure(figsize=(16,9))
plt.xlabel("净资产收益率")
plt.ylabel("数量")
plt.title("净资产收益率直方图")
#bins_partial = np.arange(lower_99, upper_99, 0.001)    
#bins_left = np.arange(min, lower_99,0.04)
#bins_right = np.arange(upper_99, max+0.01, 0.04)
#bins = np.concatenate([bins_left, bins_partial, bins_right])    # 根据净资产收益率的9全部数据区间绘制直方图。
# bins = np.arange(-5,5.01,0.02)    # 根据上一步计算的净资产收益率在-5至+5区间内的样本数绘制直方图。
bins = np.arange(-1,1.01,0.02)    # 根据上一步计算的净资产收益率在-5至+5区间内的样本数绘制直方图。
plt.hist(merge_debt_dev_risk_stock_op_magange_cash_net_govern_governother["净资产收益率B"], 
         bins=bins)
plt.show()

查看净资产收益率的统计描述：
 count    3111.000000
mean        0.054651
std         0.595801
min       -22.903987
25%         0.026819
50%         0.073488
75%         0.128509
max         6.417488
Name: 净资产收益率B, dtype: float64

净资产收益率样本数； 3111
净资产收益率均值； 0.054651202507232405
净资产收益率方差； 0.35497917851576993

净资产收益率的99%置信区间是： 0.027136579079059205 0.08216582593540561

在99%置信区间中的净资产收益率样本数： 915
净资产收益率在-5至+5的样本数是： 3104
净资产收益率在-1至+1的样本数是： 3059

分割线***************************************************************************************************************分割线
In [347]:
# 通过直方图，清晰看到净资产收益率>0时的样本明显增多。
print("净资产收益率>=0的样本数是：", merge_debt_dev_risk_stock_op_magange_cash_net_govern_governother["净资产收益率B"][
    (merge_debt_dev_risk_stock_op_magange_cash_net_govern_governother["净资产收益率B"] >= 0) & 
    (merge_debt_dev_risk_stock_op_magange_cash_net_govern_governother["净资产收益率B"] <= 1)
].count())
print("净资产收益率<0的样本数是：", merge_debt_dev_risk_stock_op_magange_cash_net_govern_governother["净资产收益率B"][
    (merge_debt_dev_risk_stock_op_magange_cash_net_govern_governother["净资产收益率B"] >= -1) & 
    (merge_debt_dev_risk_stock_op_magange_cash_net_govern_governother["净资产收益率B"] <= 0)
].count())

净资产收益率>=0的样本数是： 2733
净资产收益率<0的样本数是： 326
In [348]:
print("最小年份", merge_debt_dev_risk_stock_op_magange_cash_net_govern_governother["截止日期"].min())
print("最大年份", merge_debt_dev_risk_stock_op_magange_cash_net_govern_governother["截止日期"].max())
print("所得财报样本的时间区间符合筛选要求。")

最小年份 2007-12-31 00:00:00
最大年份 2016-12-31 00:00:00
所得财报样本的时间区间符合筛选要求。
In [349]:
# 接下来根据之前总结的财务知识，从审计结果和违规结果中寻找舞弊样本，进行标注。
# 过滤的样本集年份应在2007/12/31至2016/12/31之间。
areport_astype_audit_in_period = areport_astype_audit[(areport_astype_audit["截止日期"]>="2007-12-31") & 
                                                      (areport_astype_audit["截止日期"]<="2016-12-31")].sort_values(
    by=["股票代码", "截止日期"])

# 查看2007-12-31至2016-12-31期间的所有审计的财报的审计结果
print("审计结果有如下几类：\n",areport_astype_audit_in_period["审计意见类型"].drop_duplicates())
print("所得审计样本集的shape is：",areport_astype_audit_in_period.shape)
# 根据这个审计结果分类，我们将标准无保留意见作为非舞弊财报样本，无法发表意见作为舞弊财报样本。其它样本因为难以确认，做删除处理。

审计结果有如下几类：
 30        标准无保留意见
111     无保留意见加事项段
112          保留意见
256      保留意见加事项段
408        无法发表意见
830    无保留意见加事项段 
Name: 审计意见类型, dtype: object
所得审计样本集的shape is： (23635, 3)
In [350]:
# 继续从审计样本集中找到舞弊样本和非舞弊样本的划分依据：
# 1、将areport_astype_audit_in_period中审计意见类型为无法发表意见，且也是merge_debt_dev_risk_stock_op_magange_cash_net_govern_governother
#    样本的，连接上一步做出的fraudulent_violate_in_period样本集，标注为舞弊样本。
# 2、将areport_astype_audit_in_period中审计意见类型为标准无保留意见，且也是merge_debt_dev_risk_stock_op_magange_cash_net_govern_
#    governother样本的，且不在areport_astype_violate_in_period样本集中，且财务数据齐全，利润*现金流>0，净资产收益率不远离99%置信区间，
#    特别是不<0的，标注为非舞弊样本。

# 从审计样本集中找出无法发表意见的财报样本。
fraudulent = areport_astype_audit_in_period[(areport_astype_audit_in_period["审计意见类型"]=="无法发表意见")]
# 我们只需要股票代码和截止日期列。
fraudulent_set = fraudulent[["股票代码", "截止日期"]]
# 连接merge_debt_dev_risk_stock_op_magange_cash_net_govern_governother样本集，标注审计意见类型为无法发表意见的样本为舞弊样本。
fra_label_set = pd.merge(fraudulent_set, merge_debt_dev_risk_stock_op_magange_cash_net_govern_governother, on=["股票代码", "截止日期"])
fra_label_set
Out[350]:
股票代码 截止日期 流动比率 速动比率 保守速动比率 现金比率 利息保障倍数B 资产负债率 权益乘数 产权比率 ... 资产报酬率B 总资产净利润率（ROA）B 净资产收益率B 营业利润率 董事会会议次数 监事会会议次数 股东大会召开次数 董事人数 其中：独立董事人数 监事总规模
0 600747 2016-12-31 4.317858 4.216362 0.429126 0.007179 -2.644175 0.205642 1.258879 0.258879 ... -0.028217 -0.038888 -0.048696 -0.057097 8 0 3 7 3 3
1 600751 2007-12-31 0.099730 0.088491 0.062956 0.024068 -5.169533 1.352631 -2.835825 -3.835825 ... -0.186198 -0.222217 1.225354 -0.407479 7 3 1 8 3 3
2 600757 2008-12-31 0.270523 0.196304 0.061112 0.011513 -5.406312 1.448692 -2.228703 -3.228703 ... -0.375328 -0.444752 2.771526 -0.720218 11 3 2 8 3 5
3 600757 2009-12-31 0.161133 0.096746 0.043252 0.021410 -2.705736 2.159602 -0.862365 -1.862365 ... -0.303778 -0.416050 0.563787 -0.998000 6 4 1 9 3 5
4 600771 2007-12-31 0.520096 0.481451 0.241823 0.017312 -6.011429 1.567236 -1.762934 -2.762934 ... -0.287472 -0.335293 6.417488 -0.094552 8 4 4 13 5 2
5 600771 2008-12-31 0.474722 0.419529 0.163663 0.035937 4.028336 1.607853 -1.645136 -2.645136 ... 0.318346 0.239319 -0.395770 -1.131528 8 4 2 9 3 3
6 600771 2009-12-31 0.370831 0.319025 0.094344 0.022158 -0.615014 1.893078 -1.119722 -2.119722 ... -0.041684 -0.109461 0.142285 -0.599570 6 3 3 9 3 3
7 600806 2016-12-31 0.975833 0.464423 0.402529 0.199014 -3.361667 0.807768 5.202056 4.202056 ... -0.072584 -0.094176 -0.423256 -0.722232 19 2 4 12 4 4
8 600817 2007-12-31 1.012930 1.011474 1.010805 0.000907 -1.274907 0.966228 29.610340 28.610340 ... -0.028810 -0.051408 -0.736964 -0.049137 9 5 1 9 3 3
9 600817 2008-12-31 0.002029 0.001980 0.001742 0.000730 -57.860341 20.246714 -0.051957 -1.051957 ... -0.816729 -0.830845 2.295542 -100.895035 8 3 1 6 2 3
10 600817 2009-12-31 0.003938 0.003936 0.003751 0.003352 -113.902467 27.919909 -0.037147 -1.037147 ... -2.975073 -3.001192 0.132242 -0.984071 8 3 4 8 3 3
11 600988 2008-12-31 0.218884 0.171296 0.163674 0.012413 -0.831288 1.100881 -9.912711 -10.912711 ... -0.049032 -0.108015 2.350989 -0.589627 9 3 3 5 2 1

12 rows × 56 columns

In [351]:
# 再从违规处理样本集找舞弊的样本，依然筛选2007-12-31至2016-12-31期间的样本。
areport_astype_violate_in_period = areport_astype_violate[(areport_astype_violate["处理文件日期"] >="2007-12-31") & 
                                                  (areport_astype_violate["处理文件日期"] <="2016-12-31")].sort_values(
    by=["股票代码", "处理文件日期"])

# 按照前面的财务知识，将违规类型为：虚构利润，或虚列资产，且处罚金额>0的，标注为舞弊样本。之后再与merge_debt_dev_risk_stock_op_magange
# _cash_net_govern_governother样本inner连接，再合并审计样本集中筛选出来的舞弊样本，作为舞弊样本。
try:
    violate = areport_astype_violate_in_period["违规类型"].str.split(",", expand=True)
    violate.columns = ["违规类型"]*violate.columns.size
except KeyError:
    pass

mask = violate.isin(["虚构利润", "虚列资产"])
# 获得虚构利润和虚列资产的样本，但还需要后面与areport_astype_violate_in_period样本集连接起来。
violate_mask = violate[mask].dropna(how="all")    
areport_astype_violate_in_period.drop(["违规类型"], axis=1,inplace=True)

violate_in_period = areport_astype_violate_in_period.loc[violate_mask.index]
violate_mask_in_period = pd.concat([violate_in_period, violate_mask], axis=1)
fraudulent_violate = violate_mask_in_period[(violate_mask_in_period["处罚金额-上市公司"]>0)]
print("筛选出的虚列利润，或虚列资产，且受到罚款的样本数为：", fraudulent_violate["违规事件ID"].size)

# 对fraudulent_violate的违规年度进行筛选，只需要2007-2016年度的样本记录
fraudulent_violate_in_period_mask = fraudulent_violate["违规年度"].isin([2007,2008,2009,2010,2011,2012,2013,2014
                                                                     ,2015,2016])
# 筛选出在2007-2016年间违规行为的样本。
fraudulent_violate_in_period_mask.dropna(how="all", inplace=True)
f_violate_in_period = fraudulent_violate["违规年度"][fraudulent_violate_in_period_mask].fillna(0).astype("int")
fraudulent_violate_for_concat = fraudulent_violate.drop(["违规年度"], axis=1)
fraudulent_violate_in_period = pd.concat([fraudulent_violate_for_concat, f_violate_in_period], axis=1)
fraudulent_violate_in_period["违规年度"]

# 因为违规样本的处理文件日期和公告日期，并不是实际相关财报舞弊的年月日期，所以需要逐条从违规行为中查阅具体的违规年月日期，然后与股票
# 代码连接，形成一个“股票代码-违规日期”样本集，该样本集将与审计样本集中筛选出的舞弊样本集（也只需要股票代码-截止日期列）连接，最终
# 与merge_debt_dev_risk_stock_op_magange_cash_net_govern_governother样本集inner连接，得到最终需要的舞弊样本训练集。

# 首先通过fraudulent_violate_in_period["股票代码"]从merge_debt_dev_risk_stock_op_magange_cash_net_govern_governother样本集中筛选出舞弊
# 的公司样本。
f_vio_stock_code = pd.DataFrame(fraudulent_violate_in_period["股票代码"])
#fraudulent_company = pd.merge(f_vio_stock_code, merge_debt_dev_risk_stock_op_magange_cash_net_govern_governother
#                              ,how="inner", on=["股票代码"])
fraudulent_company = pd.merge(merge_debt_dev_risk_stock_op_magange_cash_net_govern_governother, f_vio_stock_code
                              ,how="inner", on=["股票代码"])

# 逐条人工查看违规企业的违规年度和违规行为，并人工筛选生成违规财报的截止日期。
# 筛选出merge_debt_dev_risk_stock_op_magange_cash_net_govern_governother样本集中违规企业的股票代码。
fraudulent_company_id = fraudulent_company["股票代码"].drop_duplicates()

intersection_fraudulent_violate_in_period = fraudulent_violate_in_period[
    fraudulent_violate_in_period["股票代码"].isin(fraudulent_company_id)]
intersection_fraudulent_violate_in_period[["股票代码", "违规年度"]]

# 经逐条查阅违规行为，将600800相关记录从merge_debt_dev_risk_stock_op_magange_cash_net_govern_governother样本集中删除，
# 原因是违规行为涉嫌舞弊的证据不是十分充分。既不能将其作为舞弊样本，也不能将其作为非舞弊样本。

fraudulent_violate_records = {"股票代码": [600810, 600810, 600822, 600822, 600822, 600822, 600822, 600986, 600986, 600986, 601519,
                                      601558], 
                              "截止日期": ["2014-12-31", "2015-6-30", "2008-12-31", "2009-12-31", "2010-12-31", "2011-12-31", 
                                       "2012-12-31", "2007-12-31", "2008-12-31", "2009-6-30", "2013-12-31", "2011-12-31"]}

# 人工从违规样本集中找出舞弊财报样本。
fraudulent_violate_set = pd.DataFrame(fraudulent_violate_records, columns=["股票代码", "截止日期"])
fraudulent_violate_set["截止日期"] = pd.to_datetime(fraudulent_violate_set["截止日期"])
print("\nfraudulent_violate_set的数据类型:\n", fraudulent_violate_set.dtypes)

# 从merge_debt_dev_risk_stock_op_magange_cash_net_govern_governother样本集中删除人工发现的股票代码=600800的样本记录。
company_stock_code_600800 = merge_debt_dev_risk_stock_op_magange_cash_net_govern_governother[
    merge_debt_dev_risk_stock_op_magange_cash_net_govern_governother["股票代码"]==600800].index
merge_debt_dev_risk_stock_op_magange_cash_net_govern_governother.drop(labels=company_stock_code_600800, inplace=True)

# 将获得的舞弊股票代码-截止日期与merge_debt_dev_risk_stock_op_magange_cash_net_govern_governother相关属性进行inner连接，得到从违规样本
# 集中筛选出的舞弊样本。
fraudulent_violate_sample = pd.merge(
    merge_debt_dev_risk_stock_op_magange_cash_net_govern_governother, fraudulent_violate_set, on=["股票代码", "截止日期"])
print("\n从违规样本集中人工筛选出的舞弊样本集shape是：", fraudulent_violate_sample.shape)

筛选出的虚列利润，或虚列资产，且受到罚款的样本数为： 58

fraudulent_violate_set的数据类型:
 股票代码             int64
截止日期    datetime64[ns]
dtype: object

从违规样本集中人工筛选出的舞弊样本集shape是： (10, 56)
In [352]:
# 将之前从违规样本集中得到的舞弊样本集fraudulent_violate_sample，和从审计样本集中得到的舞弊样本集fra_label_set合并，得到的总的舞弊样本集。
fraudulent_set = pd.concat([fraudulent_violate_sample, fra_label_set], ignore_index=True)
# 给舞弊样本集增加标签列，标签值为-1，表示舞弊类型。
fraudulent_set["label"] = -1
print("按照前述的判断舞弊样本的业务知识，从源数据中最终得到的舞弊样本shape是：", fraudulent_set.shape)    # 得到舞弊样本的训练集。

按照前述的判断舞弊样本的业务知识，从源数据中最终得到的舞弊样本shape是： (22, 57)
In [353]:
# 从审计样本集中找出标准无保留意见的财报样本，并只保留股票代码和截止日期属性。
normal_audit = areport_astype_audit_in_period[(areport_astype_audit_in_period["审计意见类型"]=="标准无保留意见")]
print("标准无保留意见的审计样本集shape是：：", normal_audit.shape)
# 将上一步所得数据集，与merge_debt_dev_risk_stock_op_magange_cash_net_govern_governother样本集inner连接
normal_select = pd.merge(merge_debt_dev_risk_stock_op_magange_cash_net_govern_governother, normal_audit, on=["股票代码", "截止日期"])
print("inner筛选后的标准无保留意见样本数目为：", normal_select.index.size)

标准无保留意见的审计样本集shape是：： (22527, 3)
inner筛选后的标准无保留意见样本数目为： 2963
In [354]:
# 考虑到现代企业经营中，现金流>0，但利润<0的例子也越来越多。所以最终我们只筛选出现金流为负，但利润为正的财报样本，将这些样本删除。
delete_index = normal_select[(normal_select["营业收入现金净含量"]<0) & (normal_select["营业利润现金净含量"]>0)].index

# 删除上述样本
normal_select.drop(labels=delete_index, inplace=True)
print("最终得到非舞弊样本数目：", normal_select[["营业收入现金净含量", "营业利润现金净含量"]].index.size)

最终得到非舞弊样本数目： 2788
In [374]:
# 现在再检查下筛选得到的normal_select样本是否出现在违规样本中。这种检验比较好的方法是将normal_select样本集和从违规样本集中得到的
# fraudulent_violate_set的股票代码和截止日期转换成multiindex，然后用index.isin()找到是哪个index重复出现在两个样本集中。

# 先将fraudulent_violate_set的股票代码和截止日期转换成multiindex。
idx_array_check_fraudulent_violate = [list(fraudulent_violate_set["股票代码"]),list(fraudulent_violate_set["截止日期"])]
tuples_idx_array_check_fraudulent_violate = list(zip(*idx_array_check_fraudulent_violate))
idx_check_fraudulent_violate = pd.MultiIndex.from_tuples(tuples_idx_array_check_fraudulent_violate,names=["股票代码", "截止日期"])

# 再将normal_select样本的股票代码和截止日期转换成multiindex。
normal_select_check = normal_select.iloc[:,2:]
idx_array_check_normal = [list(normal_select["股票代码"]), list(normal_select["截止日期"])]
tuples_idx_array_check_normal = list(zip(*idx_array_check_normal))
idx_check_normal = pd.MultiIndex.from_tuples(tuples_idx_array_check_normal, names=["股票代码", "截止日期"])
normal_select_check.index = idx_check_normal

# 检查normal_select_check样本集中是否有idx_check_fraudulent_violate对应的样本（即舞弊，样本）。
normal_select[idx_check_normal.isin(idx_check_fraudulent_violate)]
# 最终发现股票代码=601558，截止日期=2011-12-31的样本既出现在normal_select样本集中，也出现在违规舞弊样本中。所以将其从normal_select中
# 删除，归为舞弊样本。
normal_select.drop(labels=normal_select[idx_check_normal.isin(idx_check_fraudulent_violate)].index, inplace=True)

# 将“审计意见类型”列换成标签+1。
try:
    # normal_select_weight指权重最大的非舞弊样本，因其评审条件最严格，是最可靠的非舞弊样本。
    normal_select = normal_select.drop(labels=["审计意见类型"], axis=1)
except KeyError:
    pass
normal_select["label"] = 1
print("得到的非舞弊样本shape是：", normal_select_weight.shape)

得到的非舞弊样本shape是： (2780, 57)
In [500]:
# 检查所得的非舞弊样本和舞弊样本是否存在空值
print("删除空值后，非舞弊样本shape：", normal_select.dropna().shape, "\n删除空值后，舞弊样本shape：",fraudulent_set.dropna().shape)

# 发现都有所减少。进一步查看各字段空值情况。如果仅有少量字段有空值，则考虑用该类样本该指标的均值代替。否则删除空值。
cols = ['流动比率', '速动比率', '保守速动比率', '现金比率', '利息保障倍数B', '资产负债率',
       '权益乘数', '产权比率', '权益对负债比率', '有形净值债务率', '固定资产增长率B', '总资产增长率B',
       '净资产收益率增长率B', '净利润增长率B', '利润总额增长率B', '营业利润增长率B', '营业收入增长率B',
       '所有者权益增长率B', '财务杠杆', '经营杠杆', '综合杠杆', '股本总数', '国有股', '一般法人配售', '已流通股份',
       '应收账款与收入比', '应收账款周转率B', '存货与收入比', '存货周转率B', '应付账款周转率B', '营运资金（资本）周转率B',
       '现金及现金等价物周转率B', '流动资产与收入比', '流动资产周转率B', '固定资产与收入比', '固定资产周转率B',
       '非流动资产周转率B', '总资产周转率B', '股东权益周转率B', '营业收入现金净含量','营业利润现金净含量', '营运指数', 
        '资产报酬率B', '总资产净利润率（ROA）B', '净资产收益率B', '营业利润率', '董事会会议次数', '监事会会议次数', 
        '股东大会召开次数', '董事人数', '其中：独立董事人数', '监事总规模']

print("非舞弊样本空值情况：\n")
for col in cols:
    if normal_select[col].isna().any():
        print("带空值的字段名是：", col)
        
print("舞弊样本空值情况：\n")
for col in cols:
    if fraudulent_set[col].isna().any():
        print("带空值的字段名是：", col)
        
# 根据检查空值结果，对舞弊样本的“有形净值债务率”填充mean值。非舞弊样本的空值全部删除。

normal_select.dropna(inplace=True)    # 删除非舞弊样本的空值

fraudulent_set["有形净值债务率"] = fraudulent_set["有形净值债务率"].fillna(value=fraudulent_set["有形净值债务率"].mean()) 

删除空值后，非舞弊样本shape： (2589, 57) 
删除空值后，舞弊样本shape： (22, 57)
非舞弊样本空值情况：

舞弊样本空值情况：
In [477]:
# 再考虑对净资产收益率指标进行评估，我们选择净资产收益率的置信度为0.99的normal_select中样本作为非舞弊样本。

mean_normal = normal_select["净资产收益率B"].mean()
std_normal = normal_select_weight["净资产收益率B"].std()
print("非舞弊样本的净资产收益率的标准差：", std_normal)

Z_t001_df_infinite = 2.5758    # 置信度0.99，t分布下alpha=0.01

mask_normal_net = (normal_select["净资产收益率B"]>=(mean_normal-Z_t001_df_infinite*std_normal)) & \
                  (normal_select["净资产收益率B"]<=(mean_normal+Z_t001_df_infinite*std_normal))

# 最终得到经严格评估筛选出来的非舞弊样本。
normal = normal_select[mask_normal_net]
print("最终得到的非舞弊样本shape是：", normal.shape)

# 查看筛选得到的非舞弊样本的净资产收益率统计描述。
print("非舞弊样本的净资产收益率统计描述：\n", normal_select_weight["净资产收益率B"].describe())
min_normal = normal["净资产收益率B"].min()
max_normal = normal["净资产收益率B"].max()
count_normal = normal["净资产收益率B"].count()

# 查看非舞弊样本的净资产收益率直方图。
plt.figure(figsize=(16,9))    # 定义图表的尺寸，单位inch。

plt.xlabel("净资产收益率")
plt.ylabel("数目")
plt.title("净资产收益率直方图")

bins = np.arange(min_normal, max_normal+0.001, 0.01)
plt.hist(normal["净资产收益率B"], bins=bins, color="steelblue")

plt.show()

# 从直方图看到，按照净资产收益率指标筛选非舞弊样本后，非舞弊样本总数减少120个。统计分布总体包络似乎依然符合正态分布。
# 净资产收益率>0的区间内，样本数量更多。这意味着更多非舞弊企业的净资产收益率为正数。
# 在严格评估的非舞弊样本中，净资产收益率0-0.3之间，与现实情况比较吻合。

非舞弊样本的净资产收益率的标准差： 0.10098230116102716
最终得到的非舞弊样本shape是： (2492, 57)
非舞弊样本的净资产收益率统计描述：
 count    2761.000000
mean        0.088913
std         0.100982
min        -0.554535
25%         0.039380
50%         0.080221
75%         0.133136
max         0.766859
Name: 净资产收益率B, dtype: float64
In [478]:
# 查看非舞弊样本企业所在省份的分布
plt.figure(figsize=(16,9))
province_normal = normal["所属省份"].drop_duplicates()
province_count = province_normal.size

province_normal.index = np.arange(province_count)

y_label_count = []    # 定义各省份的企业数目，作为柱状图的y轴数值使用。
for i in range(province_count):
    y_label_count.append(normal["所属省份"][normal["所属省份"]==province_normal[i]].count())
    
# 映射省份index与省份名称，为方便各省份企业数目的排序。
map_province_company_count= pd.DataFrame({"province": province_normal, "count": y_label_count})
map_province_company_count.sort_values(by=["count"], ascending=False, inplace=True)
map_province_company_count.index = np.arange(province_count)
plt.xlabel("省份")
plt.ylabel("企业数目")
plt.title("非舞弊企业样本数目在各省份的分布")
plt.bar(map_province_company_count.index, map_province_company_count["count"], color="steelblue", 
        tick_label=map_province_company_count["province"])
plt.show()
In [501]:
# 查看舞弊企业的行业分布
fraudulent_industry = fraudulent_set["行业名称"].drop_duplicates()
fraudulent_industry.index = np.arange(fraudulent_industry.index.size)
fraudulent_industry_count = []
for i in range(fraudulent_industry.index.size):
    fraudulent_industry_count.append(fraudulent_set["行业名称"][(fraudulent_set["行业名称"])==fraudulent_industry[i]].count())

fraudulent_industry_map = pd.DataFrame({"industry":fraudulent_industry, "count":fraudulent_industry_count})
fraudulent_industry_map.sort_values(by=["count"], ascending=False, inplace=True)
fraudulent_industry_map.index = np.arange(fraudulent_industry.index.size)

# 绘制舞弊样本在各行业分布的柱形图
plt.figure(figsize=(16,9))
plt.xlabel("行业")
plt.ylabel("样本数目")
plt.title("舞弊样本在各行业分布的柱形图")

plt.bar(fraudulent_industry_map.index, fraudulent_industry_map["count"], color="steelblue", 
        tick_label=fraudulent_industry_map["industry"])
plt.show()
In [582]:
# 将之前得到的normal_select_weight样本集和fraudulent_set样本集合并，作为之后训练分类模型使用的样本集。
merge_samples = pd.concat([normal, fraudulent_set], ignore_index=True)
try:
    merge_samples.drop(labels=["所属省份", "行业名称"], axis=1, inplace=True)
except KeyError:
    pass
print("总体样本merge_samples的shape是：", merge_samples.shape)

总体样本merge_samples的shape是： (2514, 55)
In [596]:
# 将总体样本merge_samples划分为训练集和测试集，按照1：1比例划分。
train_set = merge_samples.iloc[::2,:]
test_set = merge_samples.iloc[1:merge_samples.shape[0]:2,:]
train_set.reset_index(drop=True, inplace=True)
test_set.reset_index(drop=True, inplace=True)

print("训练集shape：",train_set.shape, "\n测试集shape：",test_set.shape)

# 至此样本清洗工作完成。 清洗好的数据应当保存为文本文件，便于后续反复训练模型使用， 无需再重复清洗数据。

训练集shape： (1257, 55) 
测试集shape： (1257, 55)
In [408]:
# 从这一步开始，将使用SVM，Logisitic Regression，Naive Bayes三种分类器训练样本，评估分类效果，并最终选择最适合的模型。
In [ ]:
In [942]:
from sklearn.svm import SVC
from sklearn import preprocessing
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import StratifiedShuffleSplit
from sklearn.metrics import classification_report
from sklearn.model_selection import train_test_split

# 定义一个针对ndarray的统计函数
def stat(df):
    '''利用numpy统计模块，计算ndarray数据结构的统计指标。包括：min，max，mean，median，var，std，CV（变异系数），Z-score。
    其它有必要的函数需要时再补充。
    其中CV（变异系数） = std/mean
        Z-store = (value - mean(value))/std(value)，是一种标准化数据的方法。
    
    传入的obj是一个DataFrame。'''
    ndarray = np.array(df)
    if len(ndarray.shape)>=2:   
        dmin = np.min(ndarray, axis=0)     
        dmax = np.max(ndarray, axis=0)       
        dmean = np.mean(ndarray, axis=0)
        dmedian = np.median(ndarray, axis=0)
        dvar = np.var(ndarray, axis=0)
        dstd = np.std(ndarray, axis=0)    
        CV = np.divide(dstd, dmean)  
        index = ["min", "max", "mean", "median", "var", "std", "CV"]
        columns = df.columns
        output = pd.DataFrame([dmin, dmax, dmean, dmedian, dvar, dstd, CV], index=index, columns=columns)
    elif len(ndarray.shape) == 1:
        dmin = np.min(ndarray)
        dmax = np.max(ndarray)
        dmean = np.mean(ndarray)
        dmedian = np.median(ndarray)
        dvar = np.var(ndarray)
        dstd = np.std(ndarray)
        CV = np.divide(dstd, dmean)
        index = ["min", "max", "mean", "median", "var", "std", "CV"]
        name = df.name
        output = pd.Series([dmin, dmax, dmean, dmedian, dvar, dstd, CV], index=index, name=name )
    else:
        return "传入数据有误，请检查是否是ndarray数据结构。"
    return output


# 对训练数据预处理的方法，有信噪比和fisher分数两种方法。
# SN=abs((mean(attr_t)-mean(attr_f))/(std(attr_t)+std(attr_f)))，即某个属性的正例样本与负例样本的均值之差的绝对值，除以该属性的正例
# 样本与负例样本的标准差之和。
# 为尽可能覆盖总体样本数值的特征，首先我们将标注好分类标签的训练集train_svm和验证集test合并起来，它们都是dataFrame。

# 定义SN信噪比函数
def sn(df, ascending=False):
    '''输入参数df是一个带有分类标签label字段的DataFrame。
    df的shape是：n_samples*n_features.
    ascending参数=False时，计算好的SN值从大到小排序。True时按从小到大排序。'''
    df_drop_label = df.drop(labels=['label'], axis=1)
    true_set = df_drop_label[df["label"]==1]
    false_set = df_drop_label[df["label"]==-1]
    
    # 分别计算正负例样本的均值和标准差。
    mean_t = true_set.mean(axis=0)
    mean_f = false_set.mean(axis=0)
    std_t = true_set.std(axis=0)
    std_f = false_set.std(axis=0)
    
    SN = abs(mean_t - mean_f) / (std_t + std_f)
    SN.sort_values(axis = 0, ascending=False, inplace=True)
    return SN

# 另一种选择是计算fisher值。fisher值与SN值类似，但是有时它们计算的特征“重要性”并不完全相同。
def fishvalue(df, ascending=False):
    '''输入参数df是一个带有分类标签label字段的DataFrame。
    df的shape是：n_samples*n_features.
    ascending参数=False时，计算好的SN值从大到小排序。True时按从小到大排序。
    reeturn以df的列名为index，fisher value为值的Series。'''
    df_drop_label = df.drop(labels=['label'], axis=1)
    true_set = df_drop_label[df["label"]==1]
    false_set = df_drop_label[df["label"]==-1]
    #print(false_set.shape, true_set.shape)
    # 分别计算正负例样本的均值和标准差。
    mean_t = true_set.mean(axis=0)
    mean_f = false_set.mean(axis=0)
    std_t = true_set.std(axis=0)
    std_f = false_set.std(axis=0)
    
    fvalue = abs(mean_t - mean_f) / np.sqrt(np.power(std_t,2) + np.power(std_f,2))
    fvalue.sort_values(axis = 0, ascending=ascending, inplace=True)
    return fvalue

# 准备CV交叉验证用的训练数据
# 定义CV交叉验证函数。一般取n_splits=10, test_size=0.1
def CV_set(n_splits, train_size, test_size, random_state=0):
    '''定义CV交叉验证函数。X为输入的样本向量，y为样本的分类标签。其中n_split指将原数据集分割几次，一般选5到10次。train_size或test_size
    指K-fold中的K，即将一个数据集分成几折，一般有10折(test_size=0.1)，7折(test_size=0.3)。但要注意，在StratifiedShuffleSplit()函数中，
    train_size和test_size的个数不能少于2。random_state=int可以确保每次随机策略分割的数据集不重复，因为random随机数种子固定了。所谓
    stratified，指测试集中总是会保留有尽量符合源数据集中正负比例的正负例样本。'''
    cv = StratifiedShuffleSplit(n_splits=n_splits, train_size=train_size, test_size=test_size, random_state=random_state)
    #cv.get_n_splits(X,y)
    
    return cv

# 对训练集进行交叉验证grid search计算。
def grid(X, y, svc, parameters, cv):
    '''对训练集进行grid search计算。'''
    scores = ['precision', 'recall']

    # 从训练集中按分割好的K-fold，提取出分割好的训练验证集和测试验证集。
    assert isinstance(cv, StratifiedShuffleSplit), \
    "我们暂时强制必须使用sklearn.model_selection._split.StratifiedShuffleSplit"
        
    for score in scores:
        print("针对%s这个指标进行参数grid search。"%score)
        clf = GridSearchCV(svc, parameters, cv=cv, n_jobs=2, scoring='%s_macro'%score, return_train_score=True)
        # 当cv参数是将训练集分割好的cross-validation generator object时。
        for train_idx, test_idx in cv.split(X,y):
            X_train_cv, X_test_cv = X[train_idx], X[test_idx]
            y_train_cv, y_test_cv = y[train_idx], y[test_idx]
        clf.fit(X_train_cv, y_train_cv)

        # 只考察grid search中test_score的结果：
        means = clf.cv_results_['mean_test_score']
        stds = clf.cv_results_['std_test_score']
        print("在交叉验证集下Grid计算各参数下的测试集的Score值：\n")
        for mean, std, params in zip(means, stds, clf.cv_results_['params']):
            print("%0.3f (+/-%0.03f) for %r"% (mean, std * 2, params))

        print("CV交叉验证，分类结果如下报告：")
        y_true, y_pred = y_test_cv, clf.predict(X_test_cv)
        '''
        print("检查y_pred中是否只有正例或反例。\
              这导致warning:UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples.\
              出现。\n",np.unique(y_pred))
        print("检查y_true中是否有超过+/-1的奇怪的值。\
              这导致warning出现。\n",np.unique(y_true))
        '''
        print("交叉验证集真实标签有：", np.unique(y_true))
        print("交叉验证集预测标签有：", np.unique(y_pred))
        print("检查交叉验证集中测试集标签是否有预测不到的值：", set(y_true) - set(y_pred))
        # F1 = 2*(precision * recall)/(precision + recall)
        print("得到的分类结果评估报告如下：\n", classification_report(y_true, y_pred))
            
    # 查看cv交叉验证结果。
    print("本轮grid search结果，得到最好的参数选择是：", clf.best_params_)
    print("本轮grid search结果，最好的参数对应的真正测试集上的score是：", clf.best_score_)
    return (clf.best_params_, clf.best_score_)


# 以grid search的最优结果，选最优参数kernel，C，gamma重新训练样本。
def svc_op(X, y, class_weight, **kwargs):
    '''用grid search找到的最优参数C和gamma，训练最终的SVC模型。'''
    #if kernel == 'rbf':
    #    clf = SVC(class_weight=class_weight, **kwargs)
    #elif kernel == 'linear':
    #    clf = SVC(class_weight='balanced', **kwargs)
    clf = SVC(class_weight=class_weight, random_state=0, **kwargs)
    clf.fit(X,y)
    return clf


# 计算训练模型clf_op的AUC值，评估模型质量。
from sklearn.metrics import roc_auc_score
def auc(y_test_orgin, y_pred):
    auc = roc_auc_score(y_test_orgin, y_pred)
    return auc


# 将SVC模型调优的过程封装到函数中实现自动化操作。这一版调整超参的方法是：将grid search分成粗细search两部分。细部分是沿用之前的思路：
# 一旦发现超参到达最大/最小边界，则直接重新以该最大/最小点为中心，重新设置超参的范围。从代码看，这一版思路更严谨，因为时刻比较了2种
# kernel的grid search结果。但是通过grid search试验发现，仅仅有细search不够，因为初始超参网格{C,gamma}未必是合适的超参范围，在给定的
# 超参范围内找到score最大的超参组合，该组合未必是朝着优化超参的方向变化。深入思考可以发现：实际上初始超参究竟选在哪个点，哪个范围，
# 并没有什么办法可以预先判断。唯一能做的就是多设几个超参，分别计算，然后选择score最高的，且score朝着更高方向变化的超参。所以在细search
# 之前增加粗search步骤。粗search步骤采用5点法，即[{C=1,gamma=1}, {C=10^3, gamma=10^3}, {C=10^-3, gamma=10^3}, {C=10^3, gamma=10^-3},
# {C=10^-3, gamma=10^-3}]，选这5个点中最高score的点，以其为中心点，左右5个幂指数步长展开为超参范围。
def coarse_tunemodel(X_train, y_train, class_weight_option, cv, coarse_grid_parameter_boundry=3, coarse_grid_parameter_step_num=3):
    '''针对SVM模型，将训练模型的过程封装到函数中实现自动化操作，对模型参数不断调优，直至模型参数不再变化。
    X_train是不带分类标签的训练集，y_train是对应train的样本分类标签值。class_weight_option是class_weight组成的字典，它分为kernel=rbf和
    kernel=linear两种，示例：class_weight_option={'rbf':{-1:5}, 'linear':{-1:20}}。cv是交叉验证类的实例，
    目前我们一般选StratifiedShuffleSplit()。coarse_grid_parameter_boundry=3和coarse_grid_parameter_step_num=3指粗grid search中用到的
    生成超参范围的参数，我们目前使用函数np.logspace(start, end, num, base=10)生成粗grid search超参范围。
    其中end=coarse_grid_parameter_boundry=3，start是end取负数。num=coarse_grid_parameter_step_num。建议num取3，
    因为粗grid search不必太多，5个点或9个点的遍历计算应该很合适。
    '''
    # 先进行粗略的grid search，即coarse grid search。
    parameter_coarse_rbf = {'kernel':['rbf'], 'C':np.concatenate(([1],np.logspace(-3,3,2))), 
                            'gamma':np.concatenate(([1],np.logspace(-3,3,2)))}    # 首选C=1， gamma=1的超参。
    parameter_coarse_linear = {'kernel':['linear'], 'C':np.concatenate(([1],np.logspace(-3,3,2)))}
    svc_rbf = SVC(class_weight=class_weight_option['rbf'], random_state=0)
    svc_linear = SVC(class_weight=class_weight_option['linear'], random_state=0)
    grid_argument_coarse_rbf = grid(X_train, y_train, svc_rbf, parameter_coarse_rbf, cv)    # 分别计算rbf和linear核函数的最佳超参初始点。
    grid_argument_coarse_linear = grid(X_train, y_train, svc_linear, parameter_coarse_linear, cv)
    tuned_argument_rbf = grid_argument_coarse_rbf[0]    # 计算最好的超参组合。
    tuned_argument_linear = grid_argument_coarse_linear[0]
    print("RBF的粗grid search最佳超参：", tuned_argument_rbf,
          "RBF的粗grid search最高分值：", grid_argument_coarse_rbf[1])
    print("linear的粗grid search最佳超参：", tuned_argument_linear,
          "linear的粗grid search最高分值：", grid_argument_coarse_linear[1])
    
    # 在粗search基础上进行细微的search，即subtle grid search。
    # 以上一步得到的粗调超参为中心，创建新的超参范围parameter。
    rbf_C_grid_orgin = tuned_argument_rbf['C']    # 初始化超参范围的中点
    rbf_gamma_grid_orgin = tuned_argument_rbf['gamma']
    linear_C_grid_orgin = tuned_argument_linear['C']
    # 根据超参中点创建用于grid search的超参范围。
    parameter_orgin = [{'kernel':['rbf'], 'C':np.logspace(np.log10(rbf_C_grid_orgin)-5, np.log10(rbf_C_grid_orgin)+5, 11), 
                  'gamma':np.logspace(np.log10(rbf_gamma_grid_orgin)-5, np.log10(rbf_gamma_grid_orgin)+5, 11)}, 
                 {'kernel':['linear'], 'C':np.logspace(np.log10(linear_C_grid_orgin)-5, np.log10(linear_C_grid_orgin)+5, 11)}]
    
    return parameter_orgin

def subtle_tunemodel(X_train, y_train, class_weight_option, parameter, cv):
    '''针对SVM模型，将训练模型的过程封装到函数中实现自动化操作，对模型参数不断调优，直至模型参数不再变化。'''
    
    # 对不同kernel类型分开计算grid search。
    svc_rbf = SVC(class_weight=class_weight_option['rbf'], random_state=0)
    svc_linear = SVC(class_weight=class_weight_option['linear'], random_state=0)
    grid_argument_subtle_rbf = grid(X_train, y_train, svc_rbf, parameter, cv)
    grid_argument_subtle_linear = grid(X_train, y_train, svc_linear, parameter, cv)
    
    # 将各kernel类型的grid search得到的score汇总在list里。
    grid_argument_subtle_scores = [grid_argument_subtle_rbf[1], grid_argument_subtle_linear[1]]
    grid_argument_subtle_best_param = [grid_argument_subtle_rbf[0], grid_argument_subtle_linear[0]]
    tuned_argument = grid_argument_subtle_best_param[np.argmax(grid_argument_subtle_scores)]    # 选score最高对应的best parameter。
    new_kernel = tuned_argument['kernel']
    best_score = np.max(grid_argument_subtle_scores)
    
    assert (isinstance(parameter, list) or isinstance(parameter, dict)), "sklearn.svm的grid_search()中的参数应为dict或list。"
    
    # 将超参的情况分为4种。1、C是边界点，gamma不是边界点。这种情况只更新C，更新方法为以上一步得到的新的C值为中点，分别向左右
    # 减5个步长，base=10.这是一种完全初始化的方法。2、C是边界点，gamma也是边界点，则在gamma重新设置步骤，
    # 完全更新了超参{C,gamma}为新值。3、C不是边界点，gamma是边界点，则在gamma步骤重新设置超参，完全更新超参{C,gamma}为新值。4、
    # C和gamma都不是边界点，则在gamma步骤，按照找到的C和gamma各自的新边界点，设置新的超参{C, gamma}。
    try:
        if new_kernel == 'rbf':
            new_C_grid_center = tuned_argument['C']    # 将新计算得到的最优C值作为下一次grid计算的C值的中点。
            new_gamma_grid_center = tuned_argument['gamma']    # 将新计算得到的最优gamma值作为下一次grid计算的gamma值的中点。
            if new_C_grid_center == np.min(parameter['C']) or new_C_grid_center == np.max(parameter['C']):
                print("发现最优参数C为原先的最大/最小值，直接重新设置超参。")
                update_C = {'C':np.logspace(np.log10(new_C_grid_center)-5, np.log10(new_C_grid_center)+5,11)}
            else:
                new_C_grid_interval_left_endpoint = np.max(parameter['C'][parameter['C']<new_C_grid_center])
                new_C_grid_interval_right_endpoint = np.min(parameter['C'][parameter['C']>new_C_grid_center])
                update_C = {'C':np.logspace(np.log2(new_C_grid_interval_left_endpoint), 
                                            np.log2(new_C_grid_interval_right_endpoint), 11, base=2)}
                
            if new_gamma_grid_center == np.min(parameter['gamma']) or new_gamma_grid_center == np.max(parameter['gamma']):
                print("发现最优参数gamma为原先的最大/最小值，直接重新设置超参。")
                new_parameter = {'kernel':[new_kernel], 
                             'C':update_C['C'],
                             'gamma':np.logspace(np.log10(new_gamma_grid_center)-5, np.log10(new_gamma_grid_center)+5, 11)}
            else:
                new_gamma_grid_interval_left_endpoint = np.max(parameter['gamma'][parameter['gamma']<new_gamma_grid_center])
                new_gamma_grid_interval_right_endpoint = np.min(parameter['gamma'][parameter['gamma']>new_gamma_grid_center])
                
                new_parameter = {'kernel':[new_kernel], 
                                 'C':update_C['C'],
                                 'gamma':np.logspace(np.log2(new_gamma_grid_interval_left_endpoint), 
                                                     np.log2(new_gamma_grid_interval_right_endpoint), 11, base=2)}
            
            # 将得到的new_parameter赋值给parameter。
            parameter = new_parameter
                
        elif new_kernel == 'linear':
            new_C_grid_center = tuned_argument['C']    # 将新计算得到的最优C值作为下一次grid计算的C值的中点。
            if new_C_grid_center == np.min(parameter['C']) or new_C_grid_center == np.max(parameter['C']):
                print("发现最优参数C为原先的最大/最小值，直接重新设置超参。")
                update_C = {'C':np.logspace(np.log10(new_C_grid_center)-5, np.log10(new_C_grid_center)+5, 11)}
            else:
                new_C_grid_interval_left_endpoint = np.max(parameter['C'][parameter['C']<new_C_grid_center])
                new_C_grid_interval_right_endpoint = parameter['C'][-1] + parameter['C'][-1] - parameter['C'][-2]
                update_C = {'C':np.logspace(np.log2(new_C_grid_interval_left_endpoint), 
                                            np.log2(new_C_grid_interval_right_endpoint), 11, base=2)}
            new_parameter = {'kernel': [new_kernel], 'C': update_C['C']}
            # 将得到的new_parameter赋值给parameter
            parameter = new_parameter
        else:
            print("其它核函数待补充。")
            
    except (TypeError, SyntaxError):
        for i in range(len(parameter)):
            #print("现在是parameter的第%d个元素"%i)
            if parameter[i]['kernel'] == [new_kernel]:    # 只找与新的kernel类型一致的对应参数C和gamma。
                if new_kernel == 'rbf':
                    new_C_grid_center = tuned_argument['C']    # 将新计算得到的最优C值作为下一次grid计算的C值的中点。
                    new_gamma_grid_center = tuned_argument['gamma']    # 将新计算得到的最优gamma值作为下一次grid计算的gamma值的中点。
                    if new_C_grid_center == np.min(parameter[i]['C']) or new_C_grid_center == np.max(parameter[i]['C']):
                        print("发现最优参数C为原先的最大/最小值，直接重新设置超参。")
                        update_C = {'C':np.logspace(np.log10(new_C_grid_center)-5, np.log10(new_C_grid_center)+5, 11)}
                    else:
                        new_C_grid_interval_left_endpoint = np.max(parameter[i]['C'][parameter[i]['C']<new_C_grid_center])
                        new_C_grid_interval_right_endpoint = np.min(parameter[i]['C'][parameter[i]['C']>new_C_grid_center])
                        update_C = {'C':np.logspace(np.log2(new_C_grid_interval_left_endpoint),
                                                    np.log2(new_C_grid_interval_right_endpoint), 11, base=2)}
                    
                    if new_gamma_grid_center == np.min(parameter[i]['gamma']) or new_gamma_grid_center == np.max(parameter[i]['gamma']):
                        print("发现最优参数gamma为原先的最大/最小值，直接重新设置超参。")
                        new_parameter = {'kernel':[new_kernel], 
                                          'C':update_C['C'],
                                          'gamma':np.logspace(np.log10(new_gamma_grid_center)-5, np.log10(new_gamma_grid_center)+5, 11)}
                    else:
                        new_gamma_grid_interval_left_endpoint = np.max(parameter[i]['gamma'][parameter[i]['gamma']<new_gamma_grid_center])
                        new_gamma_grid_interval_right_endpoint = np.min(parameter[i]['gamma'][parameter[i]['gamma']>new_gamma_grid_center])
                        new_parameter = {'kernel':[new_kernel], 
                                         'C':update_C['C'],
                                         'gamma':np.logspace(np.log2(new_gamma_grid_interval_left_endpoint), 
                                                             np.log2(new_gamma_grid_interval_right_endpoint), 11, base=2)}
                    
                    # 将得到的new_parameter[i]赋值给parameter[i]
                    parameter[i] = new_parameter
                    break
                    
                elif new_kernel == 'linear':
                    new_C_grid_center = tuned_argument['C']    # 将新计算得到的最优C值作为下一次grid计算的C值的中点。
                    if new_C_grid_center == np.min(parameter[i]['C']) or new_C_grid_center == np.max(parameter[i]['C']):
                        print("发现最优参数C为原先的最大/最小值，直接重新设置超参。")
                        new_parameter = {'kernel': [new_kernel],
                                         'C':np.logspace(np.log10(new_C_grid_center)-5, np.log10(new_C_grid_center)+5, 11)}
                    else:
                        new_C_grid_interval_left_endpoint = np.max(parameter[i]['C'][parameter[i]['C']<new_C_grid_center])
                        new_C_grid_interval_right_endpoint = np.min(parameter[i]['C'][parameter[i]['C']>new_C_grid_center])

                        new_parameter = {'kernel':[new_kernel],
                                         'C': np.logspace(np.log2(new_C_grid_interval_left_endpoint), 
                                                          np.log2(new_C_grid_interval_right_endpoint), 11, base=2)}
                    
                    # 将得到的new_parameter[i]赋值给parameter[i]
                    parameter[i] = new_parameter
                    break
                else:
                    print("其它核函数待补充。")
    
    # parameter指下一轮微调超参使用的超参范围。tuned_argument指本轮微调得到的最优超参。best_score指本轮得到的最优超参对应的score。
    return (parameter, tuned_argument, best_score)


# 从微调好的SVC模型中获取最优超参。
def derive_param_from_tunemodel(iter_best_args):
    '''利用tunemodel(X_train, y_train, parameter)计算的结果中第二个元素，即最优参数，提取出来转换成ndarray格式。'''
    
    # 以上一轮获得的新参数C和gamma的1%作为判断参数是否stable的依据。
    #print("iter_best_args is", iter_best_args)
    if iter_best_args['kernel'] == 'rbf':
        new_param = np.array([iter_best_args['C'], iter_best_args['gamma']])
        return new_param   
    elif iter_best_args['kernel'] == 'linear':
        new_param = np.array([iter_best_args['C']])
        return new_param
    else:
        return "其它核函数的情况待开发。"
    

# 迭代调用subtle_tunemodel()，得到最终稳定的SVC模型参数值C和gamma。
def loop_tune(X_train, y_train, class_weight_option, parameter, cv, n=15, threshold=0.05):
    '''迭代调用subtle_tunemodel()，得到最终稳定的SVC模型参数值C和gamma。'''

    first_parameter = subtle_tunemodel(X_train, y_train, class_weight_option, parameter, cv)
    first_parameter_grid_search = first_parameter[0]
    first_best_param = derive_param_from_tunemodel(first_parameter[1])
    second_parameter = subtle_tunemodel(X_train, y_train, class_weight_option, parameter, cv)
    second_parameter_grid_search = second_parameter[0]
    second_best_param = derive_param_from_tunemodel(second_parameter[1])
    
    #print("first_best_param is:", first_best_param)
    #print("second_best_param is:", second_best_param)
    assert isinstance((first_best_param - second_best_param), np.ndarray), "说明是其它核函数，尚未开发相关处理函数。"
    delta = np.abs(first_best_param - second_best_param)
    old_best_param = second_best_param
    crit = old_best_param*threshold
    print("循环迭代之前，delta is:", delta)
    orgin_count = 0
    iter_parameter_grid_search = second_parameter_grid_search
    best_score_iter_last = first_parameter[2]    # 初始化grid search得到的最优超参对应的score值。
    best_score_iter = second_parameter[2]
    while np.any(delta > crit):
        if (best_score_iter-best_score_iter_last) >= 0:
            #print("循环迭代终止条件crit is：", crit)
            print("执行tunemodel()函数前，使用的grid search parameter是：\n", iter_parameter_grid_search)
            iter_parameter = subtle_tunemodel(X_train, y_train, class_weight_option, parameter, cv)
            print("第%d轮loop中，tunemodel函数得到的最优参数是："%orgin_count, iter_parameter)
            new_best_param = derive_param_from_tunemodel(iter_parameter[1])
            crit = new_best_param * threshold
            delta = np.abs(new_best_param - old_best_param)
            old_best_param = new_best_param

            iter_parameter_grid_search = iter_parameter[0]
            best_score_iter_last = best_score_iter
            best_score_iter = iter_parameter[2]
            print("这是第%d次迭代微调C和gamma。"%orgin_count)
            print("第%d次迭代，得到delta："%orgin_count, delta)
            orgin_count += 1
            if orgin_count>n or np.min(new_best_param)<1e-15 or np.max(new_best_param)>1e+15:    # 后一个条件是控制超参微调不要在错误的优化方向浪费算力，超过阈值则跳出。
                #pdb.set_trace()
                #print("科学记数法比较成功。")
                break
        else:
            raise "超参并没有优化SVC模型，请检查参数。"
    
    try:
        iter_best_parameter = iter_parameter[1]
    except UnboundLocalError:
        iter_best_parameter = second_parameter[1]
        
    return iter_best_parameter


# 画出样本特征数量与AUC数值的关系曲线
def auc_plot_by_feature_selection(feature_count, auc_values):
    plt.figure(figsize=(16,9))
    plt.xlabel("特征数目")
    plt.ylabel("AUC")
    plt.title("AUC值与样本特征数目的关系")
    plt.plot(feature_count, auc_values)
    plt.show()
    
    
# 从原样本集中筛选特征子集，这是针对SN或fisher value从大到小排序特征的样本使用的。
def feature_reduce(X, n=3):
    return X[:,:-n]


def Mann_Whitney_SumOfRank_test(m, n, m_samples, n_samples, Zcrit=1.96):
    '''这是曼.惠特尼 U非参数检验方法。
       m和n分别指一对随机向量的容量，为int。m_samples和n_samples指容量分别为m和n的随机向量本身，array_like或df，该df不带label列。
       return经曼.惠特尼 U检验筛选的样本集特征名为索引的fisher value，数据类型为Series。'''
    if_difference_feature = []    # 用list存储一对随机向量的均值是否有显著差异。1表示有显著差异，0表示没有显著差异。
    assert m_samples.shape[1] == n_samples.shape[1], "一对随机向量的列数目应当一致，它们仅样本容量，即行数可能不同"
    assert type(m_samples)==pd.DataFrame and type(n_samples)==pd.DataFrame, "一对随机向量的数据结构应为dataframe，便于对随机向量划分标签。"
    m_samples["label"], n_samples["label"] = 1, -1    # 给一对随机向量分别加标签，便于后续计算秩和。
    total_sample = pd.concat([m_samples, n_samples], ignore_index=True)    # reindex，利用其后续计算不同随机向量的秩。
    iter_columns = total_sample.columns.drop(["label"])
    for column in iter_columns:
        sample_mix_sorted_bycolumn = total_sample.loc[:,["label",column]].sort_values(by=[column])    # 对随机向量的某一列升序排序。
        rank_bycolumn = sample_mix_sorted_bycolumn[column].rank()
        sample_mix_sorted_bycolumn["rank"] = rank_bycolumn
        rank_p = sample_mix_sorted_bycolumn[sample_mix_sorted_bycolumn["label"]==1]["rank"]    # 抽取label==1的列，得到series。
        Wp = rank_p.sum()    # 正例样本的秩和
        rank_n = sample_mix_sorted_bycolumn[sample_mix_sorted_bycolumn["label"]==-1]["rank"]
        Wn = rank_n.sum()    # 负例样本的秩和
        Up = m*n + m*(m+1)/2 - Wp
        Un = m*n + n*(n+1)/2 - Wn
        U = np.min([Up, Un])
        Z = (U - m*n/2)/math.sqrt(m*n*(m+n+1)/12)
    
        # 当样本为大样本，即正负例样本容量远大于20时，Z近似逼近标准正态分布。我们一般以置信度alpha=0.05来做假设检验。双边检验时，
        # Z0.05=+/-1.96。选置信度alpha=0.05.

        if abs(Z) > Zcrit:
            if_difference_feature.append(True)    # 1表示一对随机向量的均值有显著差异。
        else:
            if_difference_feature.append(False)    # 0表示一对随机向量的均值无显著差异。
    print(if_difference_feature)   
    selected_columns = iter_columns[if_difference_feature]
    
    return selected_columns


def multicollinearity_check(sample, coef_corr_crit = 0.7):
    '''输入参数是一个不带分类标签label的DataFrame。该DataFrame的全部特征已经经过正负例均值显著性差异的假设检验，即全都是正负例均值
    有显著差异的特征向量。因为本函数用于检查多重共线性，当考虑用线性模型时，正负例样本的均值没有显著差异的特征对线性模型没有贡献。
    判断两个连续型特征向量是否相关的相关系数阈值，是一个float值，一般定为0.7。
    return是一个tuple，包括删除了多重共线性列的新的DataFrame和用list保存的计算得到的各列相关系数结果。'''
    corr_list = []    # 用空list存储遍历计算的相关系数，用于后续倒序判断是否删除后一个特征用。
    for i in range(sample.columns.size):    # i指相关系数对称方阵中的第i行
        for j in range(i+1, sample.columns.size):    # j指相关系数对称方阵中的第j列
            # 第一轮遍历，按照第1列和第2列，第2列和第3列……这样的规律遍历计算相关系数。
            #corr_list.append(samples.iloc[:,i].corr(samples.iloc[:,i+1]))
            # 判断第i列特征与其它所有列（除第1列之外）的相关系数，如果存在相关系数>相关系数阈值，则说明fishvalue最小的第1列与其它
            # 某特征强相关，删除第i列。
            corr_list.append(sample.iloc[:,i].corr(sample.iloc[:,j]))
            if abs(sample.iloc[:,i].corr(sample.iloc[:,j]))>coef_corr_crit:
                sample_independence = sample.drop(labels=sample.columns[i], axis=1)
    print("各列相关系数：\n", corr_list)
    return (sample_independence, corr_list)


def hist_matrix(df, col_num, bins=100):
    '''绘制多个直方图子图。
       输入参数df是一个DataFrame，内容为多个特征的样本集。df不应该包括除有意义的特征之外其它列，如label，index等。
       输入参数col_num指df的列的数量。绘图的行数row_num由样本特征数目和列数相除向上取整而得。
       bins指对特征向量分箱的数目，默认为50。
       return一个row_num行，col_num列的的绘图。
      '''
    import math

    # 一共需要绘制多少个子图
    sub_fig_num = df.columns.size    # 样本特征数目
    row_num = math.ceil((sub_fig_num+1)/col_num)    # 根据全部指标数目和绘图列数，计算绘图行数。
    
    fig = plt.figure(figsize=(16,9*row_num/col_num))
    
    for i in range(1, sub_fig_num+1):
        sub_fig = fig.add_subplot(row_num, col_num, i)
        min_by_col = df.iloc[:, i-1].min()
        max_by_col = df.iloc[:, i-1].max()
        plt.hist(df.iloc[:,i-1], bins=bins, color="steelblue")
        plt.xlabel(df.columns[i-1])
        plt.ylabel("数值")
        plt.title("样本各指标直方图")
    
    plt.subplots_adjust(wspace=9, hspace=16)    #调整子图间距
    plt.tight_layout()    # 调整整图空白。
    plt.show()
In [852]:
# 处理训练集、测试集数据，使之能够用于特定模型的训练和测试。主要是删除股票代码，截止日期字段，将数据类型改为ndarray。
# 首先查看训练集和测试集的统计描述，粗略判断mean+/-3*std，与max和min分别比较，检查是否有离群值。
train = train_set.drop(labels=["股票代码", "截止日期", "label"], axis=1)
test = test_set.drop(labels=["股票代码", "截止日期", "label"], axis=1)
stat_result = stat(train)

# 比较样本最大值与mean+3*std，是否>0。
great_than_exist = (stat_result.loc["max"] - (stat_result.loc["mean",:]+3*stat_result.loc["std",:]))>0
# 比较mean-3*std与样本最小值，是否>0。
less_than_exist = (stat_result.loc["min"] - (stat_result.loc["mean",:] - 3*stat_result.loc["std",:]))<0
print("如果>0，说明max值大于mean+3*std，即有离群值：", great_than_exist)
print("\n如果<0，说明min值小于mean-3*std，即有离群值：", less_than_exist)
print("\n以上结果说明训练集中很多特征存在离群值。这预示了单个分类器的性能将不会非常高。")

如果>0，说明max值大于mean+3*std，即有离群值： 0
流动比率              True
速动比率              True
保守速动比率            True
现金比率              True
利息保障倍数B           True
资产负债率             True
权益乘数              True
产权比率              True
权益对负债比率           True
有形净值债务率           True
固定资产增长率B          True
总资产增长率B           True
净资产收益率增长率B        True
净利润增长率B           True
利润总额增长率B          True
营业利润增长率B          True
营业收入增长率B          True
所有者权益增长率B         True
财务杠杆              True
经营杠杆              True
综合杠杆              True
股本总数              True
国有股               True
一般法人配售            True
已流通股份             True
应收账款与收入比          True
应收账款周转率B          True
存货与收入比            True
存货周转率B            True
应付账款周转率B          True
营运资金（资本）周转率B      True
现金及现金等价物周转率B      True
流动资产与收入比          True
流动资产周转率B          True
固定资产与收入比          True
固定资产周转率B          True
非流动资产周转率B         True
总资产周转率B           True
股东权益周转率B          True
营业收入现金净含量         True
营业利润现金净含量         True
营运指数              True
资产报酬率B           False
总资产净利润率（ROA）B    False
净资产收益率B           True
营业利润率             True
董事会会议次数           True
监事会会议次数           True
股东大会召开次数          True
董事人数              True
其中：独立董事人数         True
监事总规模             True
dtype: bool

如果<0，说明min值小于mean-3*std，即有离群值： 0
流动比率             False
速动比率             False
保守速动比率           False
现金比率             False
利息保障倍数B          False
资产负债率            False
权益乘数             False
产权比率             False
权益对负债比率          False
有形净值债务率          False
固定资产增长率B         False
总资产增长率B          False
净资产收益率增长率B        True
净利润增长率B           True
利润总额增长率B          True
营业利润增长率B         False
营业收入增长率B         False
所有者权益增长率B         True
财务杠杆             False
经营杠杆              True
综合杠杆              True
股本总数             False
国有股              False
一般法人配售           False
已流通股份            False
应收账款与收入比         False
应收账款周转率B         False
存货与收入比           False
存货周转率B           False
应付账款周转率B         False
营运资金（资本）周转率B      True
现金及现金等价物周转率B     False
流动资产与收入比         False
流动资产周转率B         False
固定资产与收入比         False
固定资产周转率B         False
非流动资产周转率B        False
总资产周转率B          False
股东权益周转率B         False
营业收入现金净含量         True
营业利润现金净含量         True
营运指数              True
资产报酬率B            True
总资产净利润率（ROA）B     True
净资产收益率B           True
营业利润率            False
董事会会议次数          False
监事会会议次数          False
股东大会召开次数         False
董事人数              True
其中：独立董事人数         True
监事总规模            False
dtype: bool

以上结果说明训练集中很多特征存在离群值。这预示了单个分类器的性能将不会非常高。
In [943]:
# 绘制各属性的直方图。
plt_samples = merge_samples.drop(labels=['股票代码', '截止日期', 'label'], axis=1)    # 准备绘制用的数据。
hist_matrix(plt_samples, 4)
In [853]:
# 利用fishervalue方法计算的样本特征重要性。
columns_by_fisher = fishvalue(merge_samples.drop(labels=['股票代码', '截止日期'], axis=1))
cols = ['label'] + list(columns_by_fisher.index)
print("按计算的SN或fishervalue排序好的列名（已添加分类标签label）：\n", cols)
print("columns_by_fisher is:\n",columns_by_fisher)
# 按SN信噪比重新排序dataframe的columns顺序。
train_cols_fisher_value_ordered = train.loc[:,cols]
train_cols_fisher_value_ordered['label'] = train_set['label']
test_cols_fisher_value_ordered = test.loc[:,cols]
test_cols_fisher_value_ordered['label'] = test_set['label']

按计算的SN或fishervalue排序好的列名（已添加分类标签label）：
 ['label', '监事总规模', '权益对负债比率', '非流动资产周转率B', '营业收入现金净含量', '应收账款与收入比', '总资产净利润率（ROA）B', '总资产周转率B', '股东权益周转率B', '应付账款周转率B', '固定资产周转率B', '资产报酬率B', '保守速动比率', '资产负债率', '净资产收益率B', '流动比率', '有形净值债务率', '现金比率', '流动资产周转率B', '现金及现金等价物周转率B', '监事会会议次数', '速动比率', '综合杠杆', '营业利润率', '营业收入增长率B', '财务杠杆', '固定资产与收入比', '存货与收入比', '股本总数', '股东大会召开次数', '已流通股份', '固定资产增长率B', '经营杠杆', '总资产增长率B', '董事人数', '所有者权益增长率B', '其中：独立董事人数', '净利润增长率B', '国有股', '营运资金（资本）周转率B', '营业利润现金净含量', '应收账款周转率B', '利润总额增长率B', '董事会会议次数', '营运指数', '一般法人配售', '产权比率', '权益乘数', '利息保障倍数B', '营业利润增长率B', '净资产收益率增长率B', '存货周转率B', '流动资产与收入比']
columns_by_fisher is:
 0
监事总规模            0.548191
权益对负债比率          0.518996
非流动资产周转率B        0.504345
营业收入现金净含量        0.460457
应收账款与收入比         0.445547
总资产净利润率（ROA）B    0.443927
总资产周转率B          0.431815
股东权益周转率B         0.429421
应付账款周转率B         0.415471
固定资产周转率B         0.412982
资产报酬率B           0.406682
保守速动比率           0.398686
资产负债率            0.376918
净资产收益率B          0.350935
流动比率             0.349765
有形净值债务率          0.349547
现金比率             0.340413
流动资产周转率B         0.332929
现金及现金等价物周转率B     0.326825
监事会会议次数          0.253676
速动比率             0.250274
综合杠杆             0.238151
营业利润率            0.232845
营业收入增长率B         0.203102
财务杠杆             0.194951
固定资产与收入比         0.193079
存货与收入比           0.192904
股本总数             0.185301
股东大会召开次数         0.182862
已流通股份            0.178975
固定资产增长率B         0.161007
经营杠杆             0.159151
总资产增长率B          0.156792
董事人数             0.146993
所有者权益增长率B        0.137061
其中：独立董事人数        0.130426
净利润增长率B          0.122319
国有股              0.085387
营运资金（资本）周转率B     0.079066
营业利润现金净含量        0.078418
应收账款周转率B         0.071688
利润总额增长率B         0.064017
董事会会议次数          0.062636
营运指数             0.050425
一般法人配售           0.034427
产权比率             0.028945
权益乘数             0.028945
利息保障倍数B          0.022635
营业利润增长率B         0.020660
净资产收益率增长率B       0.019121
存货周转率B           0.014615
流动资产与收入比         0.003325
dtype: float64
In [854]:
# 其次需要查看样本集中各特征向量之间的相关性，当使用线性模型时，需要考虑多重共线性问题。利用之前按fisher value排序好的特征进行计算。
total_sample = merge_samples.drop(labels=["股票代码", "截止日期", "label"], axis=1)
total_sample = total_sample.loc[:,columns_by_fisher.index]
print("删除了股票代码，截止日期，label三个属性的样本集shape是：", total_sample.shape)
print("检查total_sample的columns：\n", total_sample.columns)

# 将total_sample样本集按label分成正例和负例子集。
p_samples = total_sample[merge_samples['label']==1]
n_samples = total_sample[merge_samples['label']==-1]
p_samples_count = p_samples.shape[0]
n_samples_count = n_samples.shape[0]
feature_selected = Mann_Whitney_SumOfRank_test(p_samples_count, n_samples_count, p_samples, n_samples, Zcrit=1.96)
print("feature_selected的个数：", feature_selected.size)

# 然后对筛选出的正负例样本有显著差异的连续型特征向量计算相关系数，找出高度相关的特征。
feature_test_sample = total_sample.loc[:, feature_selected]

删除了股票代码，截止日期，label三个属性的样本集shape是： (2514, 52)
检查total_sample的columns：
 Index(['监事总规模', '权益对负债比率', '非流动资产周转率B', '营业收入现金净含量', '应收账款与收入比',
       '总资产净利润率（ROA）B', '总资产周转率B', '股东权益周转率B', '应付账款周转率B', '固定资产周转率B',
       '资产报酬率B', '保守速动比率', '资产负债率', '净资产收益率B', '流动比率', '有形净值债务率', '现金比率',
       '流动资产周转率B', '现金及现金等价物周转率B', '监事会会议次数', '速动比率', '综合杠杆', '营业利润率',
       '营业收入增长率B', '财务杠杆', '固定资产与收入比', '存货与收入比', '股本总数', '股东大会召开次数', '已流通股份',
       '固定资产增长率B', '经营杠杆', '总资产增长率B', '董事人数', '所有者权益增长率B', '其中：独立董事人数',
       '净利润增长率B', '国有股', '营运资金（资本）周转率B', '营业利润现金净含量', '应收账款周转率B', '利润总额增长率B',
       '董事会会议次数', '营运指数', '一般法人配售', '产权比率', '权益乘数', '利息保障倍数B', '营业利润增长率B',
       '净资产收益率增长率B', '存货周转率B', '流动资产与收入比'],
      dtype='object', name=0)
[True, True, False, True, False, True, False, False, False, False, True, True, True, False, True, True, True, False, True, False, True, True, True, True, True, False, True, True, False, True, True, True, True, False, True, False, True, False, False, True, False, True, False, False, False, False, False, True, False, True, False, False]
feature_selected的个数： 28
In [855]:
# 通过观察训练集的特征属性的统计描述和直方图，我们注意到2个基本情况：
# 1、各特征属性的分布并非正态分布，而是有偏度的（一般右偏，这被认为是符合市场常识的），或者是其它分布。
# 2、各特征属性的数量级不同，且个别特征属性差别非常大，需要标准化。
# 至于训练样本中是否有离群值，这个需要结合具体财报相关知识，以及具体企业在各自行业的规律的业务知识，我们目前无法给出准确判断。从观察
# 看，没有特别夸张的离群数值出现，只有“营业利润增长率”和“利息保障倍数”2个指标有5倍标准差的样本出现（各1例），所以假设训练集中没有
# 离群值，即都是正常业务下的正常范围内数值。

# 对训练集数据进行scaling。我们采用Z-score方法进行scaling。但随后发现“一般法人配售”属性的值在训练集中全为0，但该属性在整个源数据集中
# 并不全为0。我们暂时想保留这个属性，所以放弃Z-score方法，因为该属性的标准差为0.
# ndf_mean = np.mean(ndf, axis=0)
# ndf_std = np.std(ndf, axis=0)
# X = (ndf - ndf_mean) / ndf_std

# scaling思路改为将训练集各属性值映射到[0,1]之间。为方便，直接调用sklearn的preprocessing模块。

# 首先将准备好的训练集、测试集的数据类型从DataFrame改为ndarray。
X_train_orgin = np.array(train_cols_fisher_value_ordered.drop(labels=['label'], axis=1))
y_train = np.array(train_cols_fisher_value_ordered["label"])
X_test_orgin = np.array(test_cols_fisher_value_ordered.drop(labels=['label'], axis=1))
y_test = np.array(test_cols_fisher_value_ordered['label'])

min_max_scaler = preprocessing.MinMaxScaler()
X_train = min_max_scaler.fit_transform(X_train_orgin)    # 适用于sklearn svm SVC的训练集。
print("X_train的shape是：", X_train.shape)    # 每个属性都在[0,1]之间。至此训练集数据准备就绪。

# 删除数据集的列名，并转换为ndarray格式，然后与训练集做同样的标准化。
X_test = min_max_scaler.fit_transform(X_test_orgin)    # 与训练集一样，将test_svm_orgin标准化
print("X_test的shape是：", X_test.shape)

X_train的shape是： (1257, 52)
X_test的shape是： (1257, 52)
In [857]:
# SVC训练初始参数。

class_weight_rbf = {-1:15}
class_weight_linear = {-1:30}
class_weight_option = {'rbf':class_weight_rbf, 'linear':class_weight_linear}
#svc = SVC(class_weight=class_weight, random_state=0)#'balanced' 

# 针对二元分类，利用AUC进行特征筛选，找到最优特征数目。
auc_values_svc = []    # 用列表存储多次特征筛选后得到的SVC模型的AUC值。
best_params_svc = []    # 用列表存储多次特征筛选后得到的SVC模型的最优参数。
X_train_iter_svc = X_train
X_test_iter_svc = X_test
feature_selection_iteration_count = X_train.shape[1] - 4    # 设置最多要删减多少个属性
reduce_count_per_iterate = 1    # 设置每次删减几个属性
for i in range(feature_selection_iteration_count):    # 最多操作10次，删减30个特征。
    # 由于tunemodel()函数返回值为parameter，所以将初始化的parameter移到循环内部。
    cv_iter = CV_set(10, 0.5, 0.5)
    parameter = coarse_tunemodel(X_train_iter_svc, y_train, class_weight_option, 
                                 cv_iter, coarse_grid_parameter_boundry=3, coarse_grid_parameter_step_num=3)
    print("粗grid search得到的parameter是：\n",parameter)
    #pdb.set_trace()    # c继续，q中断并离开。
    param_op_iter = loop_tune(X_train_iter_svc, y_train, class_weight_option, parameter, cv_iter)
    best_params_svc.append(param_op_iter)
    
    # 将舞弊样本权重提高至非舞弊样本权重的20倍后，预测测试集正确率。
    train_sample_weight_iter = np.ones(X_train_iter_svc.shape[0], dtype=int)
    test_sample_weight_iter = np.ones(X_test_iter_svc.shape[0], dtype=int)
    try:    # 针对kernel=rbf的情况
        clf_op_iter = svc_op(X_train_iter_svc, y_train, class_weight_rbf, 
                         kernel = param_op_iter['kernel'], 
                         C=param_op_iter['C'], 
                         gamma=param_op_iter['gamma'])
        train_sample_weight_iter[train_set["label"]==-1] = class_weight_option['rbf'][-1]
        # 预测样本的score中，正负例的权重与训练样本中正负例权重保持一致。
        test_sample_weight_iter[test_set["label"]==-1] = class_weight_option['rbf'][-1]
    except KeyError:    # 针对kernel=linear的情况
        clf_op_iter = svc_op(X_train_iter_svc, y_train, class_weight_linear, kernel=param_op_iter['kernel'], C=param_op_iter['C'])
        train_sample_weight_iter[train_set["label"]==-1] = class_weight_option['linear'][-1]
        # 预测样本的score中，正负例的权重与训练样本中正负例权重保持一致。
        test_sample_weight_iter[test_set["label"]==-1] = class_weight_option['linear'][-1]
    print("SVC模型clf_op_iter的参数是：", clf_op_iter.get_params(deep=True))
    
    # 将舞弊样本权重提高至非舞弊样本权重的20倍后，预测训练集正确率。
    print("\n训练模型SVC对训练样本的预测准确率：", clf_op_iter.score(X_train_iter_svc, y_train, sample_weight=train_sample_weight_iter))
    
    y_pred_iter_svc = clf_op_iter.predict(X_test_iter_svc)
    print("\n测试集中，预测为舞弊样本的有：", np.where(y_pred_iter_svc==-1), 
          "\n测试集中，实际为舞弊样本的有：", np.where(y_test==-1),
          "\n预测的舞弊样本数目：", y_pred_iter_svc[y_pred_iter_svc==-1].size)
    
    print("\n训练模型SVC对测试样本的预测准确率：", clf_op_iter.score(X_test_iter_svc,y_test, sample_weight=test_sample_weight_iter))
    
    # 计算训练模型clf_op的AUC值，评估模型质量。
    auc_value_iter = auc(y_test, y_pred_iter_svc)
    auc_values_svc.append(auc_value_iter)
    print("以上是第%d次特征筛选。"%i)
    print("第%d次特征筛选，AUC值是："%i, auc_value_iter)
    print("X_train_iter_svc.shape is:",X_train_iter_svc.shape)
    print("X_test_iter_svc.shape is:",X_test_iter_svc.shape)
    X_train_iter_svc = feature_reduce(X_train_iter_svc, n=reduce_count_per_iterate)
    X_test_iter_svc = feature_reduce(X_test_iter_svc, n=reduce_count_per_iterate)


# 计算特征数目
print("AUC值随特征数目变化：", auc_values_svc)
feature_count_svc = [X_train.shape[1] - reduce_count_per_iterate*i for i in range(feature_selection_iteration_count)]
print("样本特征数目：", feature_count_svc)

auc_plot_by_feature_selection(feature_count_svc, auc_values_svc)
print("各轮特征筛选得到的最优超参是：\n", best_params_svc)

针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.616 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6166666666666667
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6663461538461538
RBF的粗grid search最佳超参： {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0} RBF的粗grid search最高分值： 0.6166666666666667
linear的粗grid search最佳超参： {'C': 1.0, 'kernel': 'linear'} linear的粗grid search最高分值： 0.6663461538461538
粗grid search得到的parameter是：
 [{'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05]), 'kernel': ['rbf'], 'gamma': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.689 (+/-0.436) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.662 (+/-0.389) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.714 (+/-0.417) for {'C': 10.0, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 100.0, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 1000.0, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 10000.0, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      1.00       623

avg / total       0.98      0.99      0.99       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.324) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.324) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.323) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.665 (+/-0.314) for {'C': 10.0, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 100.0, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 10000.0, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 10.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.665224358974359
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.672 (+/-0.392) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.689 (+/-0.374) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.689 (+/-0.436) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.383) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.689 (+/-0.382) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.689 (+/-0.382) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.681 (+/-0.372) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.662 (+/-0.389) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.681 (+/-0.372) for {'C': 10.0, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 100.0, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 1000.0, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 10000.0, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.641 (+/-0.235) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.315) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.666 (+/-0.315) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.324) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.315) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.324) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.666 (+/-0.314) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.323) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.664 (+/-0.315) for {'C': 10.0, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 100.0, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 10000.0, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6663461538461538
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.689 (+/-0.436) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.662 (+/-0.389) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.689 (+/-0.436) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.681 (+/-0.372) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.714 (+/-0.417) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      1.00       623

avg / total       0.98      0.99      0.99       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.324) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.324) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.323) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.641 (+/-0.235) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.665 (+/-0.314) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 9.999999999999998, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.665224358974359
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.672 (+/-0.392) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.689 (+/-0.374) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.689 (+/-0.436) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.383) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.689 (+/-0.382) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.689 (+/-0.382) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.681 (+/-0.372) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.662 (+/-0.389) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.689 (+/-0.382) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.659 (+/-0.313) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.663 (+/-0.372) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.635 (+/-0.307) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.681 (+/-0.372) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.641 (+/-0.235) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.315) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.666 (+/-0.315) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.324) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.315) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.324) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.666 (+/-0.314) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.323) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.666 (+/-0.315) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.665 (+/-0.313) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.664 (+/-0.312) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.663 (+/-0.313) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.664 (+/-0.315) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 0.6309573444801931, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6663461538461538
循环迭代之前，delta is: [0.36904266]
执行tunemodel()函数前，使用的grid search parameter是：
 [{'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05]), 'kernel': ['rbf'], 'gamma': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05])}, {'C': array([0.39810717, 0.43651583, 0.47863009, 0.52480746, 0.57543994,
       0.63095734, 0.69183097, 0.75857758, 0.83176377, 0.91201084,
       1.        ]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.689 (+/-0.436) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.662 (+/-0.389) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.43651583224016594, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.47863009232263826, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.5248074602497725, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.5754399373371568, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.6918309709189364, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.7585775750291837, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.8317637711026709, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.9120108393559095, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.9999999999999997, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      1.00       623

avg / total       0.98      0.99      0.99       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.324) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.324) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.323) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.43651583224016594, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.47863009232263826, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.5248074602497725, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.5754399373371568, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.6918309709189364, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.7585775750291837, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.8317637711026709, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.9120108393559095, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.9999999999999997, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6397435897435898
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.672 (+/-0.392) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.689 (+/-0.374) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.689 (+/-0.436) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.383) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.689 (+/-0.382) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.689 (+/-0.382) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.681 (+/-0.372) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.662 (+/-0.389) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.43651583224016594, 'kernel': 'linear'}
0.797 (+/-0.492) for {'C': 0.47863009232263826, 'kernel': 'linear'}
0.772 (+/-0.473) for {'C': 0.5248074602497725, 'kernel': 'linear'}
0.747 (+/-0.449) for {'C': 0.5754399373371568, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.6918309709189364, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.7585775750291837, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.8317637711026709, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.9120108393559095, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.9999999999999997, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.641 (+/-0.235) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.315) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.666 (+/-0.315) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.324) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.315) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.324) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.666 (+/-0.314) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.323) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.43651583224016594, 'kernel': 'linear'}
0.642 (+/-0.236) for {'C': 0.47863009232263826, 'kernel': 'linear'}
0.642 (+/-0.236) for {'C': 0.5248074602497725, 'kernel': 'linear'}
0.641 (+/-0.236) for {'C': 0.5754399373371568, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.6918309709189364, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.7585775750291837, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.8317637711026709, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.9120108393559095, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.9999999999999997, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 0.6309573444801931, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6663461538461538
第0轮loop中，tunemodel函数得到的最优参数是： ([{'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05]), 'kernel': ['rbf'], 'gamma': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05])}, {'C': array([0.57543994, 0.58613816, 0.59703529, 0.608135  , 0.61944108,
       0.63095734, 0.64268772, 0.65463617, 0.66680677, 0.67920363,
       0.69183097]), 'kernel': ['linear']}], {'C': 0.6309573444801931, 'kernel': 'linear'}, 0.6663461538461538)
这是第0次迭代微调C和gamma。
第0次迭代，得到delta： [0.]
SVC模型clf_op_iter的参数是： {'kernel': 'linear', 'decision_function_shape': 'ovr', 'class_weight': {-1: 30}, 'tol': 0.001, 'gamma': 'auto', 'random_state': 0, 'cache_size': 200, 'verbose': False, 'degree': 3, 'C': 0.6309573444801931, 'probability': False, 'shrinking': True, 'coef0': 0.0, 'max_iter': -1}

训练模型SVC对训练样本的预测准确率： 0.9029187817258884

测试集中，预测为舞弊样本的有： (array([ 370,  658, 1246, 1247, 1248, 1251, 1252, 1255, 1256], dtype=int64),) 
测试集中，实际为舞弊样本的有： (array([1246, 1247, 1248, 1249, 1250, 1251, 1252, 1253, 1254, 1255, 1256],
      dtype=int64),) 
预测的舞弊样本数目： 9

训练模型SVC对测试样本的预测准确率： 0.9225888324873096
以上是第0次特征筛选。
第0次特征筛选，AUC值是： 0.8173792499635195
X_train_iter_svc.shape is: (1257, 52)
X_test_iter_svc.shape is: (1257, 52)
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.616 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6166666666666667
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6663461538461538
RBF的粗grid search最佳超参： {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0} RBF的粗grid search最高分值： 0.6166666666666667
linear的粗grid search最佳超参： {'C': 1.0, 'kernel': 'linear'} linear的粗grid search最高分值： 0.6663461538461538
粗grid search得到的parameter是：
 [{'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05]), 'kernel': ['rbf'], 'gamma': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.689 (+/-0.436) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.714 (+/-0.417) for {'C': 10.0, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 100.0, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 1000.0, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 10000.0, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      1.00       623

avg / total       0.98      0.99      0.99       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.324) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.324) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.324) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.665 (+/-0.314) for {'C': 10.0, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 100.0, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 10000.0, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 10.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.665224358974359
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.672 (+/-0.392) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.689 (+/-0.374) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.689 (+/-0.436) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.383) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.689 (+/-0.382) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.689 (+/-0.382) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.664 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.681 (+/-0.372) for {'C': 10.0, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 100.0, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 1000.0, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 10000.0, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.641 (+/-0.235) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.315) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.666 (+/-0.315) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.324) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.315) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.324) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.324) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.664 (+/-0.315) for {'C': 10.0, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 100.0, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 10000.0, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6663461538461538
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.689 (+/-0.436) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.689 (+/-0.436) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.714 (+/-0.418) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.714 (+/-0.417) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      1.00       623

avg / total       0.98      0.99      0.99       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.324) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.324) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.324) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.641 (+/-0.235) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.665 (+/-0.314) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 9.999999999999998, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.665224358974359
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.672 (+/-0.392) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.689 (+/-0.374) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.689 (+/-0.436) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.383) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.689 (+/-0.382) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.689 (+/-0.382) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.664 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.689 (+/-0.382) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.659 (+/-0.313) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.663 (+/-0.372) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.671 (+/-0.376) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.681 (+/-0.372) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.641 (+/-0.235) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.315) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.666 (+/-0.315) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.324) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.315) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.324) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.324) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.666 (+/-0.315) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.665 (+/-0.313) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.664 (+/-0.312) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.663 (+/-0.314) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.664 (+/-0.315) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 0.6309573444801931, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6663461538461538
循环迭代之前，delta is: [0.36904266]
执行tunemodel()函数前，使用的grid search parameter是：
 [{'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05]), 'kernel': ['rbf'], 'gamma': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05])}, {'C': array([0.39810717, 0.43651583, 0.47863009, 0.52480746, 0.57543994,
       0.63095734, 0.69183097, 0.75857758, 0.83176377, 0.91201084,
       1.        ]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.689 (+/-0.436) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.43651583224016594, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.47863009232263826, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.5248074602497725, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.5754399373371568, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.6918309709189364, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.7585775750291837, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.8317637711026709, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.9120108393559095, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.9999999999999997, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      1.00       623

avg / total       0.98      0.99      0.99       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.324) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.324) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.324) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.43651583224016594, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.47863009232263826, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.5248074602497725, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.5754399373371568, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.6918309709189364, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.7585775750291837, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.8317637711026709, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.9120108393559095, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.9999999999999997, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6397435897435898
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.672 (+/-0.392) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.689 (+/-0.374) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.689 (+/-0.436) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.383) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.689 (+/-0.382) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.689 (+/-0.382) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.664 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.43651583224016594, 'kernel': 'linear'}
0.797 (+/-0.492) for {'C': 0.47863009232263826, 'kernel': 'linear'}
0.772 (+/-0.473) for {'C': 0.5248074602497725, 'kernel': 'linear'}
0.747 (+/-0.449) for {'C': 0.5754399373371568, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.6918309709189364, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.7585775750291837, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.8317637711026709, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.9120108393559095, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.9999999999999997, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.641 (+/-0.235) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.315) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.666 (+/-0.315) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.324) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.315) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.324) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.324) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.43651583224016594, 'kernel': 'linear'}
0.642 (+/-0.236) for {'C': 0.47863009232263826, 'kernel': 'linear'}
0.642 (+/-0.236) for {'C': 0.5248074602497725, 'kernel': 'linear'}
0.641 (+/-0.236) for {'C': 0.5754399373371568, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.6918309709189364, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.7585775750291837, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.8317637711026709, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.9120108393559095, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.9999999999999997, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 0.6309573444801931, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6663461538461538
第0轮loop中，tunemodel函数得到的最优参数是： ([{'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05]), 'kernel': ['rbf'], 'gamma': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05])}, {'C': array([0.57543994, 0.58613816, 0.59703529, 0.608135  , 0.61944108,
       0.63095734, 0.64268772, 0.65463617, 0.66680677, 0.67920363,
       0.69183097]), 'kernel': ['linear']}], {'C': 0.6309573444801931, 'kernel': 'linear'}, 0.6663461538461538)
这是第0次迭代微调C和gamma。
第0次迭代，得到delta： [0.]
SVC模型clf_op_iter的参数是： {'kernel': 'linear', 'decision_function_shape': 'ovr', 'class_weight': {-1: 30}, 'tol': 0.001, 'gamma': 'auto', 'random_state': 0, 'cache_size': 200, 'verbose': False, 'degree': 3, 'C': 0.6309573444801931, 'probability': False, 'shrinking': True, 'coef0': 0.0, 'max_iter': -1}

训练模型SVC对训练样本的预测准确率： 0.9029187817258884

测试集中，预测为舞弊样本的有： (array([ 370,  658, 1246, 1247, 1248, 1251, 1252, 1255, 1256], dtype=int64),) 
测试集中，实际为舞弊样本的有： (array([1246, 1247, 1248, 1249, 1250, 1251, 1252, 1253, 1254, 1255, 1256],
      dtype=int64),) 
预测的舞弊样本数目： 9

训练模型SVC对测试样本的预测准确率： 0.9225888324873096
以上是第1次特征筛选。
第1次特征筛选，AUC值是： 0.8173792499635195
X_train_iter_svc.shape is: (1257, 51)
X_test_iter_svc.shape is: (1257, 51)
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.616 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6166666666666667
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6663461538461538
RBF的粗grid search最佳超参： {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0} RBF的粗grid search最高分值： 0.6166666666666667
linear的粗grid search最佳超参： {'C': 1.0, 'kernel': 'linear'} linear的粗grid search最高分值： 0.6663461538461538
粗grid search得到的parameter是：
 [{'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05]), 'kernel': ['rbf'], 'gamma': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.714 (+/-0.417) for {'C': 10.0, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 100.0, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 1000.0, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 10000.0, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      1.00       623

avg / total       0.98      0.99      0.99       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.236) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.323) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.323) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.323) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.665 (+/-0.314) for {'C': 10.0, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 100.0, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 10000.0, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 10.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.665224358974359
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.672 (+/-0.392) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.689 (+/-0.374) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.673 (+/-0.330) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.326) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.326) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.664 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.689 (+/-0.374) for {'C': 10.0, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 100.0, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 1000.0, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 10000.0, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.641 (+/-0.235) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.315) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.236) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.666 (+/-0.315) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.323) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.315) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.323) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.323) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.665 (+/-0.315) for {'C': 10.0, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 100.0, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 10000.0, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6663461538461538
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.689 (+/-0.436) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.714 (+/-0.418) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.714 (+/-0.417) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      1.00       623

avg / total       0.98      0.99      0.99       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.236) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.323) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.323) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.323) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.641 (+/-0.235) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.665 (+/-0.314) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 9.999999999999998, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.665224358974359
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.672 (+/-0.392) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.689 (+/-0.374) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.673 (+/-0.330) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.326) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.326) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.664 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.689 (+/-0.382) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.659 (+/-0.313) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.663 (+/-0.372) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.671 (+/-0.376) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.689 (+/-0.374) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.641 (+/-0.235) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.315) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.236) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.666 (+/-0.315) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.323) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.315) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.323) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.323) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.666 (+/-0.315) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.665 (+/-0.313) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.664 (+/-0.312) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.663 (+/-0.313) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.665 (+/-0.315) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 0.6309573444801931, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6663461538461538
循环迭代之前，delta is: [0.36904266]
执行tunemodel()函数前，使用的grid search parameter是：
 [{'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05]), 'kernel': ['rbf'], 'gamma': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05])}, {'C': array([0.39810717, 0.43651583, 0.47863009, 0.52480746, 0.57543994,
       0.63095734, 0.69183097, 0.75857758, 0.83176377, 0.91201084,
       1.        ]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.43651583224016594, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.47863009232263826, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.5248074602497725, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.5754399373371568, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.6918309709189364, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.7585775750291837, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.8317637711026709, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.9120108393559095, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.9999999999999997, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      1.00       623

avg / total       0.98      0.99      0.99       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.236) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.323) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.323) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.323) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.43651583224016594, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.47863009232263826, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.5248074602497725, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.5754399373371568, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.6918309709189364, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.7585775750291837, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.8317637711026709, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.9120108393559095, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.9999999999999997, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6400641025641025
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.672 (+/-0.392) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.689 (+/-0.374) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.673 (+/-0.330) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.326) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.326) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.664 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.43651583224016594, 'kernel': 'linear'}
0.797 (+/-0.492) for {'C': 0.47863009232263826, 'kernel': 'linear'}
0.772 (+/-0.473) for {'C': 0.5248074602497725, 'kernel': 'linear'}
0.747 (+/-0.449) for {'C': 0.5754399373371568, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.6918309709189364, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.7585775750291837, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.8317637711026709, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.9120108393559095, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.9999999999999997, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.641 (+/-0.235) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.315) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.236) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.666 (+/-0.315) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.323) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.315) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.323) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.323) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.43651583224016594, 'kernel': 'linear'}
0.642 (+/-0.236) for {'C': 0.47863009232263826, 'kernel': 'linear'}
0.642 (+/-0.236) for {'C': 0.5248074602497725, 'kernel': 'linear'}
0.641 (+/-0.236) for {'C': 0.5754399373371568, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.6918309709189364, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.7585775750291837, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.8317637711026709, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.9120108393559095, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.9999999999999997, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 0.6309573444801931, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6663461538461538
第0轮loop中，tunemodel函数得到的最优参数是： ([{'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05]), 'kernel': ['rbf'], 'gamma': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05])}, {'C': array([0.57543994, 0.58613816, 0.59703529, 0.608135  , 0.61944108,
       0.63095734, 0.64268772, 0.65463617, 0.66680677, 0.67920363,
       0.69183097]), 'kernel': ['linear']}], {'C': 0.6309573444801931, 'kernel': 'linear'}, 0.6663461538461538)
这是第0次迭代微调C和gamma。
第0次迭代，得到delta： [0.]
SVC模型clf_op_iter的参数是： {'kernel': 'linear', 'decision_function_shape': 'ovr', 'class_weight': {-1: 30}, 'tol': 0.001, 'gamma': 'auto', 'random_state': 0, 'cache_size': 200, 'verbose': False, 'degree': 3, 'C': 0.6309573444801931, 'probability': False, 'shrinking': True, 'coef0': 0.0, 'max_iter': -1}

训练模型SVC对训练样本的预测准确率： 0.9029187817258884

测试集中，预测为舞弊样本的有： (array([ 370,  658, 1246, 1247, 1248, 1251, 1252, 1255, 1256], dtype=int64),) 
测试集中，实际为舞弊样本的有： (array([1246, 1247, 1248, 1249, 1250, 1251, 1252, 1253, 1254, 1255, 1256],
      dtype=int64),) 
预测的舞弊样本数目： 9

训练模型SVC对测试样本的预测准确率： 0.9225888324873096
以上是第2次特征筛选。
第2次特征筛选，AUC值是： 0.8173792499635195
X_train_iter_svc.shape is: (1257, 50)
X_test_iter_svc.shape is: (1257, 50)
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.616 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6166666666666667
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6663461538461538
RBF的粗grid search最佳超参： {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0} RBF的粗grid search最高分值： 0.6166666666666667
linear的粗grid search最佳超参： {'C': 1.0, 'kernel': 'linear'} linear的粗grid search最高分值： 0.6663461538461538
粗grid search得到的parameter是：
 [{'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05]), 'kernel': ['rbf'], 'gamma': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.714 (+/-0.417) for {'C': 10.0, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 100.0, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 1000.0, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 10000.0, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      1.00       623

avg / total       0.98      0.99      0.99       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.236) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.323) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.323) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.323) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.665 (+/-0.314) for {'C': 10.0, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 100.0, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 10000.0, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 10.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.665224358974359
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.672 (+/-0.392) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.689 (+/-0.374) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.317) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.656 (+/-0.312) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.656 (+/-0.312) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.664 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.689 (+/-0.374) for {'C': 10.0, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 100.0, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 1000.0, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 10000.0, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.641 (+/-0.235) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.314) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.236) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.665 (+/-0.314) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.323) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.665 (+/-0.314) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.323) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.323) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.665 (+/-0.315) for {'C': 10.0, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 100.0, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 10000.0, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6663461538461538
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.689 (+/-0.436) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.714 (+/-0.418) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.714 (+/-0.417) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      1.00       623

avg / total       0.98      0.99      0.99       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.236) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.323) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.323) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.323) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.641 (+/-0.235) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.665 (+/-0.314) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 9.999999999999998, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.665224358974359
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.672 (+/-0.392) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.689 (+/-0.374) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.317) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.656 (+/-0.312) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.656 (+/-0.312) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.664 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.747 (+/-0.449) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.689 (+/-0.382) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.684 (+/-0.372) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.663 (+/-0.372) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.671 (+/-0.376) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.689 (+/-0.374) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.641 (+/-0.235) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.314) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.236) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.665 (+/-0.314) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.323) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.665 (+/-0.314) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.323) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.323) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.641 (+/-0.236) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.666 (+/-0.315) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.666 (+/-0.314) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.664 (+/-0.312) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.664 (+/-0.313) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.665 (+/-0.315) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 0.9999999999999997, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6663461538461538
循环迭代之前，delta is: [3.33066907e-16]
SVC模型clf_op_iter的参数是： {'kernel': 'linear', 'decision_function_shape': 'ovr', 'class_weight': {-1: 30}, 'tol': 0.001, 'gamma': 'auto', 'random_state': 0, 'cache_size': 200, 'verbose': False, 'degree': 3, 'C': 0.9999999999999997, 'probability': False, 'shrinking': True, 'coef0': 0.0, 'max_iter': -1}

训练模型SVC对训练样本的预测准确率： 0.9022842639593909

测试集中，预测为舞弊样本的有： (array([ 370, 1248, 1252, 1255, 1256], dtype=int64),) 
测试集中，实际为舞弊样本的有： (array([1246, 1247, 1248, 1249, 1250, 1251, 1252, 1253, 1254, 1255, 1256],
      dtype=int64),) 
预测的舞弊样本数目： 5

训练模型SVC对测试样本的预测准确率： 0.8661167512690355
以上是第3次特征筛选。
第3次特征筛选，AUC值是： 0.6814168977090325
X_train_iter_svc.shape is: (1257, 49)
X_test_iter_svc.shape is: (1257, 49)
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.616 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6166666666666667
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6663461538461538
RBF的粗grid search最佳超参： {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0} RBF的粗grid search最高分值： 0.6166666666666667
linear的粗grid search最佳超参： {'C': 1.0, 'kernel': 'linear'} linear的粗grid search最高分值： 0.6663461538461538
粗grid search得到的parameter是：
 [{'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05]), 'kernel': ['rbf'], 'gamma': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.714 (+/-0.417) for {'C': 10.0, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 100.0, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 1000.0, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 10000.0, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      1.00       623

avg / total       0.98      0.99      0.99       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.236) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.323) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.323) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.323) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.665 (+/-0.314) for {'C': 10.0, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 100.0, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 10000.0, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 10.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.665224358974359
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.672 (+/-0.392) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.689 (+/-0.374) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.317) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.656 (+/-0.312) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.656 (+/-0.312) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.664 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.689 (+/-0.374) for {'C': 10.0, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 100.0, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 1000.0, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 10000.0, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.641 (+/-0.235) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.314) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.236) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.665 (+/-0.314) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.323) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.665 (+/-0.314) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.323) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.323) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.665 (+/-0.315) for {'C': 10.0, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 100.0, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 10000.0, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6663461538461538
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.689 (+/-0.436) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.714 (+/-0.418) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.714 (+/-0.417) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      1.00       623

avg / total       0.98      0.99      0.99       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.236) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.323) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.323) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.323) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.641 (+/-0.235) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.665 (+/-0.314) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 9.999999999999998, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.665224358974359
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.672 (+/-0.392) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.689 (+/-0.374) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.317) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.656 (+/-0.312) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.656 (+/-0.312) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.664 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.747 (+/-0.449) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.689 (+/-0.382) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.684 (+/-0.372) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.663 (+/-0.372) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.671 (+/-0.376) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.689 (+/-0.374) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.641 (+/-0.235) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.314) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.236) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.665 (+/-0.314) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.323) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.665 (+/-0.314) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.323) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.323) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.641 (+/-0.236) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.666 (+/-0.315) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.666 (+/-0.314) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.664 (+/-0.312) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.664 (+/-0.313) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.665 (+/-0.315) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 0.9999999999999997, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6663461538461538
循环迭代之前，delta is: [3.33066907e-16]
SVC模型clf_op_iter的参数是： {'kernel': 'linear', 'decision_function_shape': 'ovr', 'class_weight': {-1: 30}, 'tol': 0.001, 'gamma': 'auto', 'random_state': 0, 'cache_size': 200, 'verbose': False, 'degree': 3, 'C': 0.9999999999999997, 'probability': False, 'shrinking': True, 'coef0': 0.0, 'max_iter': -1}

训练模型SVC对训练样本的预测准确率： 0.9022842639593909

测试集中，预测为舞弊样本的有： (array([ 370, 1248, 1252, 1255, 1256], dtype=int64),) 
测试集中，实际为舞弊样本的有： (array([1246, 1247, 1248, 1249, 1250, 1251, 1252, 1253, 1254, 1255, 1256],
      dtype=int64),) 
预测的舞弊样本数目： 5

训练模型SVC对测试样本的预测准确率： 0.8661167512690355
以上是第4次特征筛选。
第4次特征筛选，AUC值是： 0.6814168977090325
X_train_iter_svc.shape is: (1257, 48)
X_test_iter_svc.shape is: (1257, 48)
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.616 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6166666666666667
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6663461538461538
RBF的粗grid search最佳超参： {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0} RBF的粗grid search最高分值： 0.6166666666666667
linear的粗grid search最佳超参： {'C': 1.0, 'kernel': 'linear'} linear的粗grid search最高分值： 0.6663461538461538
粗grid search得到的parameter是：
 [{'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05]), 'kernel': ['rbf'], 'gamma': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.714 (+/-0.417) for {'C': 10.0, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 100.0, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 1000.0, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 10000.0, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      1.00       623

avg / total       0.98      0.99      0.99       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.236) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.323) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.323) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.323) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.665 (+/-0.314) for {'C': 10.0, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 100.0, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 10000.0, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 10.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.665224358974359
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.672 (+/-0.392) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.689 (+/-0.374) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.317) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.656 (+/-0.312) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.656 (+/-0.312) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.656 (+/-0.312) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.689 (+/-0.374) for {'C': 10.0, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 100.0, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 1000.0, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 10000.0, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.641 (+/-0.235) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.314) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.236) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.665 (+/-0.314) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.323) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.665 (+/-0.314) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.323) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.665 (+/-0.314) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.323) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.665 (+/-0.315) for {'C': 10.0, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 100.0, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 10000.0, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6663461538461538
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.689 (+/-0.436) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.714 (+/-0.418) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.714 (+/-0.417) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      1.00       623

avg / total       0.98      0.99      0.99       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.236) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.323) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.323) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.323) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.641 (+/-0.235) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.665 (+/-0.314) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 9.999999999999998, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.665224358974359
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.672 (+/-0.392) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.689 (+/-0.374) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.317) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.656 (+/-0.312) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.656 (+/-0.312) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.656 (+/-0.312) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.689 (+/-0.382) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.684 (+/-0.372) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.663 (+/-0.372) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.671 (+/-0.376) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.689 (+/-0.374) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.641 (+/-0.235) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.314) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.236) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.665 (+/-0.314) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.323) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.665 (+/-0.314) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.323) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.665 (+/-0.314) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.323) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.666 (+/-0.315) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.666 (+/-0.314) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.664 (+/-0.312) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.664 (+/-0.313) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.665 (+/-0.315) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 0.6309573444801931, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6663461538461538
循环迭代之前，delta is: [0.36904266]
执行tunemodel()函数前，使用的grid search parameter是：
 [{'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05]), 'kernel': ['rbf'], 'gamma': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05])}, {'C': array([0.39810717, 0.43651583, 0.47863009, 0.52480746, 0.57543994,
       0.63095734, 0.69183097, 0.75857758, 0.83176377, 0.91201084,
       1.        ]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.43651583224016594, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.47863009232263826, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.5248074602497725, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.5754399373371568, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.6918309709189364, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.7585775750291837, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.8317637711026709, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.9120108393559095, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.9999999999999997, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      1.00       623

avg / total       0.98      0.99      0.99       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.236) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.323) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.323) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.323) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.43651583224016594, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.47863009232263826, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.5248074602497725, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.5754399373371568, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.6918309709189364, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.7585775750291837, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.8317637711026709, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.9120108393559095, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.9999999999999997, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6400641025641025
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.672 (+/-0.392) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.689 (+/-0.374) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.317) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.656 (+/-0.312) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.656 (+/-0.312) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.656 (+/-0.312) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.43651583224016594, 'kernel': 'linear'}
0.797 (+/-0.492) for {'C': 0.47863009232263826, 'kernel': 'linear'}
0.772 (+/-0.473) for {'C': 0.5248074602497725, 'kernel': 'linear'}
0.747 (+/-0.449) for {'C': 0.5754399373371568, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.6918309709189364, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.7585775750291837, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.8317637711026709, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.9120108393559095, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.9999999999999997, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.641 (+/-0.235) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.314) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.236) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.665 (+/-0.314) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.323) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.665 (+/-0.314) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.323) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.665 (+/-0.314) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.323) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.43651583224016594, 'kernel': 'linear'}
0.642 (+/-0.236) for {'C': 0.47863009232263826, 'kernel': 'linear'}
0.642 (+/-0.236) for {'C': 0.5248074602497725, 'kernel': 'linear'}
0.641 (+/-0.236) for {'C': 0.5754399373371568, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.6918309709189364, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.7585775750291837, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.8317637711026709, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.9120108393559095, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.9999999999999997, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 0.6309573444801931, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6663461538461538
第0轮loop中，tunemodel函数得到的最优参数是： ([{'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05]), 'kernel': ['rbf'], 'gamma': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05])}, {'C': array([0.57543994, 0.58613816, 0.59703529, 0.608135  , 0.61944108,
       0.63095734, 0.64268772, 0.65463617, 0.66680677, 0.67920363,
       0.69183097]), 'kernel': ['linear']}], {'C': 0.6309573444801931, 'kernel': 'linear'}, 0.6663461538461538)
这是第0次迭代微调C和gamma。
第0次迭代，得到delta： [0.]
SVC模型clf_op_iter的参数是： {'kernel': 'linear', 'decision_function_shape': 'ovr', 'class_weight': {-1: 30}, 'tol': 0.001, 'gamma': 'auto', 'random_state': 0, 'cache_size': 200, 'verbose': False, 'degree': 3, 'C': 0.6309573444801931, 'probability': False, 'shrinking': True, 'coef0': 0.0, 'max_iter': -1}

训练模型SVC对训练样本的预测准确率： 0.9029187817258884

测试集中，预测为舞弊样本的有： (array([ 370,  658, 1246, 1247, 1248, 1251, 1252, 1255, 1256], dtype=int64),) 
测试集中，实际为舞弊样本的有： (array([1246, 1247, 1248, 1249, 1250, 1251, 1252, 1253, 1254, 1255, 1256],
      dtype=int64),) 
预测的舞弊样本数目： 9

训练模型SVC对测试样本的预测准确率： 0.9225888324873096
以上是第5次特征筛选。
第5次特征筛选，AUC值是： 0.8173792499635195
X_train_iter_svc.shape is: (1257, 47)
X_test_iter_svc.shape is: (1257, 47)
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.616 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6166666666666667
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6663461538461538
RBF的粗grid search最佳超参： {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0} RBF的粗grid search最高分值： 0.6166666666666667
linear的粗grid search最佳超参： {'C': 1.0, 'kernel': 'linear'} linear的粗grid search最高分值： 0.6663461538461538
粗grid search得到的parameter是：
 [{'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05]), 'kernel': ['rbf'], 'gamma': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.714 (+/-0.417) for {'C': 10.0, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 100.0, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 1000.0, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 10000.0, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      1.00       623

avg / total       0.98      0.99      0.99       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.323) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.323) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.324) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.665 (+/-0.314) for {'C': 10.0, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 100.0, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 10000.0, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.14      0.17      0.15         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 10.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.665224358974359
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.672 (+/-0.392) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.689 (+/-0.374) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.681 (+/-0.372) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.681 (+/-0.372) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.681 (+/-0.372) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.689 (+/-0.382) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.687 (+/-0.375) for {'C': 10.0, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 100.0, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 1000.0, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 10000.0, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.641 (+/-0.235) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.314) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.666 (+/-0.314) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.323) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.314) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.323) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.324) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.664 (+/-0.314) for {'C': 10.0, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 100.0, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 10000.0, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6663461538461538
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.689 (+/-0.436) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.714 (+/-0.418) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.714 (+/-0.417) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      1.00       623

avg / total       0.98      0.99      0.99       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.323) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.323) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.324) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.641 (+/-0.235) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.665 (+/-0.314) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.14      0.17      0.15         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 9.999999999999998, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.665224358974359
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.672 (+/-0.392) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.689 (+/-0.374) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.681 (+/-0.372) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.681 (+/-0.372) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.681 (+/-0.372) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.689 (+/-0.382) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.689 (+/-0.382) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.684 (+/-0.372) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.662 (+/-0.373) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.671 (+/-0.376) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.687 (+/-0.375) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.641 (+/-0.235) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.314) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.666 (+/-0.314) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.323) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.314) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.323) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.324) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.666 (+/-0.315) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.666 (+/-0.314) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.664 (+/-0.312) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.663 (+/-0.313) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.664 (+/-0.314) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 0.6309573444801931, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6663461538461538
循环迭代之前，delta is: [0.36904266]
执行tunemodel()函数前，使用的grid search parameter是：
 [{'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05]), 'kernel': ['rbf'], 'gamma': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05])}, {'C': array([0.39810717, 0.43651583, 0.47863009, 0.52480746, 0.57543994,
       0.63095734, 0.69183097, 0.75857758, 0.83176377, 0.91201084,
       1.        ]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.43651583224016594, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.47863009232263826, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.5248074602497725, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.5754399373371568, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.6918309709189364, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.7585775750291837, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.8317637711026709, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.9120108393559095, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.9999999999999997, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      1.00       623

avg / total       0.98      0.99      0.99       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.323) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.323) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.324) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.43651583224016594, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.47863009232263826, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.5248074602497725, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.5754399373371568, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.6918309709189364, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.7585775750291837, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.8317637711026709, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.9120108393559095, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.9999999999999997, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6400641025641025
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.672 (+/-0.392) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.689 (+/-0.374) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.681 (+/-0.372) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.681 (+/-0.372) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.681 (+/-0.372) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.689 (+/-0.382) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.43651583224016594, 'kernel': 'linear'}
0.797 (+/-0.492) for {'C': 0.47863009232263826, 'kernel': 'linear'}
0.772 (+/-0.473) for {'C': 0.5248074602497725, 'kernel': 'linear'}
0.747 (+/-0.449) for {'C': 0.5754399373371568, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.6918309709189364, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.7585775750291837, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.8317637711026709, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.9120108393559095, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.9999999999999997, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.641 (+/-0.235) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.314) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.666 (+/-0.314) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.323) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.314) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.323) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.324) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.43651583224016594, 'kernel': 'linear'}
0.642 (+/-0.236) for {'C': 0.47863009232263826, 'kernel': 'linear'}
0.642 (+/-0.236) for {'C': 0.5248074602497725, 'kernel': 'linear'}
0.641 (+/-0.236) for {'C': 0.5754399373371568, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.6918309709189364, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.7585775750291837, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.8317637711026709, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.9120108393559095, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.9999999999999997, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 0.6309573444801931, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6663461538461538
第0轮loop中，tunemodel函数得到的最优参数是： ([{'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05]), 'kernel': ['rbf'], 'gamma': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05])}, {'C': array([0.57543994, 0.58613816, 0.59703529, 0.608135  , 0.61944108,
       0.63095734, 0.64268772, 0.65463617, 0.66680677, 0.67920363,
       0.69183097]), 'kernel': ['linear']}], {'C': 0.6309573444801931, 'kernel': 'linear'}, 0.6663461538461538)
这是第0次迭代微调C和gamma。
第0次迭代，得到delta： [0.]
SVC模型clf_op_iter的参数是： {'kernel': 'linear', 'decision_function_shape': 'ovr', 'class_weight': {-1: 30}, 'tol': 0.001, 'gamma': 'auto', 'random_state': 0, 'cache_size': 200, 'verbose': False, 'degree': 3, 'C': 0.6309573444801931, 'probability': False, 'shrinking': True, 'coef0': 0.0, 'max_iter': -1}

训练模型SVC对训练样本的预测准确率： 0.9029187817258884

测试集中，预测为舞弊样本的有： (array([ 370,  658, 1246, 1247, 1248, 1251, 1252, 1255, 1256], dtype=int64),) 
测试集中，实际为舞弊样本的有： (array([1246, 1247, 1248, 1249, 1250, 1251, 1252, 1253, 1254, 1255, 1256],
      dtype=int64),) 
预测的舞弊样本数目： 9

训练模型SVC对测试样本的预测准确率： 0.9225888324873096
以上是第6次特征筛选。
第6次特征筛选，AUC值是： 0.8173792499635195
X_train_iter_svc.shape is: (1257, 46)
X_test_iter_svc.shape is: (1257, 46)
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.722 (+/-0.473) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.616 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6166666666666667
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.723 (+/-0.417) for {'C': 1.0, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.635 (+/-0.326) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.615 (+/-0.237) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6661858974358974
RBF的粗grid search最佳超参： {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0} RBF的粗grid search最高分值： 0.6166666666666667
linear的粗grid search最佳超参： {'C': 1.0, 'kernel': 'linear'} linear的粗grid search最高分值： 0.6661858974358974
粗grid search得到的parameter是：
 [{'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05]), 'kernel': ['rbf'], 'gamma': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.662 (+/-0.389) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.662 (+/-0.389) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.626 (+/-0.318) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.635 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.626 (+/-0.318) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.714 (+/-0.417) for {'C': 10.0, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 100.0, 'kernel': 'linear'}
0.635 (+/-0.326) for {'C': 1000.0, 'kernel': 'linear'}
0.635 (+/-0.326) for {'C': 10000.0, 'kernel': 'linear'}
0.635 (+/-0.326) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      1.00       623

avg / total       0.98      0.99      0.99       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.323) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.323) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.323) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.665 (+/-0.315) for {'C': 10.0, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 100.0, 'kernel': 'linear'}
0.615 (+/-0.237) for {'C': 1000.0, 'kernel': 'linear'}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'linear'}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.14      0.17      0.15         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 10.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6650641025641026
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.672 (+/-0.392) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.698 (+/-0.383) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.681 (+/-0.372) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.681 (+/-0.372) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.662 (+/-0.389) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.681 (+/-0.372) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.662 (+/-0.389) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.626 (+/-0.318) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.698 (+/-0.383) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.635 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.626 (+/-0.318) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.723 (+/-0.417) for {'C': 1.0, 'kernel': 'linear'}
0.679 (+/-0.373) for {'C': 10.0, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 100.0, 'kernel': 'linear'}
0.635 (+/-0.326) for {'C': 1000.0, 'kernel': 'linear'}
0.635 (+/-0.326) for {'C': 10000.0, 'kernel': 'linear'}
0.635 (+/-0.326) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      1.00       623

avg / total       0.98      0.99      0.99       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.666 (+/-0.315) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.314) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.666 (+/-0.314) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.323) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.314) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.323) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.323) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.664 (+/-0.314) for {'C': 10.0, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 100.0, 'kernel': 'linear'}
0.615 (+/-0.237) for {'C': 1000.0, 'kernel': 'linear'}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'linear'}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6661858974358974
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.662 (+/-0.389) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.662 (+/-0.389) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.626 (+/-0.318) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.635 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.626 (+/-0.318) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.689 (+/-0.436) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.714 (+/-0.418) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.714 (+/-0.417) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      1.00       623

avg / total       0.98      0.99      0.99       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.323) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.323) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.323) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.641 (+/-0.235) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.665 (+/-0.315) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.14      0.17      0.15         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 9.999999999999998, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6650641025641026
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.672 (+/-0.392) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.698 (+/-0.383) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.681 (+/-0.372) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.681 (+/-0.372) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.662 (+/-0.389) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.681 (+/-0.372) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.662 (+/-0.389) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.626 (+/-0.318) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.698 (+/-0.383) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.635 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.626 (+/-0.318) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.797 (+/-0.492) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.723 (+/-0.417) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.689 (+/-0.382) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.684 (+/-0.372) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.668 (+/-0.371) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.670 (+/-0.377) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.679 (+/-0.373) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.666 (+/-0.315) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.314) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.666 (+/-0.314) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.323) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.314) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.323) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.323) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.642 (+/-0.236) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.666 (+/-0.315) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.666 (+/-0.314) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.664 (+/-0.313) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.663 (+/-0.314) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.664 (+/-0.314) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 0.6309573444801931, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6663461538461538
循环迭代之前，delta is: [0.36904266]
执行tunemodel()函数前，使用的grid search parameter是：
 [{'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05]), 'kernel': ['rbf'], 'gamma': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05])}, {'C': array([0.39810717, 0.43651583, 0.47863009, 0.52480746, 0.57543994,
       0.63095734, 0.69183097, 0.75857758, 0.83176377, 0.91201084,
       1.        ]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.662 (+/-0.389) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.662 (+/-0.389) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.626 (+/-0.318) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.635 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.626 (+/-0.318) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.43651583224016594, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.47863009232263826, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.5248074602497725, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.5754399373371568, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.6918309709189364, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.7585775750291837, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.8317637711026709, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.9120108393559095, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.9999999999999997, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      1.00       623

avg / total       0.98      0.99      0.99       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.323) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.323) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.323) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.43651583224016594, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.47863009232263826, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.5248074602497725, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.5754399373371568, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.6918309709189364, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.7585775750291837, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.8317637711026709, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.9120108393559095, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.9999999999999997, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6400641025641025
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.672 (+/-0.392) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.698 (+/-0.383) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.681 (+/-0.372) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.681 (+/-0.372) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.662 (+/-0.389) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.681 (+/-0.372) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.662 (+/-0.389) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.626 (+/-0.318) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.698 (+/-0.383) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.635 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.626 (+/-0.318) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.797 (+/-0.492) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.797 (+/-0.492) for {'C': 0.43651583224016594, 'kernel': 'linear'}
0.797 (+/-0.492) for {'C': 0.47863009232263826, 'kernel': 'linear'}
0.772 (+/-0.473) for {'C': 0.5248074602497725, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.5754399373371568, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.6918309709189364, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.7585775750291837, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.8317637711026709, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.9120108393559095, 'kernel': 'linear'}
0.723 (+/-0.417) for {'C': 0.9999999999999997, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.666 (+/-0.315) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.314) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.666 (+/-0.314) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.323) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.314) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.323) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.323) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.642 (+/-0.236) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.642 (+/-0.236) for {'C': 0.43651583224016594, 'kernel': 'linear'}
0.642 (+/-0.236) for {'C': 0.47863009232263826, 'kernel': 'linear'}
0.642 (+/-0.236) for {'C': 0.5248074602497725, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.5754399373371568, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.6918309709189364, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.7585775750291837, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.8317637711026709, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.9120108393559095, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.9999999999999997, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 0.5754399373371568, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6663461538461538
第0轮loop中，tunemodel函数得到的最优参数是： ([{'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05]), 'kernel': ['rbf'], 'gamma': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05])}, {'C': array([0.52480746, 0.53456436, 0.54450265, 0.55462571, 0.56493697,
       0.57543994, 0.58613816, 0.59703529, 0.608135  , 0.61944108,
       0.63095734]), 'kernel': ['linear']}], {'C': 0.5754399373371568, 'kernel': 'linear'}, 0.6663461538461538)
这是第0次迭代微调C和gamma。
第0次迭代，得到delta： [0.05551741]
执行tunemodel()函数前，使用的grid search parameter是：
 [{'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05]), 'kernel': ['rbf'], 'gamma': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05])}, {'C': array([0.52480746, 0.53456436, 0.54450265, 0.55462571, 0.56493697,
       0.57543994, 0.58613816, 0.59703529, 0.608135  , 0.61944108,
       0.63095734]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.662 (+/-0.389) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.662 (+/-0.389) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.626 (+/-0.318) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.635 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.626 (+/-0.318) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.5248074602497725, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.5345643593969717, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.5445026528424212, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.5546257129579107, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.5649369748123024, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.5754399373371568, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.5861381645140287, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.5970352865838368, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.6081350012787178, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.6194410750767814, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.6309573444801931, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      1.00       623

avg / total       0.98      0.99      0.99       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.323) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.323) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.323) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.5248074602497725, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.5345643593969717, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.5445026528424212, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.5546257129579107, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.5649369748123024, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.5754399373371568, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.5861381645140287, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.5970352865838368, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.6081350012787178, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.6194410750767814, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.6309573444801931, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6400641025641025
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.672 (+/-0.392) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.698 (+/-0.383) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.681 (+/-0.372) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.681 (+/-0.372) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.662 (+/-0.389) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.681 (+/-0.372) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.662 (+/-0.389) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.626 (+/-0.318) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.698 (+/-0.383) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.635 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.626 (+/-0.318) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.772 (+/-0.473) for {'C': 0.5248074602497725, 'kernel': 'linear'}
0.773 (+/-0.474) for {'C': 0.5345643593969717, 'kernel': 'linear'}
0.773 (+/-0.474) for {'C': 0.5445026528424212, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.5546257129579107, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.5649369748123024, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.5754399373371568, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.5861381645140287, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.5970352865838368, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.6081350012787178, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.6194410750767814, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.6309573444801931, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.666 (+/-0.315) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.314) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.666 (+/-0.314) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.323) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.314) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.323) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.323) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.642 (+/-0.236) for {'C': 0.5248074602497725, 'kernel': 'linear'}
0.667 (+/-0.316) for {'C': 0.5345643593969717, 'kernel': 'linear'}
0.667 (+/-0.316) for {'C': 0.5445026528424212, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.5546257129579107, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.5649369748123024, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.5754399373371568, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.5861381645140287, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.5970352865838368, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.6081350012787178, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.6194410750767814, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.6309573444801931, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 0.5345643593969717, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6665064102564102
第1轮loop中，tunemodel函数得到的最优参数是： ([{'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05]), 'kernel': ['rbf'], 'gamma': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05])}, {'C': array([0.52480746, 0.52674449, 0.52868867, 0.53064002, 0.53259857,
       0.53456436, 0.5365374 , 0.53851772, 0.54050535, 0.54250032,
       0.54450265]), 'kernel': ['linear']}], {'C': 0.5345643593969717, 'kernel': 'linear'}, 0.6665064102564102)
这是第1次迭代微调C和gamma。
第1次迭代，得到delta： [0.04087558]
执行tunemodel()函数前，使用的grid search parameter是：
 [{'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05]), 'kernel': ['rbf'], 'gamma': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05])}, {'C': array([0.52480746, 0.52674449, 0.52868867, 0.53064002, 0.53259857,
       0.53456436, 0.5365374 , 0.53851772, 0.54050535, 0.54250032,
       0.54450265]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.662 (+/-0.389) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.662 (+/-0.389) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.626 (+/-0.318) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.635 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.626 (+/-0.318) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.5248074602497725, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.526744488331906, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.5286886658508809, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.5306400191947748, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.5325985748490616, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.5345643593969717, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.5365373995198517, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.538517721997528, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.5405053537086689, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.5425003216311501, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.5445026528424212, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      1.00       623

avg / total       0.98      0.99      0.99       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.323) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.323) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.323) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.5248074602497725, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.526744488331906, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.5286886658508809, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.5306400191947748, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.5325985748490616, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.5345643593969717, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.5365373995198517, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.538517721997528, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.5405053537086689, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.5425003216311501, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.5445026528424212, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6400641025641025
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.672 (+/-0.392) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.698 (+/-0.383) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.681 (+/-0.372) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.681 (+/-0.372) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.662 (+/-0.389) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.681 (+/-0.372) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.662 (+/-0.389) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.626 (+/-0.318) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.698 (+/-0.383) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.635 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.626 (+/-0.318) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.772 (+/-0.473) for {'C': 0.5248074602497725, 'kernel': 'linear'}
0.772 (+/-0.473) for {'C': 0.526744488331906, 'kernel': 'linear'}
0.772 (+/-0.473) for {'C': 0.5286886658508809, 'kernel': 'linear'}
0.773 (+/-0.474) for {'C': 0.5306400191947748, 'kernel': 'linear'}
0.773 (+/-0.474) for {'C': 0.5325985748490616, 'kernel': 'linear'}
0.773 (+/-0.474) for {'C': 0.5345643593969717, 'kernel': 'linear'}
0.773 (+/-0.474) for {'C': 0.5365373995198517, 'kernel': 'linear'}
0.773 (+/-0.474) for {'C': 0.538517721997528, 'kernel': 'linear'}
0.773 (+/-0.474) for {'C': 0.5405053537086689, 'kernel': 'linear'}
0.773 (+/-0.474) for {'C': 0.5425003216311501, 'kernel': 'linear'}
0.773 (+/-0.474) for {'C': 0.5445026528424212, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.666 (+/-0.315) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.314) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.666 (+/-0.314) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.323) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.314) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.323) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.323) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.642 (+/-0.236) for {'C': 0.5248074602497725, 'kernel': 'linear'}
0.642 (+/-0.236) for {'C': 0.526744488331906, 'kernel': 'linear'}
0.642 (+/-0.236) for {'C': 0.5286886658508809, 'kernel': 'linear'}
0.667 (+/-0.316) for {'C': 0.5306400191947748, 'kernel': 'linear'}
0.667 (+/-0.316) for {'C': 0.5325985748490616, 'kernel': 'linear'}
0.667 (+/-0.316) for {'C': 0.5345643593969717, 'kernel': 'linear'}
0.667 (+/-0.316) for {'C': 0.5365373995198517, 'kernel': 'linear'}
0.667 (+/-0.316) for {'C': 0.538517721997528, 'kernel': 'linear'}
0.667 (+/-0.316) for {'C': 0.5405053537086689, 'kernel': 'linear'}
0.667 (+/-0.316) for {'C': 0.5425003216311501, 'kernel': 'linear'}
0.667 (+/-0.316) for {'C': 0.5445026528424212, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 0.5306400191947748, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6665064102564102
第2轮loop中，tunemodel函数得到的最优参数是： ([{'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05]), 'kernel': ['rbf'], 'gamma': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05])}, {'C': array([0.52868867, 0.52907836, 0.52946834, 0.52985862, 0.53024917,
       0.53064002, 0.53103115, 0.53142258, 0.53181429, 0.53220629,
       0.53259857]), 'kernel': ['linear']}], {'C': 0.5306400191947748, 'kernel': 'linear'}, 0.6665064102564102)
这是第2次迭代微调C和gamma。
第2次迭代，得到delta： [0.00392434]
SVC模型clf_op_iter的参数是： {'kernel': 'linear', 'decision_function_shape': 'ovr', 'class_weight': {-1: 30}, 'tol': 0.001, 'gamma': 'auto', 'random_state': 0, 'cache_size': 200, 'verbose': False, 'degree': 3, 'C': 0.5306400191947748, 'probability': False, 'shrinking': True, 'coef0': 0.0, 'max_iter': -1}

训练模型SVC对训练样本的预测准确率： 0.9029187817258884

测试集中，预测为舞弊样本的有： (array([ 370,  658,  769, 1246, 1247, 1248, 1251, 1252, 1255, 1256],
      dtype=int64),) 
测试集中，实际为舞弊样本的有： (array([1246, 1247, 1248, 1249, 1250, 1251, 1252, 1253, 1254, 1255, 1256],
      dtype=int64),) 
预测的舞弊样本数目： 10

训练模型SVC对测试样本的预测准确率： 0.9219543147208121
以上是第7次特征筛选。
第7次特征筛选，AUC值是： 0.8169779658543703
X_train_iter_svc.shape is: (1257, 45)
X_test_iter_svc.shape is: (1257, 45)
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.722 (+/-0.473) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.616 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6166666666666667
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.723 (+/-0.417) for {'C': 1.0, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.635 (+/-0.326) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.615 (+/-0.237) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6661858974358974
RBF的粗grid search最佳超参： {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0} RBF的粗grid search最高分值： 0.6166666666666667
linear的粗grid search最佳超参： {'C': 1.0, 'kernel': 'linear'} linear的粗grid search最高分值： 0.6661858974358974
粗grid search得到的parameter是：
 [{'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05]), 'kernel': ['rbf'], 'gamma': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.662 (+/-0.389) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.662 (+/-0.389) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.626 (+/-0.318) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.635 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.626 (+/-0.318) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.714 (+/-0.417) for {'C': 10.0, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 100.0, 'kernel': 'linear'}
0.635 (+/-0.326) for {'C': 1000.0, 'kernel': 'linear'}
0.635 (+/-0.326) for {'C': 10000.0, 'kernel': 'linear'}
0.635 (+/-0.326) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      1.00       623

avg / total       0.98      0.99      0.99       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.323) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.323) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.323) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.665 (+/-0.315) for {'C': 10.0, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 100.0, 'kernel': 'linear'}
0.615 (+/-0.237) for {'C': 1000.0, 'kernel': 'linear'}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'linear'}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.14      0.17      0.15         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 10.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6650641025641026
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.672 (+/-0.392) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.698 (+/-0.383) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.681 (+/-0.372) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.681 (+/-0.372) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.662 (+/-0.389) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.681 (+/-0.372) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.662 (+/-0.389) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.626 (+/-0.318) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.698 (+/-0.383) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.635 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.626 (+/-0.318) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.723 (+/-0.417) for {'C': 1.0, 'kernel': 'linear'}
0.679 (+/-0.373) for {'C': 10.0, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 100.0, 'kernel': 'linear'}
0.635 (+/-0.326) for {'C': 1000.0, 'kernel': 'linear'}
0.635 (+/-0.326) for {'C': 10000.0, 'kernel': 'linear'}
0.635 (+/-0.326) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      1.00       623

avg / total       0.98      0.99      0.99       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.666 (+/-0.315) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.314) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.666 (+/-0.314) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.323) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.314) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.323) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.323) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.664 (+/-0.314) for {'C': 10.0, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 100.0, 'kernel': 'linear'}
0.615 (+/-0.237) for {'C': 1000.0, 'kernel': 'linear'}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'linear'}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6661858974358974
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.662 (+/-0.389) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.662 (+/-0.389) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.626 (+/-0.318) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.635 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.626 (+/-0.318) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.689 (+/-0.436) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.714 (+/-0.418) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.714 (+/-0.417) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      1.00       623

avg / total       0.98      0.99      0.99       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.323) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.323) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.323) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.641 (+/-0.235) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.665 (+/-0.315) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.14      0.17      0.15         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 9.999999999999998, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6650641025641026
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.672 (+/-0.392) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.698 (+/-0.383) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.681 (+/-0.372) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.681 (+/-0.372) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.662 (+/-0.389) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.681 (+/-0.372) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.662 (+/-0.389) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.626 (+/-0.318) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.698 (+/-0.383) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.635 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.626 (+/-0.318) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.797 (+/-0.492) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.723 (+/-0.417) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.689 (+/-0.382) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.684 (+/-0.372) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.668 (+/-0.371) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.670 (+/-0.377) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.679 (+/-0.373) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.666 (+/-0.315) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.314) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.666 (+/-0.314) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.323) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.314) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.323) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.323) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.642 (+/-0.236) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.666 (+/-0.315) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.666 (+/-0.314) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.664 (+/-0.313) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.663 (+/-0.314) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.664 (+/-0.314) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 0.6309573444801931, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6663461538461538
循环迭代之前，delta is: [0.36904266]
执行tunemodel()函数前，使用的grid search parameter是：
 [{'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05]), 'kernel': ['rbf'], 'gamma': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05])}, {'C': array([0.39810717, 0.43651583, 0.47863009, 0.52480746, 0.57543994,
       0.63095734, 0.69183097, 0.75857758, 0.83176377, 0.91201084,
       1.        ]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.662 (+/-0.389) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.662 (+/-0.389) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.626 (+/-0.318) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.635 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.626 (+/-0.318) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.43651583224016594, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.47863009232263826, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.5248074602497725, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.5754399373371568, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.6918309709189364, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.7585775750291837, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.8317637711026709, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.9120108393559095, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.9999999999999997, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      1.00       623

avg / total       0.98      0.99      0.99       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.323) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.323) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.323) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.43651583224016594, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.47863009232263826, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.5248074602497725, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.5754399373371568, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.6918309709189364, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.7585775750291837, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.8317637711026709, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.9120108393559095, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.9999999999999997, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6400641025641025
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.672 (+/-0.392) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.698 (+/-0.383) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.681 (+/-0.372) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.681 (+/-0.372) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.662 (+/-0.389) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.681 (+/-0.372) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.662 (+/-0.389) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.626 (+/-0.318) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.698 (+/-0.383) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.635 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.626 (+/-0.318) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.797 (+/-0.492) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.797 (+/-0.492) for {'C': 0.43651583224016594, 'kernel': 'linear'}
0.797 (+/-0.492) for {'C': 0.47863009232263826, 'kernel': 'linear'}
0.772 (+/-0.473) for {'C': 0.5248074602497725, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.5754399373371568, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.6918309709189364, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.7585775750291837, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.8317637711026709, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.9120108393559095, 'kernel': 'linear'}
0.723 (+/-0.417) for {'C': 0.9999999999999997, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.666 (+/-0.315) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.314) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.666 (+/-0.314) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.323) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.314) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.323) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.323) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.642 (+/-0.236) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.642 (+/-0.236) for {'C': 0.43651583224016594, 'kernel': 'linear'}
0.642 (+/-0.236) for {'C': 0.47863009232263826, 'kernel': 'linear'}
0.642 (+/-0.236) for {'C': 0.5248074602497725, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.5754399373371568, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.6918309709189364, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.7585775750291837, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.8317637711026709, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.9120108393559095, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.9999999999999997, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 0.5754399373371568, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6663461538461538
第0轮loop中，tunemodel函数得到的最优参数是： ([{'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05]), 'kernel': ['rbf'], 'gamma': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05])}, {'C': array([0.52480746, 0.53456436, 0.54450265, 0.55462571, 0.56493697,
       0.57543994, 0.58613816, 0.59703529, 0.608135  , 0.61944108,
       0.63095734]), 'kernel': ['linear']}], {'C': 0.5754399373371568, 'kernel': 'linear'}, 0.6663461538461538)
这是第0次迭代微调C和gamma。
第0次迭代，得到delta： [0.05551741]
执行tunemodel()函数前，使用的grid search parameter是：
 [{'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05]), 'kernel': ['rbf'], 'gamma': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05])}, {'C': array([0.52480746, 0.53456436, 0.54450265, 0.55462571, 0.56493697,
       0.57543994, 0.58613816, 0.59703529, 0.608135  , 0.61944108,
       0.63095734]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.662 (+/-0.389) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.662 (+/-0.389) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.626 (+/-0.318) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.635 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.626 (+/-0.318) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.5248074602497725, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.5345643593969717, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.5445026528424212, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.5546257129579107, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.5649369748123024, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.5754399373371568, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.5861381645140287, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.5970352865838368, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.6081350012787178, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.6194410750767814, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.6309573444801931, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      1.00       623

avg / total       0.98      0.99      0.99       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.323) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.323) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.323) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.5248074602497725, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.5345643593969717, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.5445026528424212, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.5546257129579107, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.5649369748123024, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.5754399373371568, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.5861381645140287, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.5970352865838368, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.6081350012787178, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.6194410750767814, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.6309573444801931, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6400641025641025
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.672 (+/-0.392) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.698 (+/-0.383) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.681 (+/-0.372) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.681 (+/-0.372) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.662 (+/-0.389) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.681 (+/-0.372) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.662 (+/-0.389) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.626 (+/-0.318) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.698 (+/-0.383) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.635 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.626 (+/-0.318) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.772 (+/-0.473) for {'C': 0.5248074602497725, 'kernel': 'linear'}
0.773 (+/-0.474) for {'C': 0.5345643593969717, 'kernel': 'linear'}
0.773 (+/-0.474) for {'C': 0.5445026528424212, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.5546257129579107, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.5649369748123024, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.5754399373371568, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.5861381645140287, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.5970352865838368, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.6081350012787178, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.6194410750767814, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.6309573444801931, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.666 (+/-0.315) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.314) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.666 (+/-0.314) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.323) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.314) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.323) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.323) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.642 (+/-0.236) for {'C': 0.5248074602497725, 'kernel': 'linear'}
0.667 (+/-0.316) for {'C': 0.5345643593969717, 'kernel': 'linear'}
0.667 (+/-0.316) for {'C': 0.5445026528424212, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.5546257129579107, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.5649369748123024, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.5754399373371568, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.5861381645140287, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.5970352865838368, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.6081350012787178, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.6194410750767814, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.6309573444801931, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 0.5345643593969717, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6665064102564102
第1轮loop中，tunemodel函数得到的最优参数是： ([{'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05]), 'kernel': ['rbf'], 'gamma': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05])}, {'C': array([0.52480746, 0.52674449, 0.52868867, 0.53064002, 0.53259857,
       0.53456436, 0.5365374 , 0.53851772, 0.54050535, 0.54250032,
       0.54450265]), 'kernel': ['linear']}], {'C': 0.5345643593969717, 'kernel': 'linear'}, 0.6665064102564102)
这是第1次迭代微调C和gamma。
第1次迭代，得到delta： [0.04087558]
执行tunemodel()函数前，使用的grid search parameter是：
 [{'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05]), 'kernel': ['rbf'], 'gamma': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05])}, {'C': array([0.52480746, 0.52674449, 0.52868867, 0.53064002, 0.53259857,
       0.53456436, 0.5365374 , 0.53851772, 0.54050535, 0.54250032,
       0.54450265]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.662 (+/-0.389) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.662 (+/-0.389) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.626 (+/-0.318) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.635 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.626 (+/-0.318) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.5248074602497725, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.526744488331906, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.5286886658508809, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.5306400191947748, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.5325985748490616, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.5345643593969717, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.5365373995198517, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.538517721997528, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.5405053537086689, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.5425003216311501, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.5445026528424212, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      1.00       623

avg / total       0.98      0.99      0.99       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.323) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.323) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.323) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.5248074602497725, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.526744488331906, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.5286886658508809, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.5306400191947748, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.5325985748490616, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.5345643593969717, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.5365373995198517, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.538517721997528, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.5405053537086689, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.5425003216311501, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.5445026528424212, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6400641025641025
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.672 (+/-0.392) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.698 (+/-0.383) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.681 (+/-0.372) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.681 (+/-0.372) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.662 (+/-0.389) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.681 (+/-0.372) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.662 (+/-0.389) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.626 (+/-0.318) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.698 (+/-0.383) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.635 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.626 (+/-0.318) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.772 (+/-0.473) for {'C': 0.5248074602497725, 'kernel': 'linear'}
0.772 (+/-0.473) for {'C': 0.526744488331906, 'kernel': 'linear'}
0.772 (+/-0.473) for {'C': 0.5286886658508809, 'kernel': 'linear'}
0.773 (+/-0.474) for {'C': 0.5306400191947748, 'kernel': 'linear'}
0.773 (+/-0.474) for {'C': 0.5325985748490616, 'kernel': 'linear'}
0.773 (+/-0.474) for {'C': 0.5345643593969717, 'kernel': 'linear'}
0.773 (+/-0.474) for {'C': 0.5365373995198517, 'kernel': 'linear'}
0.773 (+/-0.474) for {'C': 0.538517721997528, 'kernel': 'linear'}
0.773 (+/-0.474) for {'C': 0.5405053537086689, 'kernel': 'linear'}
0.773 (+/-0.474) for {'C': 0.5425003216311501, 'kernel': 'linear'}
0.773 (+/-0.474) for {'C': 0.5445026528424212, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.666 (+/-0.315) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.314) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.666 (+/-0.314) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.323) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.314) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.323) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.323) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.642 (+/-0.236) for {'C': 0.5248074602497725, 'kernel': 'linear'}
0.642 (+/-0.236) for {'C': 0.526744488331906, 'kernel': 'linear'}
0.642 (+/-0.236) for {'C': 0.5286886658508809, 'kernel': 'linear'}
0.667 (+/-0.316) for {'C': 0.5306400191947748, 'kernel': 'linear'}
0.667 (+/-0.316) for {'C': 0.5325985748490616, 'kernel': 'linear'}
0.667 (+/-0.316) for {'C': 0.5345643593969717, 'kernel': 'linear'}
0.667 (+/-0.316) for {'C': 0.5365373995198517, 'kernel': 'linear'}
0.667 (+/-0.316) for {'C': 0.538517721997528, 'kernel': 'linear'}
0.667 (+/-0.316) for {'C': 0.5405053537086689, 'kernel': 'linear'}
0.667 (+/-0.316) for {'C': 0.5425003216311501, 'kernel': 'linear'}
0.667 (+/-0.316) for {'C': 0.5445026528424212, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 0.5306400191947748, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6665064102564102
第2轮loop中，tunemodel函数得到的最优参数是： ([{'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05]), 'kernel': ['rbf'], 'gamma': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05])}, {'C': array([0.52868867, 0.52907836, 0.52946834, 0.52985862, 0.53024917,
       0.53064002, 0.53103115, 0.53142258, 0.53181429, 0.53220629,
       0.53259857]), 'kernel': ['linear']}], {'C': 0.5306400191947748, 'kernel': 'linear'}, 0.6665064102564102)
这是第2次迭代微调C和gamma。
第2次迭代，得到delta： [0.00392434]
SVC模型clf_op_iter的参数是： {'kernel': 'linear', 'decision_function_shape': 'ovr', 'class_weight': {-1: 30}, 'tol': 0.001, 'gamma': 'auto', 'random_state': 0, 'cache_size': 200, 'verbose': False, 'degree': 3, 'C': 0.5306400191947748, 'probability': False, 'shrinking': True, 'coef0': 0.0, 'max_iter': -1}

训练模型SVC对训练样本的预测准确率： 0.9029187817258884

测试集中，预测为舞弊样本的有： (array([ 370,  658,  769, 1246, 1247, 1248, 1251, 1252, 1255, 1256],
      dtype=int64),) 
测试集中，实际为舞弊样本的有： (array([1246, 1247, 1248, 1249, 1250, 1251, 1252, 1253, 1254, 1255, 1256],
      dtype=int64),) 
预测的舞弊样本数目： 10

训练模型SVC对测试样本的预测准确率： 0.9219543147208121
以上是第8次特征筛选。
第8次特征筛选，AUC值是： 0.8169779658543703
X_train_iter_svc.shape is: (1257, 44)
X_test_iter_svc.shape is: (1257, 44)
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.722 (+/-0.473) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.616 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6166666666666667
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.723 (+/-0.417) for {'C': 1.0, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.635 (+/-0.326) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.615 (+/-0.237) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6661858974358974
RBF的粗grid search最佳超参： {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0} RBF的粗grid search最高分值： 0.6166666666666667
linear的粗grid search最佳超参： {'C': 1.0, 'kernel': 'linear'} linear的粗grid search最高分值： 0.6661858974358974
粗grid search得到的parameter是：
 [{'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05]), 'kernel': ['rbf'], 'gamma': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.662 (+/-0.389) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.662 (+/-0.389) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.626 (+/-0.318) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.635 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.626 (+/-0.318) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.714 (+/-0.417) for {'C': 10.0, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 100.0, 'kernel': 'linear'}
0.635 (+/-0.326) for {'C': 1000.0, 'kernel': 'linear'}
0.635 (+/-0.326) for {'C': 10000.0, 'kernel': 'linear'}
0.635 (+/-0.326) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      1.00       623

avg / total       0.98      0.99      0.99       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.323) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.323) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.323) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.665 (+/-0.315) for {'C': 10.0, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 100.0, 'kernel': 'linear'}
0.615 (+/-0.237) for {'C': 1000.0, 'kernel': 'linear'}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'linear'}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.14      0.17      0.15         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 10.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6650641025641026
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.672 (+/-0.392) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.698 (+/-0.383) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.681 (+/-0.372) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.681 (+/-0.372) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.662 (+/-0.389) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.681 (+/-0.372) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.662 (+/-0.389) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.626 (+/-0.318) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.698 (+/-0.383) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.635 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.626 (+/-0.318) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.723 (+/-0.417) for {'C': 1.0, 'kernel': 'linear'}
0.679 (+/-0.373) for {'C': 10.0, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 100.0, 'kernel': 'linear'}
0.635 (+/-0.326) for {'C': 1000.0, 'kernel': 'linear'}
0.635 (+/-0.326) for {'C': 10000.0, 'kernel': 'linear'}
0.635 (+/-0.326) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      1.00       623

avg / total       0.98      0.99      0.99       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.666 (+/-0.315) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.314) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.666 (+/-0.314) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.323) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.314) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.323) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.323) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.664 (+/-0.314) for {'C': 10.0, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 100.0, 'kernel': 'linear'}
0.615 (+/-0.237) for {'C': 1000.0, 'kernel': 'linear'}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'linear'}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6661858974358974
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.662 (+/-0.389) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.662 (+/-0.389) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.626 (+/-0.318) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.635 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.626 (+/-0.318) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.689 (+/-0.436) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.689 (+/-0.374) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.714 (+/-0.417) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      1.00       623

avg / total       0.98      0.99      0.99       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.323) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.323) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.323) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.641 (+/-0.235) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.665 (+/-0.315) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.14      0.17      0.15         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 9.999999999999998, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6650641025641026
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.672 (+/-0.392) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.698 (+/-0.383) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.681 (+/-0.372) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.681 (+/-0.372) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.662 (+/-0.389) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.681 (+/-0.372) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.662 (+/-0.389) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.626 (+/-0.318) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.698 (+/-0.383) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.635 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.626 (+/-0.318) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.797 (+/-0.492) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.723 (+/-0.417) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.689 (+/-0.382) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.709 (+/-0.417) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.668 (+/-0.371) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.645 (+/-0.314) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.679 (+/-0.373) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.666 (+/-0.315) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.314) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.666 (+/-0.314) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.323) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.314) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.323) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.323) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.642 (+/-0.236) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.666 (+/-0.315) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.666 (+/-0.314) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.664 (+/-0.313) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.663 (+/-0.314) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.664 (+/-0.314) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 0.6309573444801931, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6663461538461538
循环迭代之前，delta is: [0.36904266]
执行tunemodel()函数前，使用的grid search parameter是：
 [{'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05]), 'kernel': ['rbf'], 'gamma': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05])}, {'C': array([0.39810717, 0.43651583, 0.47863009, 0.52480746, 0.57543994,
       0.63095734, 0.69183097, 0.75857758, 0.83176377, 0.91201084,
       1.        ]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.662 (+/-0.389) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.662 (+/-0.389) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.626 (+/-0.318) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.635 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.626 (+/-0.318) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.43651583224016594, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.47863009232263826, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.5248074602497725, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.5754399373371568, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.6918309709189364, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.7585775750291837, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.8317637711026709, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.9120108393559095, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.9999999999999997, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      1.00       623

avg / total       0.98      0.99      0.99       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.323) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.323) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.323) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.43651583224016594, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.47863009232263826, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.5248074602497725, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.5754399373371568, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.6918309709189364, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.7585775750291837, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.8317637711026709, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.9120108393559095, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.9999999999999997, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6399038461538461
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.672 (+/-0.392) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.698 (+/-0.383) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.681 (+/-0.372) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.681 (+/-0.372) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.662 (+/-0.389) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.681 (+/-0.372) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.662 (+/-0.389) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.626 (+/-0.318) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.698 (+/-0.383) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.635 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.626 (+/-0.318) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.797 (+/-0.492) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.797 (+/-0.492) for {'C': 0.43651583224016594, 'kernel': 'linear'}
0.797 (+/-0.492) for {'C': 0.47863009232263826, 'kernel': 'linear'}
0.772 (+/-0.473) for {'C': 0.5248074602497725, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.5754399373371568, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.6918309709189364, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.7585775750291837, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.8317637711026709, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.9120108393559095, 'kernel': 'linear'}
0.723 (+/-0.417) for {'C': 0.9999999999999997, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.666 (+/-0.315) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.314) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.666 (+/-0.314) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.323) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.314) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.323) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.323) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.642 (+/-0.236) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.642 (+/-0.236) for {'C': 0.43651583224016594, 'kernel': 'linear'}
0.642 (+/-0.236) for {'C': 0.47863009232263826, 'kernel': 'linear'}
0.642 (+/-0.236) for {'C': 0.5248074602497725, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.5754399373371568, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.6918309709189364, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.7585775750291837, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.8317637711026709, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.9120108393559095, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.9999999999999997, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 0.5754399373371568, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6663461538461538
第0轮loop中，tunemodel函数得到的最优参数是： ([{'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05]), 'kernel': ['rbf'], 'gamma': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05])}, {'C': array([0.52480746, 0.53456436, 0.54450265, 0.55462571, 0.56493697,
       0.57543994, 0.58613816, 0.59703529, 0.608135  , 0.61944108,
       0.63095734]), 'kernel': ['linear']}], {'C': 0.5754399373371568, 'kernel': 'linear'}, 0.6663461538461538)
这是第0次迭代微调C和gamma。
第0次迭代，得到delta： [0.05551741]
执行tunemodel()函数前，使用的grid search parameter是：
 [{'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05]), 'kernel': ['rbf'], 'gamma': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05])}, {'C': array([0.52480746, 0.53456436, 0.54450265, 0.55462571, 0.56493697,
       0.57543994, 0.58613816, 0.59703529, 0.608135  , 0.61944108,
       0.63095734]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.662 (+/-0.389) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.662 (+/-0.389) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.626 (+/-0.318) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.635 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.626 (+/-0.318) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.5248074602497725, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.5345643593969717, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.5445026528424212, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.5546257129579107, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.5649369748123024, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.5754399373371568, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.5861381645140287, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.5970352865838368, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.6081350012787178, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.6194410750767814, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.6309573444801931, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      1.00       623

avg / total       0.98      0.99      0.99       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.323) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.323) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.323) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.5248074602497725, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.5345643593969717, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.5445026528424212, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.5546257129579107, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.5649369748123024, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.5754399373371568, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.5861381645140287, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.5970352865838368, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.6081350012787178, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.6194410750767814, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.6309573444801931, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6399038461538461
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.672 (+/-0.392) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.698 (+/-0.383) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.681 (+/-0.372) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.681 (+/-0.372) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.662 (+/-0.389) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.681 (+/-0.372) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.662 (+/-0.389) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.626 (+/-0.318) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.698 (+/-0.383) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.635 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.626 (+/-0.318) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.772 (+/-0.473) for {'C': 0.5248074602497725, 'kernel': 'linear'}
0.772 (+/-0.473) for {'C': 0.5345643593969717, 'kernel': 'linear'}
0.773 (+/-0.474) for {'C': 0.5445026528424212, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.5546257129579107, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.5649369748123024, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.5754399373371568, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.5861381645140287, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.5970352865838368, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.6081350012787178, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.6194410750767814, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.6309573444801931, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.666 (+/-0.315) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.314) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.666 (+/-0.314) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.323) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.314) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.323) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.323) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.642 (+/-0.236) for {'C': 0.5248074602497725, 'kernel': 'linear'}
0.642 (+/-0.236) for {'C': 0.5345643593969717, 'kernel': 'linear'}
0.667 (+/-0.316) for {'C': 0.5445026528424212, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.5546257129579107, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.5649369748123024, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.5754399373371568, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.5861381645140287, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.5970352865838368, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.6081350012787178, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.6194410750767814, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.6309573444801931, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 0.5445026528424212, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6665064102564102
第1轮loop中，tunemodel函数得到的最优参数是： ([{'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05]), 'kernel': ['rbf'], 'gamma': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05])}, {'C': array([0.53456436, 0.5365374 , 0.53851772, 0.54050535, 0.54250032,
       0.54450265, 0.54651237, 0.54852951, 0.5505541 , 0.55258616,
       0.55462571]), 'kernel': ['linear']}], {'C': 0.5445026528424212, 'kernel': 'linear'}, 0.6665064102564102)
这是第1次迭代微调C和gamma。
第1次迭代，得到delta： [0.03093728]
执行tunemodel()函数前，使用的grid search parameter是：
 [{'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05]), 'kernel': ['rbf'], 'gamma': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05])}, {'C': array([0.53456436, 0.5365374 , 0.53851772, 0.54050535, 0.54250032,
       0.54450265, 0.54651237, 0.54852951, 0.5505541 , 0.55258616,
       0.55462571]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.662 (+/-0.389) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.662 (+/-0.389) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.626 (+/-0.318) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.635 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.626 (+/-0.318) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.5345643593969717, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.5365373995198518, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.538517721997528, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.5405053537086689, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.5425003216311503, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.5445026528424212, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.5465123745198721, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.5485295139412031, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.5505540984847945, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.5525861556300782, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.5546257129579107, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      1.00       623

avg / total       0.98      0.99      0.99       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.323) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.323) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.323) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.5345643593969717, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.5365373995198518, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.538517721997528, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.5405053537086689, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.5425003216311503, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.5445026528424212, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.5465123745198721, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.5485295139412031, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.5505540984847945, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.5525861556300782, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.5546257129579107, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6399038461538461
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.672 (+/-0.392) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.698 (+/-0.383) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.681 (+/-0.372) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.681 (+/-0.372) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.662 (+/-0.389) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.681 (+/-0.372) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.662 (+/-0.389) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.626 (+/-0.318) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.698 (+/-0.383) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.635 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.626 (+/-0.318) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.772 (+/-0.473) for {'C': 0.5345643593969717, 'kernel': 'linear'}
0.773 (+/-0.474) for {'C': 0.5365373995198518, 'kernel': 'linear'}
0.773 (+/-0.474) for {'C': 0.538517721997528, 'kernel': 'linear'}
0.773 (+/-0.474) for {'C': 0.5405053537086689, 'kernel': 'linear'}
0.773 (+/-0.474) for {'C': 0.5425003216311503, 'kernel': 'linear'}
0.773 (+/-0.474) for {'C': 0.5445026528424212, 'kernel': 'linear'}
0.773 (+/-0.474) for {'C': 0.5465123745198721, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.5485295139412031, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.5505540984847945, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.5525861556300782, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.5546257129579107, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.666 (+/-0.315) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.314) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.666 (+/-0.314) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.323) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.314) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.323) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.323) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.642 (+/-0.236) for {'C': 0.5345643593969717, 'kernel': 'linear'}
0.667 (+/-0.316) for {'C': 0.5365373995198518, 'kernel': 'linear'}
0.667 (+/-0.316) for {'C': 0.538517721997528, 'kernel': 'linear'}
0.667 (+/-0.316) for {'C': 0.5405053537086689, 'kernel': 'linear'}
0.667 (+/-0.316) for {'C': 0.5425003216311503, 'kernel': 'linear'}
0.667 (+/-0.316) for {'C': 0.5445026528424212, 'kernel': 'linear'}
0.667 (+/-0.316) for {'C': 0.5465123745198721, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.5485295139412031, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.5505540984847945, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.5525861556300782, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.5546257129579107, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 0.5365373995198518, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6665064102564102
第2轮loop中，tunemodel函数得到的最优参数是： ([{'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05]), 'kernel': ['rbf'], 'gamma': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05])}, {'C': array([0.53456436, 0.53495839, 0.5353527 , 0.53574731, 0.53614221,
       0.5365374 , 0.53693288, 0.53732865, 0.53772472, 0.53812107,
       0.53851772]), 'kernel': ['linear']}], {'C': 0.5365373995198518, 'kernel': 'linear'}, 0.6665064102564102)
这是第2次迭代微调C和gamma。
第2次迭代，得到delta： [0.00796525]
SVC模型clf_op_iter的参数是： {'kernel': 'linear', 'decision_function_shape': 'ovr', 'class_weight': {-1: 30}, 'tol': 0.001, 'gamma': 'auto', 'random_state': 0, 'cache_size': 200, 'verbose': False, 'degree': 3, 'C': 0.5365373995198518, 'probability': False, 'shrinking': True, 'coef0': 0.0, 'max_iter': -1}

训练模型SVC对训练样本的预测准确率： 0.9029187817258884

测试集中，预测为舞弊样本的有： (array([ 370,  658,  769, 1246, 1247, 1248, 1251, 1252, 1255, 1256],
      dtype=int64),) 
测试集中，实际为舞弊样本的有： (array([1246, 1247, 1248, 1249, 1250, 1251, 1252, 1253, 1254, 1255, 1256],
      dtype=int64),) 
预测的舞弊样本数目： 10

训练模型SVC对测试样本的预测准确率： 0.9219543147208121
以上是第9次特征筛选。
第9次特征筛选，AUC值是： 0.8169779658543703
X_train_iter_svc.shape is: (1257, 43)
X_test_iter_svc.shape is: (1257, 43)
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.615 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6166666666666667
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.723 (+/-0.417) for {'C': 1.0, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.656 (+/-0.384) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.614 (+/-0.240) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6661858974358974
RBF的粗grid search最佳超参： {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0} RBF的粗grid search最高分值： 0.6166666666666667
linear的粗grid search最佳超参： {'C': 1.0, 'kernel': 'linear'} linear的粗grid search最高分值： 0.6661858974358974
粗grid search得到的parameter是：
 [{'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05]), 'kernel': ['rbf'], 'gamma': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.639 (+/-0.396) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.374) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.668 (+/-0.378) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.656 (+/-0.384) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.668 (+/-0.378) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.689 (+/-0.436) for {'C': 10.0, 'kernel': 'linear'}
0.666 (+/-0.372) for {'C': 100.0, 'kernel': 'linear'}
0.656 (+/-0.384) for {'C': 1000.0, 'kernel': 'linear'}
0.656 (+/-0.384) for {'C': 10000.0, 'kernel': 'linear'}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      1.00       623

avg / total       0.98      0.99      0.99       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.232) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.639 (+/-0.236) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.236) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.240) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.639 (+/-0.236) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.616 (+/-0.238) for {'C': 10.0, 'kernel': 'linear'}
0.638 (+/-0.238) for {'C': 100.0, 'kernel': 'linear'}
0.614 (+/-0.240) for {'C': 1000.0, 'kernel': 'linear'}
0.614 (+/-0.240) for {'C': 10000.0, 'kernel': 'linear'}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.14      0.17      0.15         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6389423076923078
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.664 (+/-0.388) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.383) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.639 (+/-0.396) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.698 (+/-0.383) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.655 (+/-0.378) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.698 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.661 (+/-0.384) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.656 (+/-0.384) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.698 (+/-0.383) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.663 (+/-0.382) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.723 (+/-0.417) for {'C': 1.0, 'kernel': 'linear'}
0.653 (+/-0.379) for {'C': 10.0, 'kernel': 'linear'}
0.666 (+/-0.372) for {'C': 100.0, 'kernel': 'linear'}
0.656 (+/-0.384) for {'C': 1000.0, 'kernel': 'linear'}
0.656 (+/-0.384) for {'C': 10000.0, 'kernel': 'linear'}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      1.00       623

avg / total       0.98      0.99      0.99       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.315) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.232) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.666 (+/-0.315) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.315) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.637 (+/-0.236) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.240) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.638 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.661 (+/-0.313) for {'C': 10.0, 'kernel': 'linear'}
0.638 (+/-0.238) for {'C': 100.0, 'kernel': 'linear'}
0.614 (+/-0.240) for {'C': 1000.0, 'kernel': 'linear'}
0.614 (+/-0.240) for {'C': 10000.0, 'kernel': 'linear'}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6661858974358974
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.639 (+/-0.396) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.374) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.668 (+/-0.378) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.656 (+/-0.384) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.668 (+/-0.378) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.664 (+/-0.388) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.689 (+/-0.436) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.689 (+/-0.436) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      1.00       623

avg / total       0.98      0.99      0.99       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.232) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.639 (+/-0.236) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.236) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.240) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.639 (+/-0.236) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.616 (+/-0.238) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.14      0.17      0.15         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6389423076923078
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.664 (+/-0.388) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.383) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.639 (+/-0.396) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.698 (+/-0.383) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.655 (+/-0.378) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.698 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.661 (+/-0.384) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.656 (+/-0.384) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.698 (+/-0.383) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.663 (+/-0.382) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.747 (+/-0.449) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.723 (+/-0.417) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.689 (+/-0.374) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.698 (+/-0.383) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.681 (+/-0.372) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.659 (+/-0.375) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.653 (+/-0.379) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.315) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.232) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.666 (+/-0.315) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.315) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.637 (+/-0.236) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.240) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.638 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.641 (+/-0.236) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.666 (+/-0.314) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.666 (+/-0.315) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.665 (+/-0.314) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.663 (+/-0.313) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.661 (+/-0.313) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 0.9999999999999997, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6661858974358974
循环迭代之前，delta is: [3.33066907e-16]
SVC模型clf_op_iter的参数是： {'kernel': 'linear', 'decision_function_shape': 'ovr', 'class_weight': {-1: 30}, 'tol': 0.001, 'gamma': 'auto', 'random_state': 0, 'cache_size': 200, 'verbose': False, 'degree': 3, 'C': 0.9999999999999997, 'probability': False, 'shrinking': True, 'coef0': 0.0, 'max_iter': -1}

训练模型SVC对训练样本的预测准确率： 0.9016497461928934

测试集中，预测为舞弊样本的有： (array([ 370, 1248, 1251, 1252, 1255, 1256], dtype=int64),) 
测试集中，实际为舞弊样本的有： (array([1246, 1247, 1248, 1249, 1250, 1251, 1252, 1253, 1254, 1255, 1256],
      dtype=int64),) 
预测的舞弊样本数目： 6

训练模型SVC对测试样本的预测准确率： 0.8851522842639594
以上是第10次特征筛选。
第10次特征筛选，AUC值是： 0.726871443163578
X_train_iter_svc.shape is: (1257, 42)
X_test_iter_svc.shape is: (1257, 42)
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.615 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6166666666666667
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.723 (+/-0.417) for {'C': 1.0, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.656 (+/-0.384) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.614 (+/-0.240) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6661858974358974
RBF的粗grid search最佳超参： {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0} RBF的粗grid search最高分值： 0.6166666666666667
linear的粗grid search最佳超参： {'C': 1.0, 'kernel': 'linear'} linear的粗grid search最高分值： 0.6661858974358974
粗grid search得到的parameter是：
 [{'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05]), 'kernel': ['rbf'], 'gamma': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.639 (+/-0.396) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.374) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.668 (+/-0.378) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.656 (+/-0.384) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.668 (+/-0.378) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.689 (+/-0.436) for {'C': 10.0, 'kernel': 'linear'}
0.656 (+/-0.384) for {'C': 100.0, 'kernel': 'linear'}
0.656 (+/-0.384) for {'C': 1000.0, 'kernel': 'linear'}
0.656 (+/-0.384) for {'C': 10000.0, 'kernel': 'linear'}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      1.00       623

avg / total       0.98      0.99      0.99       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.232) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.639 (+/-0.236) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.236) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.240) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.639 (+/-0.236) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.616 (+/-0.238) for {'C': 10.0, 'kernel': 'linear'}
0.613 (+/-0.241) for {'C': 100.0, 'kernel': 'linear'}
0.614 (+/-0.240) for {'C': 1000.0, 'kernel': 'linear'}
0.614 (+/-0.240) for {'C': 10000.0, 'kernel': 'linear'}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.14      0.17      0.15         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6389423076923078
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.664 (+/-0.388) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.383) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.639 (+/-0.396) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.698 (+/-0.383) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.655 (+/-0.378) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.698 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.662 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.656 (+/-0.384) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.698 (+/-0.383) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.663 (+/-0.382) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.723 (+/-0.417) for {'C': 1.0, 'kernel': 'linear'}
0.653 (+/-0.379) for {'C': 10.0, 'kernel': 'linear'}
0.656 (+/-0.384) for {'C': 100.0, 'kernel': 'linear'}
0.656 (+/-0.384) for {'C': 1000.0, 'kernel': 'linear'}
0.656 (+/-0.384) for {'C': 10000.0, 'kernel': 'linear'}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      1.00       623

avg / total       0.98      0.99      0.99       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.315) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.232) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.666 (+/-0.315) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.315) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.637 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.240) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.638 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.661 (+/-0.313) for {'C': 10.0, 'kernel': 'linear'}
0.613 (+/-0.241) for {'C': 100.0, 'kernel': 'linear'}
0.614 (+/-0.240) for {'C': 1000.0, 'kernel': 'linear'}
0.614 (+/-0.240) for {'C': 10000.0, 'kernel': 'linear'}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6661858974358974
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.639 (+/-0.396) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.374) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.668 (+/-0.378) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.656 (+/-0.384) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.668 (+/-0.378) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.664 (+/-0.388) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.689 (+/-0.436) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.689 (+/-0.436) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      1.00       623

avg / total       0.98      0.99      0.99       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.232) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.639 (+/-0.236) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.236) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.240) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.639 (+/-0.236) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.616 (+/-0.238) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.14      0.17      0.15         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6389423076923078
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.664 (+/-0.388) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.383) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.639 (+/-0.396) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.698 (+/-0.383) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.655 (+/-0.378) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.698 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.662 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.656 (+/-0.384) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.698 (+/-0.383) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.663 (+/-0.382) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.747 (+/-0.449) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.723 (+/-0.417) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.689 (+/-0.374) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.698 (+/-0.383) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.681 (+/-0.372) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.659 (+/-0.375) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.653 (+/-0.379) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.315) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.232) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.666 (+/-0.315) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.315) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.637 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.240) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.638 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.641 (+/-0.236) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.666 (+/-0.314) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.666 (+/-0.315) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.665 (+/-0.314) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.663 (+/-0.313) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.661 (+/-0.313) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 0.9999999999999997, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6661858974358974
循环迭代之前，delta is: [3.33066907e-16]
SVC模型clf_op_iter的参数是： {'kernel': 'linear', 'decision_function_shape': 'ovr', 'class_weight': {-1: 30}, 'tol': 0.001, 'gamma': 'auto', 'random_state': 0, 'cache_size': 200, 'verbose': False, 'degree': 3, 'C': 0.9999999999999997, 'probability': False, 'shrinking': True, 'coef0': 0.0, 'max_iter': -1}

训练模型SVC对训练样本的预测准确率： 0.9016497461928934

测试集中，预测为舞弊样本的有： (array([ 370, 1248, 1251, 1252, 1255, 1256], dtype=int64),) 
测试集中，实际为舞弊样本的有： (array([1246, 1247, 1248, 1249, 1250, 1251, 1252, 1253, 1254, 1255, 1256],
      dtype=int64),) 
预测的舞弊样本数目： 6

训练模型SVC对测试样本的预测准确率： 0.8851522842639594
以上是第11次特征筛选。
第11次特征筛选，AUC值是： 0.726871443163578
X_train_iter_svc.shape is: (1257, 41)
X_test_iter_svc.shape is: (1257, 41)
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.615 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6166666666666667
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.723 (+/-0.417) for {'C': 1.0, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.656 (+/-0.384) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.614 (+/-0.240) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6661858974358974
RBF的粗grid search最佳超参： {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0} RBF的粗grid search最高分值： 0.6166666666666667
linear的粗grid search最佳超参： {'C': 1.0, 'kernel': 'linear'} linear的粗grid search最高分值： 0.6661858974358974
粗grid search得到的parameter是：
 [{'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05]), 'kernel': ['rbf'], 'gamma': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.639 (+/-0.396) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.374) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.668 (+/-0.378) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.656 (+/-0.384) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.668 (+/-0.378) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.689 (+/-0.436) for {'C': 10.0, 'kernel': 'linear'}
0.656 (+/-0.384) for {'C': 100.0, 'kernel': 'linear'}
0.656 (+/-0.384) for {'C': 1000.0, 'kernel': 'linear'}
0.656 (+/-0.384) for {'C': 10000.0, 'kernel': 'linear'}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      1.00       623

avg / total       0.98      0.99      0.99       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.232) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.639 (+/-0.236) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.236) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.240) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.639 (+/-0.236) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.616 (+/-0.238) for {'C': 10.0, 'kernel': 'linear'}
0.613 (+/-0.241) for {'C': 100.0, 'kernel': 'linear'}
0.614 (+/-0.240) for {'C': 1000.0, 'kernel': 'linear'}
0.614 (+/-0.240) for {'C': 10000.0, 'kernel': 'linear'}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.14      0.17      0.15         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6389423076923078
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.664 (+/-0.388) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.383) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.639 (+/-0.396) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.698 (+/-0.383) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.655 (+/-0.378) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.698 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.662 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.656 (+/-0.384) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.698 (+/-0.383) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.663 (+/-0.382) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.723 (+/-0.417) for {'C': 1.0, 'kernel': 'linear'}
0.653 (+/-0.379) for {'C': 10.0, 'kernel': 'linear'}
0.656 (+/-0.384) for {'C': 100.0, 'kernel': 'linear'}
0.656 (+/-0.384) for {'C': 1000.0, 'kernel': 'linear'}
0.656 (+/-0.384) for {'C': 10000.0, 'kernel': 'linear'}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      1.00       623

avg / total       0.98      0.99      0.99       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.315) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.232) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.666 (+/-0.315) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.315) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.637 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.240) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.638 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.661 (+/-0.313) for {'C': 10.0, 'kernel': 'linear'}
0.613 (+/-0.241) for {'C': 100.0, 'kernel': 'linear'}
0.614 (+/-0.240) for {'C': 1000.0, 'kernel': 'linear'}
0.614 (+/-0.240) for {'C': 10000.0, 'kernel': 'linear'}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6661858974358974
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.639 (+/-0.396) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.374) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.668 (+/-0.378) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.656 (+/-0.384) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.668 (+/-0.378) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.664 (+/-0.388) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.689 (+/-0.436) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.689 (+/-0.436) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      1.00       623

avg / total       0.98      0.99      0.99       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.232) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.639 (+/-0.236) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.236) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.240) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.639 (+/-0.236) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.616 (+/-0.238) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.14      0.17      0.15         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6389423076923078
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.664 (+/-0.388) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.383) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.639 (+/-0.396) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.698 (+/-0.383) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.655 (+/-0.378) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.698 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.662 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.656 (+/-0.384) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.698 (+/-0.383) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.663 (+/-0.382) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.747 (+/-0.449) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.723 (+/-0.417) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.689 (+/-0.374) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.698 (+/-0.383) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.681 (+/-0.372) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.659 (+/-0.375) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.653 (+/-0.379) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.315) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.232) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.666 (+/-0.315) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.315) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.637 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.240) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.638 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.641 (+/-0.236) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.666 (+/-0.314) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.666 (+/-0.315) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.665 (+/-0.314) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.663 (+/-0.313) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.661 (+/-0.313) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 0.9999999999999997, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6661858974358974
循环迭代之前，delta is: [3.33066907e-16]
SVC模型clf_op_iter的参数是： {'kernel': 'linear', 'decision_function_shape': 'ovr', 'class_weight': {-1: 30}, 'tol': 0.001, 'gamma': 'auto', 'random_state': 0, 'cache_size': 200, 'verbose': False, 'degree': 3, 'C': 0.9999999999999997, 'probability': False, 'shrinking': True, 'coef0': 0.0, 'max_iter': -1}

训练模型SVC对训练样本的预测准确率： 0.9016497461928934

测试集中，预测为舞弊样本的有： (array([ 370, 1248, 1251, 1252, 1255, 1256], dtype=int64),) 
测试集中，实际为舞弊样本的有： (array([1246, 1247, 1248, 1249, 1250, 1251, 1252, 1253, 1254, 1255, 1256],
      dtype=int64),) 
预测的舞弊样本数目： 6

训练模型SVC对测试样本的预测准确率： 0.8851522842639594
以上是第12次特征筛选。
第12次特征筛选，AUC值是： 0.726871443163578
X_train_iter_svc.shape is: (1257, 40)
X_test_iter_svc.shape is: (1257, 40)
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.639 (+/-0.326) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.615 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6166666666666667
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.723 (+/-0.417) for {'C': 1.0, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.614 (+/-0.240) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6661858974358974
RBF的粗grid search最佳超参： {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0} RBF的粗grid search最高分值： 0.6166666666666667
linear的粗grid search最佳超参： {'C': 1.0, 'kernel': 'linear'} linear的粗grid search最高分值： 0.6661858974358974
粗grid search得到的parameter是：
 [{'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05]), 'kernel': ['rbf'], 'gamma': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.639 (+/-0.396) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.374) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.668 (+/-0.378) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.656 (+/-0.384) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.668 (+/-0.378) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.689 (+/-0.436) for {'C': 10.0, 'kernel': 'linear'}
0.656 (+/-0.384) for {'C': 100.0, 'kernel': 'linear'}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'linear'}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'linear'}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      1.00       623

avg / total       0.98      0.99      0.99       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.589 (+/-0.232) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.639 (+/-0.236) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.236) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.240) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.639 (+/-0.236) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.614 (+/-0.241) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.616 (+/-0.238) for {'C': 10.0, 'kernel': 'linear'}
0.613 (+/-0.241) for {'C': 100.0, 'kernel': 'linear'}
0.614 (+/-0.240) for {'C': 1000.0, 'kernel': 'linear'}
0.614 (+/-0.240) for {'C': 10000.0, 'kernel': 'linear'}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.14      0.17      0.15         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6389423076923078
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.664 (+/-0.388) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.383) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.639 (+/-0.396) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.698 (+/-0.383) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.655 (+/-0.378) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.698 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.662 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.656 (+/-0.384) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.698 (+/-0.383) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.379) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.723 (+/-0.417) for {'C': 1.0, 'kernel': 'linear'}
0.653 (+/-0.379) for {'C': 10.0, 'kernel': 'linear'}
0.656 (+/-0.384) for {'C': 100.0, 'kernel': 'linear'}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'linear'}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'linear'}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      1.00       623

avg / total       0.98      0.99      0.99       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.315) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.232) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.666 (+/-0.315) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.315) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.637 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.240) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.662 (+/-0.314) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.614 (+/-0.241) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.661 (+/-0.314) for {'C': 10.0, 'kernel': 'linear'}
0.613 (+/-0.241) for {'C': 100.0, 'kernel': 'linear'}
0.614 (+/-0.240) for {'C': 1000.0, 'kernel': 'linear'}
0.614 (+/-0.240) for {'C': 10000.0, 'kernel': 'linear'}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6661858974358974
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.639 (+/-0.396) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.374) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.668 (+/-0.378) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.656 (+/-0.384) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.668 (+/-0.378) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.664 (+/-0.388) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.689 (+/-0.436) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.689 (+/-0.436) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      1.00       623

avg / total       0.98      0.99      0.99       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.589 (+/-0.232) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.639 (+/-0.236) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.236) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.240) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.639 (+/-0.236) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.614 (+/-0.241) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.616 (+/-0.238) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.14      0.17      0.15         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6389423076923078
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.664 (+/-0.388) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.383) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.639 (+/-0.396) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.698 (+/-0.383) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.655 (+/-0.378) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.698 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.662 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.656 (+/-0.384) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.698 (+/-0.383) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.379) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.747 (+/-0.449) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.723 (+/-0.417) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.689 (+/-0.374) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.698 (+/-0.383) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.681 (+/-0.372) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.659 (+/-0.375) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.653 (+/-0.379) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.315) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.232) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.666 (+/-0.315) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.315) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.637 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.240) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.662 (+/-0.314) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.614 (+/-0.241) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.641 (+/-0.236) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.666 (+/-0.314) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.666 (+/-0.315) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.665 (+/-0.314) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.663 (+/-0.313) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.661 (+/-0.314) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 0.9999999999999997, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6661858974358974
循环迭代之前，delta is: [3.33066907e-16]
SVC模型clf_op_iter的参数是： {'kernel': 'linear', 'decision_function_shape': 'ovr', 'class_weight': {-1: 30}, 'tol': 0.001, 'gamma': 'auto', 'random_state': 0, 'cache_size': 200, 'verbose': False, 'degree': 3, 'C': 0.9999999999999997, 'probability': False, 'shrinking': True, 'coef0': 0.0, 'max_iter': -1}

训练模型SVC对训练样本的预测准确率： 0.9016497461928934

测试集中，预测为舞弊样本的有： (array([ 370, 1248, 1251, 1252, 1255, 1256], dtype=int64),) 
测试集中，实际为舞弊样本的有： (array([1246, 1247, 1248, 1249, 1250, 1251, 1252, 1253, 1254, 1255, 1256],
      dtype=int64),) 
预测的舞弊样本数目： 6

训练模型SVC对测试样本的预测准确率： 0.8851522842639594
以上是第13次特征筛选。
第13次特征筛选，AUC值是： 0.726871443163578
X_train_iter_svc.shape is: (1257, 39)
X_test_iter_svc.shape is: (1257, 39)
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.639 (+/-0.326) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.615 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6166666666666667
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.723 (+/-0.417) for {'C': 1.0, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.614 (+/-0.240) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6661858974358974
RBF的粗grid search最佳超参： {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0} RBF的粗grid search最高分值： 0.6166666666666667
linear的粗grid search最佳超参： {'C': 1.0, 'kernel': 'linear'} linear的粗grid search最高分值： 0.6661858974358974
粗grid search得到的parameter是：
 [{'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05]), 'kernel': ['rbf'], 'gamma': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.639 (+/-0.396) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.374) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.668 (+/-0.378) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.656 (+/-0.384) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.668 (+/-0.378) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.689 (+/-0.436) for {'C': 10.0, 'kernel': 'linear'}
0.656 (+/-0.384) for {'C': 100.0, 'kernel': 'linear'}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'linear'}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'linear'}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      1.00       623

avg / total       0.98      0.99      0.99       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.589 (+/-0.232) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.639 (+/-0.236) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.236) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.240) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.639 (+/-0.236) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.614 (+/-0.241) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.616 (+/-0.238) for {'C': 10.0, 'kernel': 'linear'}
0.613 (+/-0.241) for {'C': 100.0, 'kernel': 'linear'}
0.614 (+/-0.240) for {'C': 1000.0, 'kernel': 'linear'}
0.614 (+/-0.240) for {'C': 10000.0, 'kernel': 'linear'}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.14      0.17      0.15         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6389423076923078
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.664 (+/-0.388) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.383) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.639 (+/-0.396) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.698 (+/-0.383) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.655 (+/-0.378) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.698 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.662 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.656 (+/-0.384) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.698 (+/-0.383) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.379) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.723 (+/-0.417) for {'C': 1.0, 'kernel': 'linear'}
0.653 (+/-0.379) for {'C': 10.0, 'kernel': 'linear'}
0.656 (+/-0.384) for {'C': 100.0, 'kernel': 'linear'}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'linear'}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'linear'}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      1.00       623

avg / total       0.98      0.99      0.99       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.315) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.232) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.666 (+/-0.315) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.315) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.637 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.240) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.662 (+/-0.314) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.614 (+/-0.241) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.661 (+/-0.314) for {'C': 10.0, 'kernel': 'linear'}
0.613 (+/-0.241) for {'C': 100.0, 'kernel': 'linear'}
0.614 (+/-0.240) for {'C': 1000.0, 'kernel': 'linear'}
0.614 (+/-0.240) for {'C': 10000.0, 'kernel': 'linear'}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6661858974358974
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.639 (+/-0.396) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.374) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.668 (+/-0.378) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.656 (+/-0.384) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.668 (+/-0.378) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.664 (+/-0.388) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.689 (+/-0.436) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.689 (+/-0.436) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      1.00       623

avg / total       0.98      0.99      0.99       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.589 (+/-0.232) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.639 (+/-0.236) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.236) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.240) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.639 (+/-0.236) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.614 (+/-0.241) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.616 (+/-0.238) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.14      0.17      0.15         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6389423076923078
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.664 (+/-0.388) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.383) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.639 (+/-0.396) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.698 (+/-0.383) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.655 (+/-0.378) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.698 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.662 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.656 (+/-0.384) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.698 (+/-0.383) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.379) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.747 (+/-0.449) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.723 (+/-0.417) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.689 (+/-0.374) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.698 (+/-0.383) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.681 (+/-0.372) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.659 (+/-0.375) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.653 (+/-0.379) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.315) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.232) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.666 (+/-0.315) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.315) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.637 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.240) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.662 (+/-0.314) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.614 (+/-0.241) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.641 (+/-0.236) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.666 (+/-0.314) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.666 (+/-0.315) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.665 (+/-0.314) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.663 (+/-0.313) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.661 (+/-0.314) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 0.9999999999999997, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6661858974358974
循环迭代之前，delta is: [3.33066907e-16]
SVC模型clf_op_iter的参数是： {'kernel': 'linear', 'decision_function_shape': 'ovr', 'class_weight': {-1: 30}, 'tol': 0.001, 'gamma': 'auto', 'random_state': 0, 'cache_size': 200, 'verbose': False, 'degree': 3, 'C': 0.9999999999999997, 'probability': False, 'shrinking': True, 'coef0': 0.0, 'max_iter': -1}

训练模型SVC对训练样本的预测准确率： 0.9016497461928934

测试集中，预测为舞弊样本的有： (array([ 370, 1248, 1251, 1252, 1255, 1256], dtype=int64),) 
测试集中，实际为舞弊样本的有： (array([1246, 1247, 1248, 1249, 1250, 1251, 1252, 1253, 1254, 1255, 1256],
      dtype=int64),) 
预测的舞弊样本数目： 6

训练模型SVC对测试样本的预测准确率： 0.8851522842639594
以上是第14次特征筛选。
第14次特征筛选，AUC值是： 0.726871443163578
X_train_iter_svc.shape is: (1257, 38)
X_test_iter_svc.shape is: (1257, 38)
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.639 (+/-0.326) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.615 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6166666666666667
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.723 (+/-0.417) for {'C': 1.0, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.614 (+/-0.241) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6661858974358974
RBF的粗grid search最佳超参： {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0} RBF的粗grid search最高分值： 0.6166666666666667
linear的粗grid search最佳超参： {'C': 1.0, 'kernel': 'linear'} linear的粗grid search最高分值： 0.6661858974358974
粗grid search得到的parameter是：
 [{'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05]), 'kernel': ['rbf'], 'gamma': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.639 (+/-0.396) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.374) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.668 (+/-0.378) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.656 (+/-0.384) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.668 (+/-0.378) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.689 (+/-0.436) for {'C': 10.0, 'kernel': 'linear'}
0.656 (+/-0.384) for {'C': 100.0, 'kernel': 'linear'}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'linear'}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'linear'}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      1.00       623

avg / total       0.98      0.99      0.99       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.589 (+/-0.232) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.639 (+/-0.236) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.236) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.240) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.639 (+/-0.236) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.614 (+/-0.241) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.616 (+/-0.238) for {'C': 10.0, 'kernel': 'linear'}
0.613 (+/-0.241) for {'C': 100.0, 'kernel': 'linear'}
0.614 (+/-0.241) for {'C': 1000.0, 'kernel': 'linear'}
0.614 (+/-0.241) for {'C': 10000.0, 'kernel': 'linear'}
0.614 (+/-0.241) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.14      0.17      0.15         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6389423076923078
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.664 (+/-0.388) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.383) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.639 (+/-0.396) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.698 (+/-0.383) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.655 (+/-0.378) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.698 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.662 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.656 (+/-0.384) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.698 (+/-0.383) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.379) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.723 (+/-0.417) for {'C': 1.0, 'kernel': 'linear'}
0.653 (+/-0.379) for {'C': 10.0, 'kernel': 'linear'}
0.656 (+/-0.384) for {'C': 100.0, 'kernel': 'linear'}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'linear'}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'linear'}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      1.00       623

avg / total       0.98      0.99      0.99       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.315) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.232) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.666 (+/-0.315) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.315) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.637 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.240) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.662 (+/-0.314) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.614 (+/-0.241) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.661 (+/-0.314) for {'C': 10.0, 'kernel': 'linear'}
0.613 (+/-0.241) for {'C': 100.0, 'kernel': 'linear'}
0.614 (+/-0.241) for {'C': 1000.0, 'kernel': 'linear'}
0.614 (+/-0.241) for {'C': 10000.0, 'kernel': 'linear'}
0.614 (+/-0.241) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6661858974358974
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.639 (+/-0.396) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.374) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.668 (+/-0.378) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.656 (+/-0.384) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.668 (+/-0.378) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.664 (+/-0.388) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.689 (+/-0.436) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.689 (+/-0.436) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      1.00       623

avg / total       0.98      0.99      0.99       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.589 (+/-0.232) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.639 (+/-0.236) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.236) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.240) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.639 (+/-0.236) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.614 (+/-0.241) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.616 (+/-0.238) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.14      0.17      0.15         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6389423076923078
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.664 (+/-0.388) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.383) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.639 (+/-0.396) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.698 (+/-0.383) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.655 (+/-0.378) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.698 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.662 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.656 (+/-0.384) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.698 (+/-0.383) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.379) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.747 (+/-0.449) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.723 (+/-0.417) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.689 (+/-0.374) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.698 (+/-0.383) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.681 (+/-0.372) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.659 (+/-0.375) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.653 (+/-0.379) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.315) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.232) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.666 (+/-0.315) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.315) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.637 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.240) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.662 (+/-0.314) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.614 (+/-0.241) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.641 (+/-0.236) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.666 (+/-0.314) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.666 (+/-0.315) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.665 (+/-0.314) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.663 (+/-0.313) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.661 (+/-0.314) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 0.9999999999999997, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6661858974358974
循环迭代之前，delta is: [3.33066907e-16]
SVC模型clf_op_iter的参数是： {'kernel': 'linear', 'decision_function_shape': 'ovr', 'class_weight': {-1: 30}, 'tol': 0.001, 'gamma': 'auto', 'random_state': 0, 'cache_size': 200, 'verbose': False, 'degree': 3, 'C': 0.9999999999999997, 'probability': False, 'shrinking': True, 'coef0': 0.0, 'max_iter': -1}

训练模型SVC对训练样本的预测准确率： 0.9016497461928934

测试集中，预测为舞弊样本的有： (array([ 370, 1248, 1251, 1252, 1255, 1256], dtype=int64),) 
测试集中，实际为舞弊样本的有： (array([1246, 1247, 1248, 1249, 1250, 1251, 1252, 1253, 1254, 1255, 1256],
      dtype=int64),) 
预测的舞弊样本数目： 6

训练模型SVC对测试样本的预测准确率： 0.8851522842639594
以上是第15次特征筛选。
第15次特征筛选，AUC值是： 0.726871443163578
X_train_iter_svc.shape is: (1257, 37)
X_test_iter_svc.shape is: (1257, 37)
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.639 (+/-0.326) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.615 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6166666666666667
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.722 (+/-0.417) for {'C': 1.0, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.651 (+/-0.385) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.641 (+/-0.235) for {'C': 1.0, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.613 (+/-0.241) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6411858974358975
RBF的粗grid search最佳超参： {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0} RBF的粗grid search最高分值： 0.6166666666666667
linear的粗grid search最佳超参： {'C': 1.0, 'kernel': 'linear'} linear的粗grid search最高分值： 0.6411858974358975
粗grid search得到的parameter是：
 [{'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05]), 'kernel': ['rbf'], 'gamma': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.639 (+/-0.396) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.374) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.672 (+/-0.377) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.651 (+/-0.385) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.672 (+/-0.377) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.651 (+/-0.385) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.651 (+/-0.385) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.689 (+/-0.436) for {'C': 10.0, 'kernel': 'linear'}
0.656 (+/-0.384) for {'C': 100.0, 'kernel': 'linear'}
0.651 (+/-0.385) for {'C': 1000.0, 'kernel': 'linear'}
0.651 (+/-0.385) for {'C': 10000.0, 'kernel': 'linear'}
0.651 (+/-0.385) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      1.00       623

avg / total       0.98      0.99      0.99       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.589 (+/-0.232) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.639 (+/-0.236) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.613 (+/-0.240) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.639 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.613 (+/-0.241) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.616 (+/-0.238) for {'C': 10.0, 'kernel': 'linear'}
0.613 (+/-0.242) for {'C': 100.0, 'kernel': 'linear'}
0.613 (+/-0.241) for {'C': 1000.0, 'kernel': 'linear'}
0.613 (+/-0.241) for {'C': 10000.0, 'kernel': 'linear'}
0.613 (+/-0.241) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.14      0.17      0.15         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6389423076923078
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.664 (+/-0.388) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.383) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.639 (+/-0.396) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.698 (+/-0.383) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.651 (+/-0.381) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.698 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.662 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.651 (+/-0.385) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.698 (+/-0.383) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.663 (+/-0.382) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.651 (+/-0.385) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.651 (+/-0.385) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.722 (+/-0.417) for {'C': 1.0, 'kernel': 'linear'}
0.654 (+/-0.379) for {'C': 10.0, 'kernel': 'linear'}
0.656 (+/-0.384) for {'C': 100.0, 'kernel': 'linear'}
0.651 (+/-0.385) for {'C': 1000.0, 'kernel': 'linear'}
0.651 (+/-0.385) for {'C': 10000.0, 'kernel': 'linear'}
0.651 (+/-0.385) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      1.00       623

avg / total       0.98      0.99      0.99       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.315) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.233) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.666 (+/-0.315) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.236) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.315) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.662 (+/-0.312) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.613 (+/-0.240) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.662 (+/-0.313) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.613 (+/-0.241) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.641 (+/-0.235) for {'C': 1.0, 'kernel': 'linear'}
0.660 (+/-0.315) for {'C': 10.0, 'kernel': 'linear'}
0.613 (+/-0.242) for {'C': 100.0, 'kernel': 'linear'}
0.613 (+/-0.241) for {'C': 1000.0, 'kernel': 'linear'}
0.613 (+/-0.241) for {'C': 10000.0, 'kernel': 'linear'}
0.613 (+/-0.241) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6658653846153846
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.747 (+/-0.502) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.747 (+/-0.502) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.747 (+/-0.502) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.747 (+/-0.502) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.747 (+/-0.502) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.747 (+/-0.502) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.747 (+/-0.502) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.747 (+/-0.502) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.697 (+/-0.437) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.747 (+/-0.502) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.747 (+/-0.502) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.747 (+/-0.502) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.747 (+/-0.502) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.747 (+/-0.502) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.747 (+/-0.502) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.747 (+/-0.502) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.697 (+/-0.437) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.664 (+/-0.388) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.747 (+/-0.502) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.747 (+/-0.502) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.747 (+/-0.502) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.747 (+/-0.502) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.747 (+/-0.502) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.747 (+/-0.502) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.697 (+/-0.437) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.664 (+/-0.388) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.664 (+/-0.388) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.747 (+/-0.502) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.747 (+/-0.502) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.747 (+/-0.502) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.747 (+/-0.502) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.747 (+/-0.502) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.697 (+/-0.437) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.664 (+/-0.388) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.664 (+/-0.388) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.664 (+/-0.388) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.747 (+/-0.502) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.747 (+/-0.502) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.747 (+/-0.502) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.747 (+/-0.502) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.697 (+/-0.437) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.664 (+/-0.388) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.664 (+/-0.388) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.689 (+/-0.436) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.664 (+/-0.388) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.747 (+/-0.502) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.747 (+/-0.502) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.747 (+/-0.502) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.697 (+/-0.437) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.664 (+/-0.388) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.664 (+/-0.388) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.689 (+/-0.436) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.664 (+/-0.388) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.639 (+/-0.396) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.747 (+/-0.502) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.722 (+/-0.473) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.697 (+/-0.437) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.664 (+/-0.388) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.664 (+/-0.388) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.689 (+/-0.436) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.664 (+/-0.388) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.645 (+/-0.388) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.639 (+/-0.396) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.722 (+/-0.473) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.697 (+/-0.437) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.664 (+/-0.388) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.664 (+/-0.388) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.689 (+/-0.436) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.664 (+/-0.388) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.647 (+/-0.387) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.639 (+/-0.396) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.639 (+/-0.396) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.001}
0.722 (+/-0.473) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.697 (+/-0.437) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.664 (+/-0.388) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.689 (+/-0.436) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.689 (+/-0.436) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.689 (+/-0.436) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.647 (+/-0.387) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.639 (+/-0.396) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.639 (+/-0.396) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.639 (+/-0.396) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.664 (+/-0.388) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.689 (+/-0.436) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.689 (+/-0.436) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.689 (+/-0.436) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.647 (+/-0.387) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.646 (+/-0.388) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.639 (+/-0.396) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.639 (+/-0.396) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.656 (+/-0.384) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.689 (+/-0.436) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.689 (+/-0.436) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.689 (+/-0.436) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.664 (+/-0.374) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.646 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.645 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.639 (+/-0.396) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.639 (+/-0.396) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.656 (+/-0.384) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.689 (+/-0.436) for {'C': 10.0, 'kernel': 'linear'}
0.656 (+/-0.384) for {'C': 100.0, 'kernel': 'linear'}
0.651 (+/-0.385) for {'C': 1000.0, 'kernel': 'linear'}
0.651 (+/-0.385) for {'C': 10000.0, 'kernel': 'linear'}
0.651 (+/-0.385) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      1.00       623

avg / total       0.98      0.99      0.99       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.617 (+/-0.238) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.617 (+/-0.238) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.617 (+/-0.238) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.617 (+/-0.238) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.617 (+/-0.238) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.617 (+/-0.238) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.617 (+/-0.238) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.617 (+/-0.238) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.616 (+/-0.237) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.617 (+/-0.238) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.617 (+/-0.238) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.617 (+/-0.238) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.617 (+/-0.238) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.617 (+/-0.238) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.617 (+/-0.238) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.617 (+/-0.238) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.616 (+/-0.237) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.616 (+/-0.237) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.617 (+/-0.238) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.617 (+/-0.238) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.617 (+/-0.238) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.617 (+/-0.238) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.617 (+/-0.238) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.617 (+/-0.238) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.616 (+/-0.237) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.616 (+/-0.237) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.616 (+/-0.237) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.617 (+/-0.238) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.617 (+/-0.238) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.617 (+/-0.238) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.617 (+/-0.238) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.617 (+/-0.238) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.616 (+/-0.237) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.616 (+/-0.237) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.616 (+/-0.237) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.616 (+/-0.237) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.617 (+/-0.238) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.617 (+/-0.238) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.617 (+/-0.238) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.617 (+/-0.238) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.616 (+/-0.237) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.616 (+/-0.237) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.616 (+/-0.237) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.616 (+/-0.238) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.615 (+/-0.239) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.617 (+/-0.238) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.617 (+/-0.238) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.617 (+/-0.238) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.616 (+/-0.237) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.616 (+/-0.237) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.616 (+/-0.237) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.616 (+/-0.238) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.615 (+/-0.239) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.589 (+/-0.232) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.617 (+/-0.238) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.617 (+/-0.238) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.616 (+/-0.237) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.616 (+/-0.237) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.616 (+/-0.237) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.616 (+/-0.238) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.615 (+/-0.239) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.613 (+/-0.238) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.589 (+/-0.232) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.617 (+/-0.238) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.616 (+/-0.237) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.616 (+/-0.237) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.616 (+/-0.237) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.616 (+/-0.238) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.615 (+/-0.239) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.614 (+/-0.238) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.588 (+/-0.232) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.588 (+/-0.232) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.616 (+/-0.237) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.616 (+/-0.237) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.616 (+/-0.237) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.616 (+/-0.238) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.615 (+/-0.240) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.614 (+/-0.238) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.588 (+/-0.233) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.588 (+/-0.232) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.589 (+/-0.232) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.616 (+/-0.237) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.616 (+/-0.237) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.616 (+/-0.238) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.615 (+/-0.240) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.614 (+/-0.238) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.613 (+/-0.239) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.588 (+/-0.233) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.589 (+/-0.232) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.614 (+/-0.240) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.616 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.615 (+/-0.240) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.639 (+/-0.236) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.613 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.613 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.588 (+/-0.233) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.589 (+/-0.231) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.614 (+/-0.240) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.616 (+/-0.238) for {'C': 10.0, 'kernel': 'linear'}
0.613 (+/-0.242) for {'C': 100.0, 'kernel': 'linear'}
0.613 (+/-0.241) for {'C': 1000.0, 'kernel': 'linear'}
0.613 (+/-0.241) for {'C': 10000.0, 'kernel': 'linear'}
0.613 (+/-0.241) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.14      0.17      0.15         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6387820512820513
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.747 (+/-0.502) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.747 (+/-0.502) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.747 (+/-0.502) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.747 (+/-0.502) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.747 (+/-0.502) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.747 (+/-0.502) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.747 (+/-0.502) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.697 (+/-0.437) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.664 (+/-0.388) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.664 (+/-0.388) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.747 (+/-0.502) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.747 (+/-0.502) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.747 (+/-0.502) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.747 (+/-0.502) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.747 (+/-0.502) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.697 (+/-0.437) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.664 (+/-0.388) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.664 (+/-0.388) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.674 (+/-0.375) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.747 (+/-0.502) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.747 (+/-0.502) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.747 (+/-0.502) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.747 (+/-0.502) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.697 (+/-0.437) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.681 (+/-0.372) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.689 (+/-0.374) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.681 (+/-0.372) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.641 (+/-0.307) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.747 (+/-0.502) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.747 (+/-0.502) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.747 (+/-0.502) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.747 (+/-0.449) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.681 (+/-0.372) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.698 (+/-0.383) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.681 (+/-0.372) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.635 (+/-0.308) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.619 (+/-0.321) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.747 (+/-0.502) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.747 (+/-0.502) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.747 (+/-0.449) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.681 (+/-0.372) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.698 (+/-0.383) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.681 (+/-0.372) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.641 (+/-0.306) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.644 (+/-0.390) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.647 (+/-0.387) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.747 (+/-0.502) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.747 (+/-0.449) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.681 (+/-0.372) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.698 (+/-0.383) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.681 (+/-0.372) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.645 (+/-0.306) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.648 (+/-0.386) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.646 (+/-0.388) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.639 (+/-0.396) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.747 (+/-0.449) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.681 (+/-0.372) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.698 (+/-0.383) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.681 (+/-0.372) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.645 (+/-0.306) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.654 (+/-0.379) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.652 (+/-0.385) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.645 (+/-0.388) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.639 (+/-0.396) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.449) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.681 (+/-0.372) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.698 (+/-0.383) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.681 (+/-0.372) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.645 (+/-0.306) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.653 (+/-0.379) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.649 (+/-0.386) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.644 (+/-0.389) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.639 (+/-0.396) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.639 (+/-0.396) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.449) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.001}
0.681 (+/-0.372) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.698 (+/-0.383) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.681 (+/-0.372) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.670 (+/-0.370) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.653 (+/-0.379) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.657 (+/-0.376) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.644 (+/-0.389) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.639 (+/-0.396) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.639 (+/-0.396) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.639 (+/-0.396) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.1}
0.681 (+/-0.372) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.383) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.681 (+/-0.372) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.666 (+/-0.371) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.653 (+/-0.379) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.657 (+/-0.376) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.653 (+/-0.379) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.646 (+/-0.388) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.639 (+/-0.396) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.639 (+/-0.396) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.656 (+/-0.384) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.1}
0.698 (+/-0.383) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.681 (+/-0.372) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.666 (+/-0.371) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.653 (+/-0.379) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.656 (+/-0.377) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.651 (+/-0.381) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.646 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.645 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.639 (+/-0.396) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.639 (+/-0.396) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.656 (+/-0.384) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.722 (+/-0.417) for {'C': 1.0, 'kernel': 'linear'}
0.654 (+/-0.379) for {'C': 10.0, 'kernel': 'linear'}
0.656 (+/-0.384) for {'C': 100.0, 'kernel': 'linear'}
0.651 (+/-0.385) for {'C': 1000.0, 'kernel': 'linear'}
0.651 (+/-0.385) for {'C': 10000.0, 'kernel': 'linear'}
0.651 (+/-0.385) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.617 (+/-0.238) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.617 (+/-0.238) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.617 (+/-0.238) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.617 (+/-0.238) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.617 (+/-0.238) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.617 (+/-0.238) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.617 (+/-0.238) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.616 (+/-0.237) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.616 (+/-0.237) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.616 (+/-0.237) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.617 (+/-0.238) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.617 (+/-0.238) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.617 (+/-0.238) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.617 (+/-0.238) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.617 (+/-0.238) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.616 (+/-0.237) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.616 (+/-0.237) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.616 (+/-0.237) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.640 (+/-0.234) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.617 (+/-0.238) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.617 (+/-0.238) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.617 (+/-0.238) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.617 (+/-0.238) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.616 (+/-0.237) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.641 (+/-0.235) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.641 (+/-0.235) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.665 (+/-0.313) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.639 (+/-0.235) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.617 (+/-0.238) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.617 (+/-0.238) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.617 (+/-0.238) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.641 (+/-0.236) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.666 (+/-0.314) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.666 (+/-0.315) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.665 (+/-0.313) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.639 (+/-0.235) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.612 (+/-0.240) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.617 (+/-0.238) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.617 (+/-0.238) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.641 (+/-0.236) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.666 (+/-0.314) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.666 (+/-0.315) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.665 (+/-0.313) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.664 (+/-0.313) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.612 (+/-0.240) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.613 (+/-0.240) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.617 (+/-0.238) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.641 (+/-0.236) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.666 (+/-0.314) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.666 (+/-0.315) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.665 (+/-0.313) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.664 (+/-0.313) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.636 (+/-0.325) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.613 (+/-0.240) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.588 (+/-0.233) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.641 (+/-0.236) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.666 (+/-0.314) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.666 (+/-0.315) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.665 (+/-0.313) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.664 (+/-0.313) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.661 (+/-0.314) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.637 (+/-0.325) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.613 (+/-0.239) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.589 (+/-0.232) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.001}
0.641 (+/-0.236) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.666 (+/-0.314) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.666 (+/-0.315) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.665 (+/-0.313) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.664 (+/-0.313) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.660 (+/-0.313) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.637 (+/-0.324) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.613 (+/-0.239) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.588 (+/-0.232) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.588 (+/-0.232) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.1}
0.641 (+/-0.236) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.314) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.666 (+/-0.315) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.665 (+/-0.313) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.664 (+/-0.313) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.660 (+/-0.314) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.662 (+/-0.314) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.612 (+/-0.239) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.588 (+/-0.233) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.588 (+/-0.232) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.589 (+/-0.232) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.1}
0.666 (+/-0.314) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.315) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.665 (+/-0.313) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.664 (+/-0.314) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.660 (+/-0.314) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.662 (+/-0.314) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.637 (+/-0.236) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.613 (+/-0.239) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.588 (+/-0.233) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.589 (+/-0.232) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.614 (+/-0.240) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.1}
0.666 (+/-0.315) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.665 (+/-0.313) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.664 (+/-0.314) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.660 (+/-0.314) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.661 (+/-0.313) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.637 (+/-0.236) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.613 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.613 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.588 (+/-0.233) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.589 (+/-0.231) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.614 (+/-0.240) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.641 (+/-0.235) for {'C': 1.0, 'kernel': 'linear'}
0.660 (+/-0.315) for {'C': 10.0, 'kernel': 'linear'}
0.613 (+/-0.242) for {'C': 100.0, 'kernel': 'linear'}
0.613 (+/-0.241) for {'C': 1000.0, 'kernel': 'linear'}
0.613 (+/-0.241) for {'C': 10000.0, 'kernel': 'linear'}
0.613 (+/-0.241) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6658653846153846
循环迭代之前，delta is: [6.01892829e+01 1.51188643e-02]
执行tunemodel()函数前，使用的grid search parameter是：
 [{'C': array([25.11886432, 27.54228703, 30.1995172 , 33.11311215, 36.30780548,
       39.81071706, 43.65158322, 47.86300923, 52.48074602, 57.54399373,
       63.09573445]), 'kernel': ['rbf'], 'gamma': array([0.01584893, 0.01737801, 0.01905461, 0.02089296, 0.02290868,
       0.02511886, 0.02754229, 0.03019952, 0.03311311, 0.03630781,
       0.03981072])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.747 (+/-0.502) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.747 (+/-0.502) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.017378008287493755}
0.747 (+/-0.502) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.01905460717963247}
0.747 (+/-0.502) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.020892961308540393}
0.747 (+/-0.502) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.022908676527677727}
0.747 (+/-0.502) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.747 (+/-0.502) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.027542287033381657}
0.697 (+/-0.437) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.03019951720402017}
0.697 (+/-0.437) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.03311311214825912}
0.697 (+/-0.437) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.036307805477010145}
0.697 (+/-0.437) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.747 (+/-0.502) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.747 (+/-0.502) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 0.017378008287493755}
0.747 (+/-0.502) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 0.01905460717963247}
0.747 (+/-0.502) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 0.020892961308540393}
0.747 (+/-0.502) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 0.022908676527677727}
0.747 (+/-0.502) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.697 (+/-0.437) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 0.027542287033381657}
0.697 (+/-0.437) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 0.03019951720402017}
0.697 (+/-0.437) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 0.03311311214825912}
0.697 (+/-0.437) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 0.036307805477010145}
0.697 (+/-0.437) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.747 (+/-0.502) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.747 (+/-0.502) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 0.017378008287493755}
0.747 (+/-0.502) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 0.01905460717963247}
0.747 (+/-0.502) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 0.020892961308540393}
0.722 (+/-0.473) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 0.022908676527677727}
0.697 (+/-0.437) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.697 (+/-0.437) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 0.027542287033381657}
0.697 (+/-0.437) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 0.03019951720402017}
0.697 (+/-0.437) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 0.03311311214825912}
0.697 (+/-0.437) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 0.036307805477010145}
0.697 (+/-0.437) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.747 (+/-0.502) for {'C': 33.1131121482591, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.747 (+/-0.502) for {'C': 33.1131121482591, 'kernel': 'rbf', 'gamma': 0.017378008287493755}
0.747 (+/-0.502) for {'C': 33.1131121482591, 'kernel': 'rbf', 'gamma': 0.01905460717963247}
0.722 (+/-0.473) for {'C': 33.1131121482591, 'kernel': 'rbf', 'gamma': 0.020892961308540393}
0.697 (+/-0.437) for {'C': 33.1131121482591, 'kernel': 'rbf', 'gamma': 0.022908676527677727}
0.697 (+/-0.437) for {'C': 33.1131121482591, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.697 (+/-0.437) for {'C': 33.1131121482591, 'kernel': 'rbf', 'gamma': 0.027542287033381657}
0.697 (+/-0.437) for {'C': 33.1131121482591, 'kernel': 'rbf', 'gamma': 0.03019951720402017}
0.697 (+/-0.437) for {'C': 33.1131121482591, 'kernel': 'rbf', 'gamma': 0.03311311214825912}
0.697 (+/-0.437) for {'C': 33.1131121482591, 'kernel': 'rbf', 'gamma': 0.036307805477010145}
0.689 (+/-0.436) for {'C': 33.1131121482591, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.747 (+/-0.502) for {'C': 36.30780547701012, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.747 (+/-0.502) for {'C': 36.30780547701012, 'kernel': 'rbf', 'gamma': 0.017378008287493755}
0.697 (+/-0.437) for {'C': 36.30780547701012, 'kernel': 'rbf', 'gamma': 0.01905460717963247}
0.697 (+/-0.437) for {'C': 36.30780547701012, 'kernel': 'rbf', 'gamma': 0.020892961308540393}
0.697 (+/-0.437) for {'C': 36.30780547701012, 'kernel': 'rbf', 'gamma': 0.022908676527677727}
0.697 (+/-0.437) for {'C': 36.30780547701012, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.697 (+/-0.437) for {'C': 36.30780547701012, 'kernel': 'rbf', 'gamma': 0.027542287033381657}
0.697 (+/-0.437) for {'C': 36.30780547701012, 'kernel': 'rbf', 'gamma': 0.03019951720402017}
0.697 (+/-0.437) for {'C': 36.30780547701012, 'kernel': 'rbf', 'gamma': 0.03311311214825912}
0.689 (+/-0.436) for {'C': 36.30780547701012, 'kernel': 'rbf', 'gamma': 0.036307805477010145}
0.689 (+/-0.436) for {'C': 36.30780547701012, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.747 (+/-0.502) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.697 (+/-0.437) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.017378008287493755}
0.697 (+/-0.437) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.01905460717963247}
0.697 (+/-0.437) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.020892961308540393}
0.697 (+/-0.437) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.022908676527677727}
0.697 (+/-0.437) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.697 (+/-0.437) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.027542287033381657}
0.697 (+/-0.437) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.03019951720402017}
0.689 (+/-0.436) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.03311311214825912}
0.689 (+/-0.436) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.036307805477010145}
0.664 (+/-0.388) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.697 (+/-0.437) for {'C': 43.651583224016605, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.697 (+/-0.437) for {'C': 43.651583224016605, 'kernel': 'rbf', 'gamma': 0.017378008287493755}
0.697 (+/-0.437) for {'C': 43.651583224016605, 'kernel': 'rbf', 'gamma': 0.01905460717963247}
0.697 (+/-0.437) for {'C': 43.651583224016605, 'kernel': 'rbf', 'gamma': 0.020892961308540393}
0.697 (+/-0.437) for {'C': 43.651583224016605, 'kernel': 'rbf', 'gamma': 0.022908676527677727}
0.697 (+/-0.437) for {'C': 43.651583224016605, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.697 (+/-0.437) for {'C': 43.651583224016605, 'kernel': 'rbf', 'gamma': 0.027542287033381657}
0.689 (+/-0.436) for {'C': 43.651583224016605, 'kernel': 'rbf', 'gamma': 0.03019951720402017}
0.689 (+/-0.436) for {'C': 43.651583224016605, 'kernel': 'rbf', 'gamma': 0.03311311214825912}
0.664 (+/-0.388) for {'C': 43.651583224016605, 'kernel': 'rbf', 'gamma': 0.036307805477010145}
0.664 (+/-0.388) for {'C': 43.651583224016605, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.697 (+/-0.437) for {'C': 47.86300923226384, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.697 (+/-0.437) for {'C': 47.86300923226384, 'kernel': 'rbf', 'gamma': 0.017378008287493755}
0.697 (+/-0.437) for {'C': 47.86300923226384, 'kernel': 'rbf', 'gamma': 0.01905460717963247}
0.697 (+/-0.437) for {'C': 47.86300923226384, 'kernel': 'rbf', 'gamma': 0.020892961308540393}
0.697 (+/-0.437) for {'C': 47.86300923226384, 'kernel': 'rbf', 'gamma': 0.022908676527677727}
0.697 (+/-0.437) for {'C': 47.86300923226384, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.689 (+/-0.436) for {'C': 47.86300923226384, 'kernel': 'rbf', 'gamma': 0.027542287033381657}
0.689 (+/-0.436) for {'C': 47.86300923226384, 'kernel': 'rbf', 'gamma': 0.03019951720402017}
0.664 (+/-0.388) for {'C': 47.86300923226384, 'kernel': 'rbf', 'gamma': 0.03311311214825912}
0.664 (+/-0.388) for {'C': 47.86300923226384, 'kernel': 'rbf', 'gamma': 0.036307805477010145}
0.664 (+/-0.388) for {'C': 47.86300923226384, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.697 (+/-0.437) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.697 (+/-0.437) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.017378008287493755}
0.697 (+/-0.437) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.01905460717963247}
0.697 (+/-0.437) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.020892961308540393}
0.697 (+/-0.437) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.022908676527677727}
0.689 (+/-0.436) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.689 (+/-0.436) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.027542287033381657}
0.664 (+/-0.388) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.03019951720402017}
0.664 (+/-0.388) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.03311311214825912}
0.664 (+/-0.388) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.036307805477010145}
0.664 (+/-0.388) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.697 (+/-0.437) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.697 (+/-0.437) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.017378008287493755}
0.697 (+/-0.437) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.01905460717963247}
0.697 (+/-0.437) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.020892961308540393}
0.689 (+/-0.436) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.022908676527677727}
0.689 (+/-0.436) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.664 (+/-0.388) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.027542287033381657}
0.664 (+/-0.388) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.03019951720402017}
0.664 (+/-0.388) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.03311311214825912}
0.664 (+/-0.388) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.036307805477010145}
0.664 (+/-0.388) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.697 (+/-0.437) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.697 (+/-0.437) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.017378008287493755}
0.697 (+/-0.437) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.01905460717963247}
0.689 (+/-0.436) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.020892961308540393}
0.689 (+/-0.436) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.022908676527677727}
0.664 (+/-0.388) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.664 (+/-0.388) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.027542287033381657}
0.664 (+/-0.388) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.03019951720402017}
0.664 (+/-0.388) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.03311311214825912}
0.664 (+/-0.388) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.036307805477010145}
0.664 (+/-0.388) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.689 (+/-0.436) for {'C': 10.0, 'kernel': 'linear'}
0.656 (+/-0.384) for {'C': 100.0, 'kernel': 'linear'}
0.651 (+/-0.385) for {'C': 1000.0, 'kernel': 'linear'}
0.651 (+/-0.385) for {'C': 10000.0, 'kernel': 'linear'}
0.651 (+/-0.385) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.617 (+/-0.238) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.617 (+/-0.238) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.017378008287493755}
0.617 (+/-0.238) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.01905460717963247}
0.617 (+/-0.238) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.020892961308540393}
0.617 (+/-0.238) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.022908676527677727}
0.617 (+/-0.238) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.617 (+/-0.238) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.027542287033381657}
0.616 (+/-0.237) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.03019951720402017}
0.616 (+/-0.237) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.03311311214825912}
0.616 (+/-0.237) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.036307805477010145}
0.616 (+/-0.237) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.617 (+/-0.238) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.617 (+/-0.238) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 0.017378008287493755}
0.617 (+/-0.238) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 0.01905460717963247}
0.617 (+/-0.238) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 0.020892961308540393}
0.617 (+/-0.238) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 0.022908676527677727}
0.617 (+/-0.238) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.616 (+/-0.237) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 0.027542287033381657}
0.616 (+/-0.237) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 0.03019951720402017}
0.616 (+/-0.237) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 0.03311311214825912}
0.616 (+/-0.237) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 0.036307805477010145}
0.616 (+/-0.237) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.617 (+/-0.238) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.617 (+/-0.238) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 0.017378008287493755}
0.617 (+/-0.238) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 0.01905460717963247}
0.617 (+/-0.238) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 0.020892961308540393}
0.617 (+/-0.238) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 0.022908676527677727}
0.616 (+/-0.237) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.616 (+/-0.237) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 0.027542287033381657}
0.616 (+/-0.237) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 0.03019951720402017}
0.616 (+/-0.237) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 0.03311311214825912}
0.616 (+/-0.237) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 0.036307805477010145}
0.616 (+/-0.237) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.617 (+/-0.238) for {'C': 33.1131121482591, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.617 (+/-0.238) for {'C': 33.1131121482591, 'kernel': 'rbf', 'gamma': 0.017378008287493755}
0.617 (+/-0.238) for {'C': 33.1131121482591, 'kernel': 'rbf', 'gamma': 0.01905460717963247}
0.617 (+/-0.238) for {'C': 33.1131121482591, 'kernel': 'rbf', 'gamma': 0.020892961308540393}
0.616 (+/-0.237) for {'C': 33.1131121482591, 'kernel': 'rbf', 'gamma': 0.022908676527677727}
0.616 (+/-0.237) for {'C': 33.1131121482591, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.616 (+/-0.237) for {'C': 33.1131121482591, 'kernel': 'rbf', 'gamma': 0.027542287033381657}
0.616 (+/-0.237) for {'C': 33.1131121482591, 'kernel': 'rbf', 'gamma': 0.03019951720402017}
0.616 (+/-0.237) for {'C': 33.1131121482591, 'kernel': 'rbf', 'gamma': 0.03311311214825912}
0.616 (+/-0.237) for {'C': 33.1131121482591, 'kernel': 'rbf', 'gamma': 0.036307805477010145}
0.616 (+/-0.237) for {'C': 33.1131121482591, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.617 (+/-0.238) for {'C': 36.30780547701012, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.617 (+/-0.238) for {'C': 36.30780547701012, 'kernel': 'rbf', 'gamma': 0.017378008287493755}
0.616 (+/-0.237) for {'C': 36.30780547701012, 'kernel': 'rbf', 'gamma': 0.01905460717963247}
0.616 (+/-0.237) for {'C': 36.30780547701012, 'kernel': 'rbf', 'gamma': 0.020892961308540393}
0.616 (+/-0.237) for {'C': 36.30780547701012, 'kernel': 'rbf', 'gamma': 0.022908676527677727}
0.616 (+/-0.237) for {'C': 36.30780547701012, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.616 (+/-0.237) for {'C': 36.30780547701012, 'kernel': 'rbf', 'gamma': 0.027542287033381657}
0.616 (+/-0.237) for {'C': 36.30780547701012, 'kernel': 'rbf', 'gamma': 0.03019951720402017}
0.616 (+/-0.237) for {'C': 36.30780547701012, 'kernel': 'rbf', 'gamma': 0.03311311214825912}
0.616 (+/-0.237) for {'C': 36.30780547701012, 'kernel': 'rbf', 'gamma': 0.036307805477010145}
0.616 (+/-0.237) for {'C': 36.30780547701012, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.617 (+/-0.238) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.616 (+/-0.237) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.017378008287493755}
0.616 (+/-0.237) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.01905460717963247}
0.616 (+/-0.237) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.020892961308540393}
0.616 (+/-0.237) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.022908676527677727}
0.616 (+/-0.237) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.616 (+/-0.237) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.027542287033381657}
0.616 (+/-0.237) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.03019951720402017}
0.616 (+/-0.237) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.03311311214825912}
0.616 (+/-0.237) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.036307805477010145}
0.616 (+/-0.237) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.616 (+/-0.237) for {'C': 43.651583224016605, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.616 (+/-0.237) for {'C': 43.651583224016605, 'kernel': 'rbf', 'gamma': 0.017378008287493755}
0.616 (+/-0.237) for {'C': 43.651583224016605, 'kernel': 'rbf', 'gamma': 0.01905460717963247}
0.616 (+/-0.237) for {'C': 43.651583224016605, 'kernel': 'rbf', 'gamma': 0.020892961308540393}
0.616 (+/-0.237) for {'C': 43.651583224016605, 'kernel': 'rbf', 'gamma': 0.022908676527677727}
0.616 (+/-0.237) for {'C': 43.651583224016605, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.616 (+/-0.237) for {'C': 43.651583224016605, 'kernel': 'rbf', 'gamma': 0.027542287033381657}
0.616 (+/-0.237) for {'C': 43.651583224016605, 'kernel': 'rbf', 'gamma': 0.03019951720402017}
0.616 (+/-0.237) for {'C': 43.651583224016605, 'kernel': 'rbf', 'gamma': 0.03311311214825912}
0.616 (+/-0.237) for {'C': 43.651583224016605, 'kernel': 'rbf', 'gamma': 0.036307805477010145}
0.616 (+/-0.237) for {'C': 43.651583224016605, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.616 (+/-0.237) for {'C': 47.86300923226384, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.616 (+/-0.237) for {'C': 47.86300923226384, 'kernel': 'rbf', 'gamma': 0.017378008287493755}
0.616 (+/-0.237) for {'C': 47.86300923226384, 'kernel': 'rbf', 'gamma': 0.01905460717963247}
0.616 (+/-0.237) for {'C': 47.86300923226384, 'kernel': 'rbf', 'gamma': 0.020892961308540393}
0.616 (+/-0.237) for {'C': 47.86300923226384, 'kernel': 'rbf', 'gamma': 0.022908676527677727}
0.616 (+/-0.237) for {'C': 47.86300923226384, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.616 (+/-0.237) for {'C': 47.86300923226384, 'kernel': 'rbf', 'gamma': 0.027542287033381657}
0.616 (+/-0.237) for {'C': 47.86300923226384, 'kernel': 'rbf', 'gamma': 0.03019951720402017}
0.616 (+/-0.237) for {'C': 47.86300923226384, 'kernel': 'rbf', 'gamma': 0.03311311214825912}
0.616 (+/-0.237) for {'C': 47.86300923226384, 'kernel': 'rbf', 'gamma': 0.036307805477010145}
0.616 (+/-0.237) for {'C': 47.86300923226384, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.616 (+/-0.237) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.616 (+/-0.237) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.017378008287493755}
0.616 (+/-0.237) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.01905460717963247}
0.616 (+/-0.237) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.020892961308540393}
0.616 (+/-0.237) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.022908676527677727}
0.616 (+/-0.237) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.616 (+/-0.237) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.027542287033381657}
0.616 (+/-0.237) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.03019951720402017}
0.616 (+/-0.237) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.03311311214825912}
0.616 (+/-0.237) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.036307805477010145}
0.616 (+/-0.237) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.616 (+/-0.237) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.616 (+/-0.237) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.017378008287493755}
0.616 (+/-0.237) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.01905460717963247}
0.616 (+/-0.237) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.020892961308540393}
0.616 (+/-0.237) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.022908676527677727}
0.616 (+/-0.237) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.616 (+/-0.237) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.027542287033381657}
0.616 (+/-0.237) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.03019951720402017}
0.616 (+/-0.237) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.03311311214825912}
0.616 (+/-0.237) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.036307805477010145}
0.616 (+/-0.237) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.616 (+/-0.237) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.616 (+/-0.237) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.017378008287493755}
0.616 (+/-0.237) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.01905460717963247}
0.616 (+/-0.237) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.020892961308540393}
0.616 (+/-0.237) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.022908676527677727}
0.616 (+/-0.237) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.616 (+/-0.237) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.027542287033381657}
0.616 (+/-0.237) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.03019951720402017}
0.616 (+/-0.237) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.03311311214825912}
0.616 (+/-0.237) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.036307805477010145}
0.616 (+/-0.237) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.616 (+/-0.238) for {'C': 10.0, 'kernel': 'linear'}
0.613 (+/-0.242) for {'C': 100.0, 'kernel': 'linear'}
0.613 (+/-0.241) for {'C': 1000.0, 'kernel': 'linear'}
0.613 (+/-0.241) for {'C': 10000.0, 'kernel': 'linear'}
0.613 (+/-0.241) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6166666666666667
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.697 (+/-0.437) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.747 (+/-0.449) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.017378008287493755}
0.747 (+/-0.449) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.01905460717963247}
0.714 (+/-0.418) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.020892961308540393}
0.681 (+/-0.372) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.022908676527677727}
0.681 (+/-0.372) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.681 (+/-0.372) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.027542287033381657}
0.664 (+/-0.388) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.03019951720402017}
0.664 (+/-0.388) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.03311311214825912}
0.689 (+/-0.374) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.036307805477010145}
0.689 (+/-0.374) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.747 (+/-0.449) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.747 (+/-0.449) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 0.017378008287493755}
0.689 (+/-0.374) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 0.01905460717963247}
0.681 (+/-0.372) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 0.020892961308540393}
0.681 (+/-0.372) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 0.022908676527677727}
0.681 (+/-0.372) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.664 (+/-0.388) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 0.027542287033381657}
0.664 (+/-0.388) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 0.03019951720402017}
0.689 (+/-0.374) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 0.03311311214825912}
0.689 (+/-0.374) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 0.036307805477010145}
0.698 (+/-0.383) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.747 (+/-0.449) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.689 (+/-0.374) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 0.017378008287493755}
0.681 (+/-0.372) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 0.01905460717963247}
0.681 (+/-0.372) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 0.020892961308540393}
0.681 (+/-0.372) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 0.022908676527677727}
0.681 (+/-0.372) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.664 (+/-0.388) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 0.027542287033381657}
0.698 (+/-0.383) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 0.03019951720402017}
0.698 (+/-0.383) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 0.03311311214825912}
0.698 (+/-0.383) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 0.036307805477010145}
0.698 (+/-0.383) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.689 (+/-0.374) for {'C': 33.1131121482591, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.689 (+/-0.382) for {'C': 33.1131121482591, 'kernel': 'rbf', 'gamma': 0.017378008287493755}
0.681 (+/-0.372) for {'C': 33.1131121482591, 'kernel': 'rbf', 'gamma': 0.01905460717963247}
0.681 (+/-0.372) for {'C': 33.1131121482591, 'kernel': 'rbf', 'gamma': 0.020892961308540393}
0.681 (+/-0.372) for {'C': 33.1131121482591, 'kernel': 'rbf', 'gamma': 0.022908676527677727}
0.664 (+/-0.388) for {'C': 33.1131121482591, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.698 (+/-0.383) for {'C': 33.1131121482591, 'kernel': 'rbf', 'gamma': 0.027542287033381657}
0.698 (+/-0.383) for {'C': 33.1131121482591, 'kernel': 'rbf', 'gamma': 0.03019951720402017}
0.698 (+/-0.383) for {'C': 33.1131121482591, 'kernel': 'rbf', 'gamma': 0.03311311214825912}
0.698 (+/-0.383) for {'C': 33.1131121482591, 'kernel': 'rbf', 'gamma': 0.036307805477010145}
0.698 (+/-0.383) for {'C': 33.1131121482591, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.689 (+/-0.382) for {'C': 36.30780547701012, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.681 (+/-0.372) for {'C': 36.30780547701012, 'kernel': 'rbf', 'gamma': 0.017378008287493755}
0.681 (+/-0.372) for {'C': 36.30780547701012, 'kernel': 'rbf', 'gamma': 0.01905460717963247}
0.681 (+/-0.372) for {'C': 36.30780547701012, 'kernel': 'rbf', 'gamma': 0.020892961308540393}
0.681 (+/-0.372) for {'C': 36.30780547701012, 'kernel': 'rbf', 'gamma': 0.022908676527677727}
0.698 (+/-0.383) for {'C': 36.30780547701012, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.698 (+/-0.383) for {'C': 36.30780547701012, 'kernel': 'rbf', 'gamma': 0.027542287033381657}
0.698 (+/-0.383) for {'C': 36.30780547701012, 'kernel': 'rbf', 'gamma': 0.03019951720402017}
0.698 (+/-0.383) for {'C': 36.30780547701012, 'kernel': 'rbf', 'gamma': 0.03311311214825912}
0.698 (+/-0.383) for {'C': 36.30780547701012, 'kernel': 'rbf', 'gamma': 0.036307805477010145}
0.684 (+/-0.372) for {'C': 36.30780547701012, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.681 (+/-0.372) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.681 (+/-0.372) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.017378008287493755}
0.681 (+/-0.372) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.01905460717963247}
0.689 (+/-0.374) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.020892961308540393}
0.698 (+/-0.383) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.022908676527677727}
0.698 (+/-0.383) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.698 (+/-0.383) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.027542287033381657}
0.698 (+/-0.383) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.03019951720402017}
0.698 (+/-0.383) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.03311311214825912}
0.684 (+/-0.372) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.036307805477010145}
0.681 (+/-0.372) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.681 (+/-0.372) for {'C': 43.651583224016605, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.689 (+/-0.374) for {'C': 43.651583224016605, 'kernel': 'rbf', 'gamma': 0.017378008287493755}
0.689 (+/-0.374) for {'C': 43.651583224016605, 'kernel': 'rbf', 'gamma': 0.01905460717963247}
0.698 (+/-0.383) for {'C': 43.651583224016605, 'kernel': 'rbf', 'gamma': 0.020892961308540393}
0.698 (+/-0.383) for {'C': 43.651583224016605, 'kernel': 'rbf', 'gamma': 0.022908676527677727}
0.698 (+/-0.383) for {'C': 43.651583224016605, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.698 (+/-0.383) for {'C': 43.651583224016605, 'kernel': 'rbf', 'gamma': 0.027542287033381657}
0.698 (+/-0.383) for {'C': 43.651583224016605, 'kernel': 'rbf', 'gamma': 0.03019951720402017}
0.684 (+/-0.372) for {'C': 43.651583224016605, 'kernel': 'rbf', 'gamma': 0.03311311214825912}
0.681 (+/-0.372) for {'C': 43.651583224016605, 'kernel': 'rbf', 'gamma': 0.036307805477010145}
0.681 (+/-0.372) for {'C': 43.651583224016605, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.689 (+/-0.374) for {'C': 47.86300923226384, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.689 (+/-0.374) for {'C': 47.86300923226384, 'kernel': 'rbf', 'gamma': 0.017378008287493755}
0.698 (+/-0.383) for {'C': 47.86300923226384, 'kernel': 'rbf', 'gamma': 0.01905460717963247}
0.698 (+/-0.383) for {'C': 47.86300923226384, 'kernel': 'rbf', 'gamma': 0.020892961308540393}
0.698 (+/-0.383) for {'C': 47.86300923226384, 'kernel': 'rbf', 'gamma': 0.022908676527677727}
0.698 (+/-0.383) for {'C': 47.86300923226384, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.698 (+/-0.383) for {'C': 47.86300923226384, 'kernel': 'rbf', 'gamma': 0.027542287033381657}
0.684 (+/-0.372) for {'C': 47.86300923226384, 'kernel': 'rbf', 'gamma': 0.03019951720402017}
0.681 (+/-0.372) for {'C': 47.86300923226384, 'kernel': 'rbf', 'gamma': 0.03311311214825912}
0.681 (+/-0.372) for {'C': 47.86300923226384, 'kernel': 'rbf', 'gamma': 0.036307805477010145}
0.656 (+/-0.312) for {'C': 47.86300923226384, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.689 (+/-0.374) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.698 (+/-0.383) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.017378008287493755}
0.698 (+/-0.383) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.01905460717963247}
0.698 (+/-0.383) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.020892961308540393}
0.698 (+/-0.383) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.022908676527677727}
0.698 (+/-0.383) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.684 (+/-0.372) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.027542287033381657}
0.681 (+/-0.372) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.03019951720402017}
0.681 (+/-0.372) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.03311311214825912}
0.656 (+/-0.312) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.036307805477010145}
0.648 (+/-0.306) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.698 (+/-0.383) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.698 (+/-0.383) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.017378008287493755}
0.698 (+/-0.383) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.01905460717963247}
0.698 (+/-0.383) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.020892961308540393}
0.698 (+/-0.383) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.022908676527677727}
0.684 (+/-0.372) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.681 (+/-0.372) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.027542287033381657}
0.681 (+/-0.372) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.03019951720402017}
0.656 (+/-0.312) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.03311311214825912}
0.648 (+/-0.306) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.036307805477010145}
0.643 (+/-0.306) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.698 (+/-0.383) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.698 (+/-0.383) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.017378008287493755}
0.698 (+/-0.383) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.01905460717963247}
0.698 (+/-0.383) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.020892961308540393}
0.684 (+/-0.372) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.022908676527677727}
0.681 (+/-0.372) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.681 (+/-0.372) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.027542287033381657}
0.656 (+/-0.312) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.03019951720402017}
0.648 (+/-0.306) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.03311311214825912}
0.648 (+/-0.306) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.036307805477010145}
0.641 (+/-0.306) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.722 (+/-0.417) for {'C': 1.0, 'kernel': 'linear'}
0.654 (+/-0.379) for {'C': 10.0, 'kernel': 'linear'}
0.656 (+/-0.384) for {'C': 100.0, 'kernel': 'linear'}
0.651 (+/-0.385) for {'C': 1000.0, 'kernel': 'linear'}
0.651 (+/-0.385) for {'C': 10000.0, 'kernel': 'linear'}
0.651 (+/-0.385) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.616 (+/-0.237) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.641 (+/-0.236) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.017378008287493755}
0.641 (+/-0.236) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.01905460717963247}
0.641 (+/-0.235) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.020892961308540393}
0.641 (+/-0.235) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.022908676527677727}
0.641 (+/-0.235) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.641 (+/-0.235) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.027542287033381657}
0.616 (+/-0.237) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.03019951720402017}
0.616 (+/-0.237) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.03311311214825912}
0.641 (+/-0.235) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.036307805477010145}
0.641 (+/-0.235) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.641 (+/-0.236) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.641 (+/-0.236) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 0.017378008287493755}
0.641 (+/-0.235) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 0.01905460717963247}
0.641 (+/-0.235) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 0.020892961308540393}
0.641 (+/-0.235) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 0.022908676527677727}
0.641 (+/-0.235) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.616 (+/-0.237) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 0.027542287033381657}
0.616 (+/-0.237) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 0.03019951720402017}
0.641 (+/-0.235) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 0.03311311214825912}
0.641 (+/-0.235) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 0.036307805477010145}
0.666 (+/-0.315) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.641 (+/-0.236) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.641 (+/-0.235) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 0.017378008287493755}
0.641 (+/-0.235) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 0.01905460717963247}
0.641 (+/-0.235) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 0.020892961308540393}
0.641 (+/-0.235) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 0.022908676527677727}
0.641 (+/-0.235) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.616 (+/-0.237) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 0.027542287033381657}
0.666 (+/-0.315) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 0.03019951720402017}
0.666 (+/-0.315) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 0.03311311214825912}
0.666 (+/-0.315) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 0.036307805477010145}
0.666 (+/-0.315) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.641 (+/-0.235) for {'C': 33.1131121482591, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.666 (+/-0.315) for {'C': 33.1131121482591, 'kernel': 'rbf', 'gamma': 0.017378008287493755}
0.641 (+/-0.235) for {'C': 33.1131121482591, 'kernel': 'rbf', 'gamma': 0.01905460717963247}
0.641 (+/-0.235) for {'C': 33.1131121482591, 'kernel': 'rbf', 'gamma': 0.020892961308540393}
0.641 (+/-0.235) for {'C': 33.1131121482591, 'kernel': 'rbf', 'gamma': 0.022908676527677727}
0.616 (+/-0.237) for {'C': 33.1131121482591, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.666 (+/-0.315) for {'C': 33.1131121482591, 'kernel': 'rbf', 'gamma': 0.027542287033381657}
0.666 (+/-0.315) for {'C': 33.1131121482591, 'kernel': 'rbf', 'gamma': 0.03019951720402017}
0.666 (+/-0.315) for {'C': 33.1131121482591, 'kernel': 'rbf', 'gamma': 0.03311311214825912}
0.666 (+/-0.315) for {'C': 33.1131121482591, 'kernel': 'rbf', 'gamma': 0.036307805477010145}
0.666 (+/-0.315) for {'C': 33.1131121482591, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.666 (+/-0.315) for {'C': 36.30780547701012, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.666 (+/-0.314) for {'C': 36.30780547701012, 'kernel': 'rbf', 'gamma': 0.017378008287493755}
0.641 (+/-0.235) for {'C': 36.30780547701012, 'kernel': 'rbf', 'gamma': 0.01905460717963247}
0.641 (+/-0.235) for {'C': 36.30780547701012, 'kernel': 'rbf', 'gamma': 0.020892961308540393}
0.641 (+/-0.235) for {'C': 36.30780547701012, 'kernel': 'rbf', 'gamma': 0.022908676527677727}
0.666 (+/-0.315) for {'C': 36.30780547701012, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.666 (+/-0.315) for {'C': 36.30780547701012, 'kernel': 'rbf', 'gamma': 0.027542287033381657}
0.666 (+/-0.315) for {'C': 36.30780547701012, 'kernel': 'rbf', 'gamma': 0.03019951720402017}
0.666 (+/-0.315) for {'C': 36.30780547701012, 'kernel': 'rbf', 'gamma': 0.03311311214825912}
0.666 (+/-0.315) for {'C': 36.30780547701012, 'kernel': 'rbf', 'gamma': 0.036307805477010145}
0.665 (+/-0.314) for {'C': 36.30780547701012, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.666 (+/-0.314) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.641 (+/-0.235) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.017378008287493755}
0.641 (+/-0.235) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.01905460717963247}
0.666 (+/-0.314) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.020892961308540393}
0.666 (+/-0.315) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.022908676527677727}
0.666 (+/-0.315) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.666 (+/-0.315) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.027542287033381657}
0.666 (+/-0.315) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.03019951720402017}
0.666 (+/-0.315) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.03311311214825912}
0.665 (+/-0.314) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.036307805477010145}
0.665 (+/-0.313) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.641 (+/-0.235) for {'C': 43.651583224016605, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.666 (+/-0.314) for {'C': 43.651583224016605, 'kernel': 'rbf', 'gamma': 0.017378008287493755}
0.666 (+/-0.314) for {'C': 43.651583224016605, 'kernel': 'rbf', 'gamma': 0.01905460717963247}
0.666 (+/-0.315) for {'C': 43.651583224016605, 'kernel': 'rbf', 'gamma': 0.020892961308540393}
0.666 (+/-0.315) for {'C': 43.651583224016605, 'kernel': 'rbf', 'gamma': 0.022908676527677727}
0.666 (+/-0.315) for {'C': 43.651583224016605, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.666 (+/-0.315) for {'C': 43.651583224016605, 'kernel': 'rbf', 'gamma': 0.027542287033381657}
0.666 (+/-0.315) for {'C': 43.651583224016605, 'kernel': 'rbf', 'gamma': 0.03019951720402017}
0.665 (+/-0.314) for {'C': 43.651583224016605, 'kernel': 'rbf', 'gamma': 0.03311311214825912}
0.665 (+/-0.313) for {'C': 43.651583224016605, 'kernel': 'rbf', 'gamma': 0.036307805477010145}
0.665 (+/-0.313) for {'C': 43.651583224016605, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.666 (+/-0.314) for {'C': 47.86300923226384, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.666 (+/-0.314) for {'C': 47.86300923226384, 'kernel': 'rbf', 'gamma': 0.017378008287493755}
0.666 (+/-0.315) for {'C': 47.86300923226384, 'kernel': 'rbf', 'gamma': 0.01905460717963247}
0.666 (+/-0.315) for {'C': 47.86300923226384, 'kernel': 'rbf', 'gamma': 0.020892961308540393}
0.666 (+/-0.315) for {'C': 47.86300923226384, 'kernel': 'rbf', 'gamma': 0.022908676527677727}
0.666 (+/-0.315) for {'C': 47.86300923226384, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.666 (+/-0.315) for {'C': 47.86300923226384, 'kernel': 'rbf', 'gamma': 0.027542287033381657}
0.665 (+/-0.314) for {'C': 47.86300923226384, 'kernel': 'rbf', 'gamma': 0.03019951720402017}
0.665 (+/-0.313) for {'C': 47.86300923226384, 'kernel': 'rbf', 'gamma': 0.03311311214825912}
0.665 (+/-0.313) for {'C': 47.86300923226384, 'kernel': 'rbf', 'gamma': 0.036307805477010145}
0.665 (+/-0.313) for {'C': 47.86300923226384, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.666 (+/-0.314) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.666 (+/-0.315) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.017378008287493755}
0.666 (+/-0.315) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.01905460717963247}
0.666 (+/-0.315) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.020892961308540393}
0.666 (+/-0.315) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.022908676527677727}
0.666 (+/-0.315) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.665 (+/-0.314) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.027542287033381657}
0.665 (+/-0.313) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.03019951720402017}
0.665 (+/-0.313) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.03311311214825912}
0.665 (+/-0.313) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.036307805477010145}
0.665 (+/-0.314) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.666 (+/-0.315) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.666 (+/-0.315) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.017378008287493755}
0.666 (+/-0.315) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.01905460717963247}
0.666 (+/-0.315) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.020892961308540393}
0.666 (+/-0.315) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.022908676527677727}
0.665 (+/-0.314) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.665 (+/-0.313) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.027542287033381657}
0.665 (+/-0.313) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.03019951720402017}
0.665 (+/-0.313) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.03311311214825912}
0.665 (+/-0.314) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.036307805477010145}
0.664 (+/-0.314) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.666 (+/-0.315) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.666 (+/-0.315) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.017378008287493755}
0.666 (+/-0.315) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.01905460717963247}
0.666 (+/-0.315) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.020892961308540393}
0.665 (+/-0.314) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.022908676527677727}
0.665 (+/-0.313) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.665 (+/-0.313) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.027542287033381657}
0.665 (+/-0.313) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.03019951720402017}
0.665 (+/-0.314) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.03311311214825912}
0.664 (+/-0.314) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.036307805477010145}
0.664 (+/-0.313) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.641 (+/-0.235) for {'C': 1.0, 'kernel': 'linear'}
0.660 (+/-0.315) for {'C': 10.0, 'kernel': 'linear'}
0.613 (+/-0.242) for {'C': 100.0, 'kernel': 'linear'}
0.613 (+/-0.241) for {'C': 1000.0, 'kernel': 'linear'}
0.613 (+/-0.241) for {'C': 10000.0, 'kernel': 'linear'}
0.613 (+/-0.241) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6658653846153846
发现最优参数gamma为原先的最大/最小值，直接重新设置超参。
第0轮loop中，tunemodel函数得到的最优参数是： ([{'C': array([25.11886432, 25.58585887, 26.0615355 , 26.54605562, 27.03958364,
       27.54228703, 28.05433638, 28.57590543, 29.10717118, 29.6483139 ,
       30.1995172 ]), 'kernel': ['rbf'], 'gamma': array([3.98107171e-07, 3.98107171e-06, 3.98107171e-05, 3.98107171e-04,
       3.98107171e-03, 3.98107171e-02, 3.98107171e-01, 3.98107171e+00,
       3.98107171e+01, 3.98107171e+02, 3.98107171e+03])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}], {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 0.039810717055349734}, 0.6658653846153846)
这是第0次迭代微调C和gamma。
第0次迭代，得到delta： [12.26843002  0.01469185]
执行tunemodel()函数前，使用的grid search parameter是：
 [{'C': array([25.11886432, 25.58585887, 26.0615355 , 26.54605562, 27.03958364,
       27.54228703, 28.05433638, 28.57590543, 29.10717118, 29.6483139 ,
       30.1995172 ]), 'kernel': ['rbf'], 'gamma': array([3.98107171e-07, 3.98107171e-06, 3.98107171e-05, 3.98107171e-04,
       3.98107171e-03, 3.98107171e-02, 3.98107171e-01, 3.98107171e+00,
       3.98107171e+01, 3.98107171e+02, 3.98107171e+03])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 3.981071705534969e-07}
0.496 (+/-0.001) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 3.981071705534969e-06}
0.496 (+/-0.001) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 3.9810717055349695e-05}
0.496 (+/-0.001) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.0003981071705534969}
0.747 (+/-0.502) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.003981071705534969}
0.697 (+/-0.437) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.03981071705534969}
0.626 (+/-0.318) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.3981071705534969}
0.496 (+/-0.001) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 3.981071705534969}
0.496 (+/-0.001) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 39.81071705534969}
0.496 (+/-0.001) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 398.1071705534969}
0.496 (+/-0.001) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 3981.0717055349733}
0.496 (+/-0.001) for {'C': 25.58585886905646, 'kernel': 'rbf', 'gamma': 3.981071705534969e-07}
0.496 (+/-0.001) for {'C': 25.58585886905646, 'kernel': 'rbf', 'gamma': 3.981071705534969e-06}
0.496 (+/-0.001) for {'C': 25.58585886905646, 'kernel': 'rbf', 'gamma': 3.9810717055349695e-05}
0.496 (+/-0.001) for {'C': 25.58585886905646, 'kernel': 'rbf', 'gamma': 0.0003981071705534969}
0.747 (+/-0.502) for {'C': 25.58585886905646, 'kernel': 'rbf', 'gamma': 0.003981071705534969}
0.697 (+/-0.437) for {'C': 25.58585886905646, 'kernel': 'rbf', 'gamma': 0.03981071705534969}
0.626 (+/-0.318) for {'C': 25.58585886905646, 'kernel': 'rbf', 'gamma': 0.3981071705534969}
0.496 (+/-0.001) for {'C': 25.58585886905646, 'kernel': 'rbf', 'gamma': 3.981071705534969}
0.496 (+/-0.001) for {'C': 25.58585886905646, 'kernel': 'rbf', 'gamma': 39.81071705534969}
0.496 (+/-0.001) for {'C': 25.58585886905646, 'kernel': 'rbf', 'gamma': 398.1071705534969}
0.496 (+/-0.001) for {'C': 25.58585886905646, 'kernel': 'rbf', 'gamma': 3981.0717055349733}
0.496 (+/-0.001) for {'C': 26.061535499988945, 'kernel': 'rbf', 'gamma': 3.981071705534969e-07}
0.496 (+/-0.001) for {'C': 26.061535499988945, 'kernel': 'rbf', 'gamma': 3.981071705534969e-06}
0.496 (+/-0.001) for {'C': 26.061535499988945, 'kernel': 'rbf', 'gamma': 3.9810717055349695e-05}
0.496 (+/-0.001) for {'C': 26.061535499988945, 'kernel': 'rbf', 'gamma': 0.0003981071705534969}
0.747 (+/-0.502) for {'C': 26.061535499988945, 'kernel': 'rbf', 'gamma': 0.003981071705534969}
0.697 (+/-0.437) for {'C': 26.061535499988945, 'kernel': 'rbf', 'gamma': 0.03981071705534969}
0.626 (+/-0.318) for {'C': 26.061535499988945, 'kernel': 'rbf', 'gamma': 0.3981071705534969}
0.496 (+/-0.001) for {'C': 26.061535499988945, 'kernel': 'rbf', 'gamma': 3.981071705534969}
0.496 (+/-0.001) for {'C': 26.061535499988945, 'kernel': 'rbf', 'gamma': 39.81071705534969}
0.496 (+/-0.001) for {'C': 26.061535499988945, 'kernel': 'rbf', 'gamma': 398.1071705534969}
0.496 (+/-0.001) for {'C': 26.061535499988945, 'kernel': 'rbf', 'gamma': 3981.0717055349733}
0.496 (+/-0.001) for {'C': 26.54605561975539, 'kernel': 'rbf', 'gamma': 3.981071705534969e-07}
0.496 (+/-0.001) for {'C': 26.54605561975539, 'kernel': 'rbf', 'gamma': 3.981071705534969e-06}
0.496 (+/-0.001) for {'C': 26.54605561975539, 'kernel': 'rbf', 'gamma': 3.9810717055349695e-05}
0.496 (+/-0.001) for {'C': 26.54605561975539, 'kernel': 'rbf', 'gamma': 0.0003981071705534969}
0.747 (+/-0.502) for {'C': 26.54605561975539, 'kernel': 'rbf', 'gamma': 0.003981071705534969}
0.697 (+/-0.437) for {'C': 26.54605561975539, 'kernel': 'rbf', 'gamma': 0.03981071705534969}
0.631 (+/-0.319) for {'C': 26.54605561975539, 'kernel': 'rbf', 'gamma': 0.3981071705534969}
0.496 (+/-0.001) for {'C': 26.54605561975539, 'kernel': 'rbf', 'gamma': 3.981071705534969}
0.496 (+/-0.001) for {'C': 26.54605561975539, 'kernel': 'rbf', 'gamma': 39.81071705534969}
0.496 (+/-0.001) for {'C': 26.54605561975539, 'kernel': 'rbf', 'gamma': 398.1071705534969}
0.496 (+/-0.001) for {'C': 26.54605561975539, 'kernel': 'rbf', 'gamma': 3981.0717055349733}
0.496 (+/-0.001) for {'C': 27.039583641088424, 'kernel': 'rbf', 'gamma': 3.981071705534969e-07}
0.496 (+/-0.001) for {'C': 27.039583641088424, 'kernel': 'rbf', 'gamma': 3.981071705534969e-06}
0.496 (+/-0.001) for {'C': 27.039583641088424, 'kernel': 'rbf', 'gamma': 3.9810717055349695e-05}
0.496 (+/-0.001) for {'C': 27.039583641088424, 'kernel': 'rbf', 'gamma': 0.0003981071705534969}
0.747 (+/-0.502) for {'C': 27.039583641088424, 'kernel': 'rbf', 'gamma': 0.003981071705534969}
0.697 (+/-0.437) for {'C': 27.039583641088424, 'kernel': 'rbf', 'gamma': 0.03981071705534969}
0.631 (+/-0.319) for {'C': 27.039583641088424, 'kernel': 'rbf', 'gamma': 0.3981071705534969}
0.496 (+/-0.001) for {'C': 27.039583641088424, 'kernel': 'rbf', 'gamma': 3.981071705534969}
0.496 (+/-0.001) for {'C': 27.039583641088424, 'kernel': 'rbf', 'gamma': 39.81071705534969}
0.496 (+/-0.001) for {'C': 27.039583641088424, 'kernel': 'rbf', 'gamma': 398.1071705534969}
0.496 (+/-0.001) for {'C': 27.039583641088424, 'kernel': 'rbf', 'gamma': 3981.0717055349733}
0.496 (+/-0.001) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 3.981071705534969e-07}
0.496 (+/-0.001) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 3.981071705534969e-06}
0.496 (+/-0.001) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 3.9810717055349695e-05}
0.496 (+/-0.001) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 0.0003981071705534969}
0.747 (+/-0.502) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 0.003981071705534969}
0.697 (+/-0.437) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 0.03981071705534969}
0.631 (+/-0.319) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 0.3981071705534969}
0.496 (+/-0.001) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 3.981071705534969}
0.496 (+/-0.001) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 39.81071705534969}
0.496 (+/-0.001) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 398.1071705534969}
0.496 (+/-0.001) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 3981.0717055349733}
0.496 (+/-0.001) for {'C': 28.054336379517135, 'kernel': 'rbf', 'gamma': 3.981071705534969e-07}
0.496 (+/-0.001) for {'C': 28.054336379517135, 'kernel': 'rbf', 'gamma': 3.981071705534969e-06}
0.496 (+/-0.001) for {'C': 28.054336379517135, 'kernel': 'rbf', 'gamma': 3.9810717055349695e-05}
0.496 (+/-0.001) for {'C': 28.054336379517135, 'kernel': 'rbf', 'gamma': 0.0003981071705534969}
0.747 (+/-0.502) for {'C': 28.054336379517135, 'kernel': 'rbf', 'gamma': 0.003981071705534969}
0.697 (+/-0.437) for {'C': 28.054336379517135, 'kernel': 'rbf', 'gamma': 0.03981071705534969}
0.631 (+/-0.319) for {'C': 28.054336379517135, 'kernel': 'rbf', 'gamma': 0.3981071705534969}
0.496 (+/-0.001) for {'C': 28.054336379517135, 'kernel': 'rbf', 'gamma': 3.981071705534969}
0.496 (+/-0.001) for {'C': 28.054336379517135, 'kernel': 'rbf', 'gamma': 39.81071705534969}
0.496 (+/-0.001) for {'C': 28.054336379517135, 'kernel': 'rbf', 'gamma': 398.1071705534969}
0.496 (+/-0.001) for {'C': 28.054336379517135, 'kernel': 'rbf', 'gamma': 3981.0717055349733}
0.496 (+/-0.001) for {'C': 28.57590543374946, 'kernel': 'rbf', 'gamma': 3.981071705534969e-07}
0.496 (+/-0.001) for {'C': 28.57590543374946, 'kernel': 'rbf', 'gamma': 3.981071705534969e-06}
0.496 (+/-0.001) for {'C': 28.57590543374946, 'kernel': 'rbf', 'gamma': 3.9810717055349695e-05}
0.496 (+/-0.001) for {'C': 28.57590543374946, 'kernel': 'rbf', 'gamma': 0.0003981071705534969}
0.747 (+/-0.502) for {'C': 28.57590543374946, 'kernel': 'rbf', 'gamma': 0.003981071705534969}
0.697 (+/-0.437) for {'C': 28.57590543374946, 'kernel': 'rbf', 'gamma': 0.03981071705534969}
0.631 (+/-0.319) for {'C': 28.57590543374946, 'kernel': 'rbf', 'gamma': 0.3981071705534969}
0.496 (+/-0.001) for {'C': 28.57590543374946, 'kernel': 'rbf', 'gamma': 3.981071705534969}
0.496 (+/-0.001) for {'C': 28.57590543374946, 'kernel': 'rbf', 'gamma': 39.81071705534969}
0.496 (+/-0.001) for {'C': 28.57590543374946, 'kernel': 'rbf', 'gamma': 398.1071705534969}
0.496 (+/-0.001) for {'C': 28.57590543374946, 'kernel': 'rbf', 'gamma': 3981.0717055349733}
0.496 (+/-0.001) for {'C': 29.10717118066605, 'kernel': 'rbf', 'gamma': 3.981071705534969e-07}
0.496 (+/-0.001) for {'C': 29.10717118066605, 'kernel': 'rbf', 'gamma': 3.981071705534969e-06}
0.496 (+/-0.001) for {'C': 29.10717118066605, 'kernel': 'rbf', 'gamma': 3.9810717055349695e-05}
0.496 (+/-0.001) for {'C': 29.10717118066605, 'kernel': 'rbf', 'gamma': 0.0003981071705534969}
0.747 (+/-0.502) for {'C': 29.10717118066605, 'kernel': 'rbf', 'gamma': 0.003981071705534969}
0.697 (+/-0.437) for {'C': 29.10717118066605, 'kernel': 'rbf', 'gamma': 0.03981071705534969}
0.631 (+/-0.319) for {'C': 29.10717118066605, 'kernel': 'rbf', 'gamma': 0.3981071705534969}
0.496 (+/-0.001) for {'C': 29.10717118066605, 'kernel': 'rbf', 'gamma': 3.981071705534969}
0.496 (+/-0.001) for {'C': 29.10717118066605, 'kernel': 'rbf', 'gamma': 39.81071705534969}
0.496 (+/-0.001) for {'C': 29.10717118066605, 'kernel': 'rbf', 'gamma': 398.1071705534969}
0.496 (+/-0.001) for {'C': 29.10717118066605, 'kernel': 'rbf', 'gamma': 3981.0717055349733}
0.496 (+/-0.001) for {'C': 29.648313895243408, 'kernel': 'rbf', 'gamma': 3.981071705534969e-07}
0.496 (+/-0.001) for {'C': 29.648313895243408, 'kernel': 'rbf', 'gamma': 3.981071705534969e-06}
0.496 (+/-0.001) for {'C': 29.648313895243408, 'kernel': 'rbf', 'gamma': 3.9810717055349695e-05}
0.496 (+/-0.001) for {'C': 29.648313895243408, 'kernel': 'rbf', 'gamma': 0.0003981071705534969}
0.747 (+/-0.502) for {'C': 29.648313895243408, 'kernel': 'rbf', 'gamma': 0.003981071705534969}
0.697 (+/-0.437) for {'C': 29.648313895243408, 'kernel': 'rbf', 'gamma': 0.03981071705534969}
0.631 (+/-0.319) for {'C': 29.648313895243408, 'kernel': 'rbf', 'gamma': 0.3981071705534969}
0.496 (+/-0.001) for {'C': 29.648313895243408, 'kernel': 'rbf', 'gamma': 3.981071705534969}
0.496 (+/-0.001) for {'C': 29.648313895243408, 'kernel': 'rbf', 'gamma': 39.81071705534969}
0.496 (+/-0.001) for {'C': 29.648313895243408, 'kernel': 'rbf', 'gamma': 398.1071705534969}
0.496 (+/-0.001) for {'C': 29.648313895243408, 'kernel': 'rbf', 'gamma': 3981.0717055349733}
0.496 (+/-0.001) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 3.981071705534969e-07}
0.496 (+/-0.001) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 3.981071705534969e-06}
0.496 (+/-0.001) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 3.9810717055349695e-05}
0.496 (+/-0.001) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 0.0003981071705534969}
0.747 (+/-0.502) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 0.003981071705534969}
0.697 (+/-0.437) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 0.03981071705534969}
0.631 (+/-0.319) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 0.3981071705534969}
0.496 (+/-0.001) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 3.981071705534969}
0.496 (+/-0.001) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 39.81071705534969}
0.496 (+/-0.001) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 398.1071705534969}
0.496 (+/-0.001) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 3981.0717055349733}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.689 (+/-0.436) for {'C': 10.0, 'kernel': 'linear'}
0.656 (+/-0.384) for {'C': 100.0, 'kernel': 'linear'}
0.651 (+/-0.385) for {'C': 1000.0, 'kernel': 'linear'}
0.651 (+/-0.385) for {'C': 10000.0, 'kernel': 'linear'}
0.651 (+/-0.385) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 3.981071705534969e-07}
0.500 (+/-0.000) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 3.981071705534969e-06}
0.500 (+/-0.000) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 3.9810717055349695e-05}
0.500 (+/-0.000) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.0003981071705534969}
0.617 (+/-0.238) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.003981071705534969}
0.616 (+/-0.237) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.03981071705534969}
0.614 (+/-0.239) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.3981071705534969}
0.499 (+/-0.002) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 3.981071705534969}
0.500 (+/-0.000) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 39.81071705534969}
0.500 (+/-0.000) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 398.1071705534969}
0.500 (+/-0.000) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 3981.0717055349733}
0.500 (+/-0.000) for {'C': 25.58585886905646, 'kernel': 'rbf', 'gamma': 3.981071705534969e-07}
0.500 (+/-0.000) for {'C': 25.58585886905646, 'kernel': 'rbf', 'gamma': 3.981071705534969e-06}
0.500 (+/-0.000) for {'C': 25.58585886905646, 'kernel': 'rbf', 'gamma': 3.9810717055349695e-05}
0.500 (+/-0.000) for {'C': 25.58585886905646, 'kernel': 'rbf', 'gamma': 0.0003981071705534969}
0.617 (+/-0.238) for {'C': 25.58585886905646, 'kernel': 'rbf', 'gamma': 0.003981071705534969}
0.616 (+/-0.237) for {'C': 25.58585886905646, 'kernel': 'rbf', 'gamma': 0.03981071705534969}
0.613 (+/-0.240) for {'C': 25.58585886905646, 'kernel': 'rbf', 'gamma': 0.3981071705534969}
0.499 (+/-0.002) for {'C': 25.58585886905646, 'kernel': 'rbf', 'gamma': 3.981071705534969}
0.500 (+/-0.000) for {'C': 25.58585886905646, 'kernel': 'rbf', 'gamma': 39.81071705534969}
0.500 (+/-0.000) for {'C': 25.58585886905646, 'kernel': 'rbf', 'gamma': 398.1071705534969}
0.500 (+/-0.000) for {'C': 25.58585886905646, 'kernel': 'rbf', 'gamma': 3981.0717055349733}
0.500 (+/-0.000) for {'C': 26.061535499988945, 'kernel': 'rbf', 'gamma': 3.981071705534969e-07}
0.500 (+/-0.000) for {'C': 26.061535499988945, 'kernel': 'rbf', 'gamma': 3.981071705534969e-06}
0.500 (+/-0.000) for {'C': 26.061535499988945, 'kernel': 'rbf', 'gamma': 3.9810717055349695e-05}
0.500 (+/-0.000) for {'C': 26.061535499988945, 'kernel': 'rbf', 'gamma': 0.0003981071705534969}
0.617 (+/-0.238) for {'C': 26.061535499988945, 'kernel': 'rbf', 'gamma': 0.003981071705534969}
0.616 (+/-0.237) for {'C': 26.061535499988945, 'kernel': 'rbf', 'gamma': 0.03981071705534969}
0.613 (+/-0.240) for {'C': 26.061535499988945, 'kernel': 'rbf', 'gamma': 0.3981071705534969}
0.499 (+/-0.002) for {'C': 26.061535499988945, 'kernel': 'rbf', 'gamma': 3.981071705534969}
0.500 (+/-0.000) for {'C': 26.061535499988945, 'kernel': 'rbf', 'gamma': 39.81071705534969}
0.500 (+/-0.000) for {'C': 26.061535499988945, 'kernel': 'rbf', 'gamma': 398.1071705534969}
0.500 (+/-0.000) for {'C': 26.061535499988945, 'kernel': 'rbf', 'gamma': 3981.0717055349733}
0.500 (+/-0.000) for {'C': 26.54605561975539, 'kernel': 'rbf', 'gamma': 3.981071705534969e-07}
0.500 (+/-0.000) for {'C': 26.54605561975539, 'kernel': 'rbf', 'gamma': 3.981071705534969e-06}
0.500 (+/-0.000) for {'C': 26.54605561975539, 'kernel': 'rbf', 'gamma': 3.9810717055349695e-05}
0.500 (+/-0.000) for {'C': 26.54605561975539, 'kernel': 'rbf', 'gamma': 0.0003981071705534969}
0.617 (+/-0.238) for {'C': 26.54605561975539, 'kernel': 'rbf', 'gamma': 0.003981071705534969}
0.616 (+/-0.237) for {'C': 26.54605561975539, 'kernel': 'rbf', 'gamma': 0.03981071705534969}
0.614 (+/-0.240) for {'C': 26.54605561975539, 'kernel': 'rbf', 'gamma': 0.3981071705534969}
0.499 (+/-0.002) for {'C': 26.54605561975539, 'kernel': 'rbf', 'gamma': 3.981071705534969}
0.500 (+/-0.000) for {'C': 26.54605561975539, 'kernel': 'rbf', 'gamma': 39.81071705534969}
0.500 (+/-0.000) for {'C': 26.54605561975539, 'kernel': 'rbf', 'gamma': 398.1071705534969}
0.500 (+/-0.000) for {'C': 26.54605561975539, 'kernel': 'rbf', 'gamma': 3981.0717055349733}
0.500 (+/-0.000) for {'C': 27.039583641088424, 'kernel': 'rbf', 'gamma': 3.981071705534969e-07}
0.500 (+/-0.000) for {'C': 27.039583641088424, 'kernel': 'rbf', 'gamma': 3.981071705534969e-06}
0.500 (+/-0.000) for {'C': 27.039583641088424, 'kernel': 'rbf', 'gamma': 3.9810717055349695e-05}
0.500 (+/-0.000) for {'C': 27.039583641088424, 'kernel': 'rbf', 'gamma': 0.0003981071705534969}
0.617 (+/-0.238) for {'C': 27.039583641088424, 'kernel': 'rbf', 'gamma': 0.003981071705534969}
0.616 (+/-0.237) for {'C': 27.039583641088424, 'kernel': 'rbf', 'gamma': 0.03981071705534969}
0.614 (+/-0.240) for {'C': 27.039583641088424, 'kernel': 'rbf', 'gamma': 0.3981071705534969}
0.499 (+/-0.002) for {'C': 27.039583641088424, 'kernel': 'rbf', 'gamma': 3.981071705534969}
0.500 (+/-0.000) for {'C': 27.039583641088424, 'kernel': 'rbf', 'gamma': 39.81071705534969}
0.500 (+/-0.000) for {'C': 27.039583641088424, 'kernel': 'rbf', 'gamma': 398.1071705534969}
0.500 (+/-0.000) for {'C': 27.039583641088424, 'kernel': 'rbf', 'gamma': 3981.0717055349733}
0.500 (+/-0.000) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 3.981071705534969e-07}
0.500 (+/-0.000) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 3.981071705534969e-06}
0.500 (+/-0.000) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 3.9810717055349695e-05}
0.500 (+/-0.000) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 0.0003981071705534969}
0.617 (+/-0.238) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 0.003981071705534969}
0.616 (+/-0.237) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 0.03981071705534969}
0.614 (+/-0.239) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 0.3981071705534969}
0.499 (+/-0.002) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 3.981071705534969}
0.500 (+/-0.000) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 39.81071705534969}
0.500 (+/-0.000) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 398.1071705534969}
0.500 (+/-0.000) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 3981.0717055349733}
0.500 (+/-0.000) for {'C': 28.054336379517135, 'kernel': 'rbf', 'gamma': 3.981071705534969e-07}
0.500 (+/-0.000) for {'C': 28.054336379517135, 'kernel': 'rbf', 'gamma': 3.981071705534969e-06}
0.500 (+/-0.000) for {'C': 28.054336379517135, 'kernel': 'rbf', 'gamma': 3.9810717055349695e-05}
0.500 (+/-0.000) for {'C': 28.054336379517135, 'kernel': 'rbf', 'gamma': 0.0003981071705534969}
0.617 (+/-0.238) for {'C': 28.054336379517135, 'kernel': 'rbf', 'gamma': 0.003981071705534969}
0.616 (+/-0.237) for {'C': 28.054336379517135, 'kernel': 'rbf', 'gamma': 0.03981071705534969}
0.614 (+/-0.239) for {'C': 28.054336379517135, 'kernel': 'rbf', 'gamma': 0.3981071705534969}
0.499 (+/-0.002) for {'C': 28.054336379517135, 'kernel': 'rbf', 'gamma': 3.981071705534969}
0.500 (+/-0.000) for {'C': 28.054336379517135, 'kernel': 'rbf', 'gamma': 39.81071705534969}
0.500 (+/-0.000) for {'C': 28.054336379517135, 'kernel': 'rbf', 'gamma': 398.1071705534969}
0.500 (+/-0.000) for {'C': 28.054336379517135, 'kernel': 'rbf', 'gamma': 3981.0717055349733}
0.500 (+/-0.000) for {'C': 28.57590543374946, 'kernel': 'rbf', 'gamma': 3.981071705534969e-07}
0.500 (+/-0.000) for {'C': 28.57590543374946, 'kernel': 'rbf', 'gamma': 3.981071705534969e-06}
0.500 (+/-0.000) for {'C': 28.57590543374946, 'kernel': 'rbf', 'gamma': 3.9810717055349695e-05}
0.500 (+/-0.000) for {'C': 28.57590543374946, 'kernel': 'rbf', 'gamma': 0.0003981071705534969}
0.617 (+/-0.238) for {'C': 28.57590543374946, 'kernel': 'rbf', 'gamma': 0.003981071705534969}
0.616 (+/-0.237) for {'C': 28.57590543374946, 'kernel': 'rbf', 'gamma': 0.03981071705534969}
0.614 (+/-0.239) for {'C': 28.57590543374946, 'kernel': 'rbf', 'gamma': 0.3981071705534969}
0.499 (+/-0.002) for {'C': 28.57590543374946, 'kernel': 'rbf', 'gamma': 3.981071705534969}
0.500 (+/-0.000) for {'C': 28.57590543374946, 'kernel': 'rbf', 'gamma': 39.81071705534969}
0.500 (+/-0.000) for {'C': 28.57590543374946, 'kernel': 'rbf', 'gamma': 398.1071705534969}
0.500 (+/-0.000) for {'C': 28.57590543374946, 'kernel': 'rbf', 'gamma': 3981.0717055349733}
0.500 (+/-0.000) for {'C': 29.10717118066605, 'kernel': 'rbf', 'gamma': 3.981071705534969e-07}
0.500 (+/-0.000) for {'C': 29.10717118066605, 'kernel': 'rbf', 'gamma': 3.981071705534969e-06}
0.500 (+/-0.000) for {'C': 29.10717118066605, 'kernel': 'rbf', 'gamma': 3.9810717055349695e-05}
0.500 (+/-0.000) for {'C': 29.10717118066605, 'kernel': 'rbf', 'gamma': 0.0003981071705534969}
0.617 (+/-0.238) for {'C': 29.10717118066605, 'kernel': 'rbf', 'gamma': 0.003981071705534969}
0.616 (+/-0.237) for {'C': 29.10717118066605, 'kernel': 'rbf', 'gamma': 0.03981071705534969}
0.614 (+/-0.239) for {'C': 29.10717118066605, 'kernel': 'rbf', 'gamma': 0.3981071705534969}
0.499 (+/-0.002) for {'C': 29.10717118066605, 'kernel': 'rbf', 'gamma': 3.981071705534969}
0.500 (+/-0.000) for {'C': 29.10717118066605, 'kernel': 'rbf', 'gamma': 39.81071705534969}
0.500 (+/-0.000) for {'C': 29.10717118066605, 'kernel': 'rbf', 'gamma': 398.1071705534969}
0.500 (+/-0.000) for {'C': 29.10717118066605, 'kernel': 'rbf', 'gamma': 3981.0717055349733}
0.500 (+/-0.000) for {'C': 29.648313895243408, 'kernel': 'rbf', 'gamma': 3.981071705534969e-07}
0.500 (+/-0.000) for {'C': 29.648313895243408, 'kernel': 'rbf', 'gamma': 3.981071705534969e-06}
0.500 (+/-0.000) for {'C': 29.648313895243408, 'kernel': 'rbf', 'gamma': 3.9810717055349695e-05}
0.500 (+/-0.000) for {'C': 29.648313895243408, 'kernel': 'rbf', 'gamma': 0.0003981071705534969}
0.617 (+/-0.238) for {'C': 29.648313895243408, 'kernel': 'rbf', 'gamma': 0.003981071705534969}
0.616 (+/-0.237) for {'C': 29.648313895243408, 'kernel': 'rbf', 'gamma': 0.03981071705534969}
0.614 (+/-0.239) for {'C': 29.648313895243408, 'kernel': 'rbf', 'gamma': 0.3981071705534969}
0.499 (+/-0.002) for {'C': 29.648313895243408, 'kernel': 'rbf', 'gamma': 3.981071705534969}
0.500 (+/-0.000) for {'C': 29.648313895243408, 'kernel': 'rbf', 'gamma': 39.81071705534969}
0.500 (+/-0.000) for {'C': 29.648313895243408, 'kernel': 'rbf', 'gamma': 398.1071705534969}
0.500 (+/-0.000) for {'C': 29.648313895243408, 'kernel': 'rbf', 'gamma': 3981.0717055349733}
0.500 (+/-0.000) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 3.981071705534969e-07}
0.500 (+/-0.000) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 3.981071705534969e-06}
0.500 (+/-0.000) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 3.9810717055349695e-05}
0.500 (+/-0.000) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 0.0003981071705534969}
0.617 (+/-0.238) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 0.003981071705534969}
0.616 (+/-0.237) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 0.03981071705534969}
0.614 (+/-0.239) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 0.3981071705534969}
0.499 (+/-0.002) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 3.981071705534969}
0.500 (+/-0.000) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 39.81071705534969}
0.500 (+/-0.000) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 398.1071705534969}
0.500 (+/-0.000) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 3981.0717055349733}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.616 (+/-0.238) for {'C': 10.0, 'kernel': 'linear'}
0.613 (+/-0.242) for {'C': 100.0, 'kernel': 'linear'}
0.613 (+/-0.241) for {'C': 1000.0, 'kernel': 'linear'}
0.613 (+/-0.241) for {'C': 10000.0, 'kernel': 'linear'}
0.613 (+/-0.241) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.003981071705534969}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6166666666666667
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 3.981071705534969e-07}
0.496 (+/-0.001) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 3.981071705534969e-06}
0.496 (+/-0.001) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 3.9810717055349695e-05}
0.747 (+/-0.502) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.0003981071705534969}
0.747 (+/-0.502) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.003981071705534969}
0.689 (+/-0.374) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.03981071705534969}
0.626 (+/-0.318) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.3981071705534969}
0.496 (+/-0.001) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 3.981071705534969}
0.496 (+/-0.001) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 39.81071705534969}
0.496 (+/-0.001) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 398.1071705534969}
0.496 (+/-0.001) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 3981.0717055349733}
0.496 (+/-0.001) for {'C': 25.58585886905646, 'kernel': 'rbf', 'gamma': 3.981071705534969e-07}
0.496 (+/-0.001) for {'C': 25.58585886905646, 'kernel': 'rbf', 'gamma': 3.981071705534969e-06}
0.496 (+/-0.001) for {'C': 25.58585886905646, 'kernel': 'rbf', 'gamma': 3.9810717055349695e-05}
0.747 (+/-0.502) for {'C': 25.58585886905646, 'kernel': 'rbf', 'gamma': 0.0003981071705534969}
0.747 (+/-0.502) for {'C': 25.58585886905646, 'kernel': 'rbf', 'gamma': 0.003981071705534969}
0.689 (+/-0.374) for {'C': 25.58585886905646, 'kernel': 'rbf', 'gamma': 0.03981071705534969}
0.626 (+/-0.318) for {'C': 25.58585886905646, 'kernel': 'rbf', 'gamma': 0.3981071705534969}
0.496 (+/-0.001) for {'C': 25.58585886905646, 'kernel': 'rbf', 'gamma': 3.981071705534969}
0.496 (+/-0.001) for {'C': 25.58585886905646, 'kernel': 'rbf', 'gamma': 39.81071705534969}
0.496 (+/-0.001) for {'C': 25.58585886905646, 'kernel': 'rbf', 'gamma': 398.1071705534969}
0.496 (+/-0.001) for {'C': 25.58585886905646, 'kernel': 'rbf', 'gamma': 3981.0717055349733}
0.496 (+/-0.001) for {'C': 26.061535499988945, 'kernel': 'rbf', 'gamma': 3.981071705534969e-07}
0.496 (+/-0.001) for {'C': 26.061535499988945, 'kernel': 'rbf', 'gamma': 3.981071705534969e-06}
0.496 (+/-0.001) for {'C': 26.061535499988945, 'kernel': 'rbf', 'gamma': 3.9810717055349695e-05}
0.747 (+/-0.502) for {'C': 26.061535499988945, 'kernel': 'rbf', 'gamma': 0.0003981071705534969}
0.747 (+/-0.502) for {'C': 26.061535499988945, 'kernel': 'rbf', 'gamma': 0.003981071705534969}
0.689 (+/-0.374) for {'C': 26.061535499988945, 'kernel': 'rbf', 'gamma': 0.03981071705534969}
0.626 (+/-0.318) for {'C': 26.061535499988945, 'kernel': 'rbf', 'gamma': 0.3981071705534969}
0.496 (+/-0.001) for {'C': 26.061535499988945, 'kernel': 'rbf', 'gamma': 3.981071705534969}
0.496 (+/-0.001) for {'C': 26.061535499988945, 'kernel': 'rbf', 'gamma': 39.81071705534969}
0.496 (+/-0.001) for {'C': 26.061535499988945, 'kernel': 'rbf', 'gamma': 398.1071705534969}
0.496 (+/-0.001) for {'C': 26.061535499988945, 'kernel': 'rbf', 'gamma': 3981.0717055349733}
0.496 (+/-0.001) for {'C': 26.54605561975539, 'kernel': 'rbf', 'gamma': 3.981071705534969e-07}
0.496 (+/-0.001) for {'C': 26.54605561975539, 'kernel': 'rbf', 'gamma': 3.981071705534969e-06}
0.496 (+/-0.001) for {'C': 26.54605561975539, 'kernel': 'rbf', 'gamma': 3.9810717055349695e-05}
0.747 (+/-0.502) for {'C': 26.54605561975539, 'kernel': 'rbf', 'gamma': 0.0003981071705534969}
0.747 (+/-0.502) for {'C': 26.54605561975539, 'kernel': 'rbf', 'gamma': 0.003981071705534969}
0.689 (+/-0.374) for {'C': 26.54605561975539, 'kernel': 'rbf', 'gamma': 0.03981071705534969}
0.631 (+/-0.319) for {'C': 26.54605561975539, 'kernel': 'rbf', 'gamma': 0.3981071705534969}
0.496 (+/-0.001) for {'C': 26.54605561975539, 'kernel': 'rbf', 'gamma': 3.981071705534969}
0.496 (+/-0.001) for {'C': 26.54605561975539, 'kernel': 'rbf', 'gamma': 39.81071705534969}
0.496 (+/-0.001) for {'C': 26.54605561975539, 'kernel': 'rbf', 'gamma': 398.1071705534969}
0.496 (+/-0.001) for {'C': 26.54605561975539, 'kernel': 'rbf', 'gamma': 3981.0717055349733}
0.496 (+/-0.001) for {'C': 27.039583641088424, 'kernel': 'rbf', 'gamma': 3.981071705534969e-07}
0.496 (+/-0.001) for {'C': 27.039583641088424, 'kernel': 'rbf', 'gamma': 3.981071705534969e-06}
0.496 (+/-0.001) for {'C': 27.039583641088424, 'kernel': 'rbf', 'gamma': 3.9810717055349695e-05}
0.747 (+/-0.502) for {'C': 27.039583641088424, 'kernel': 'rbf', 'gamma': 0.0003981071705534969}
0.747 (+/-0.502) for {'C': 27.039583641088424, 'kernel': 'rbf', 'gamma': 0.003981071705534969}
0.698 (+/-0.383) for {'C': 27.039583641088424, 'kernel': 'rbf', 'gamma': 0.03981071705534969}
0.631 (+/-0.319) for {'C': 27.039583641088424, 'kernel': 'rbf', 'gamma': 0.3981071705534969}
0.496 (+/-0.001) for {'C': 27.039583641088424, 'kernel': 'rbf', 'gamma': 3.981071705534969}
0.496 (+/-0.001) for {'C': 27.039583641088424, 'kernel': 'rbf', 'gamma': 39.81071705534969}
0.496 (+/-0.001) for {'C': 27.039583641088424, 'kernel': 'rbf', 'gamma': 398.1071705534969}
0.496 (+/-0.001) for {'C': 27.039583641088424, 'kernel': 'rbf', 'gamma': 3981.0717055349733}
0.496 (+/-0.001) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 3.981071705534969e-07}
0.496 (+/-0.001) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 3.981071705534969e-06}
0.496 (+/-0.001) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 3.9810717055349695e-05}
0.747 (+/-0.502) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 0.0003981071705534969}
0.747 (+/-0.502) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 0.003981071705534969}
0.698 (+/-0.383) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 0.03981071705534969}
0.631 (+/-0.319) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 0.3981071705534969}
0.496 (+/-0.001) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 3.981071705534969}
0.496 (+/-0.001) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 39.81071705534969}
0.496 (+/-0.001) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 398.1071705534969}
0.496 (+/-0.001) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 3981.0717055349733}
0.496 (+/-0.001) for {'C': 28.054336379517135, 'kernel': 'rbf', 'gamma': 3.981071705534969e-07}
0.496 (+/-0.001) for {'C': 28.054336379517135, 'kernel': 'rbf', 'gamma': 3.981071705534969e-06}
0.496 (+/-0.001) for {'C': 28.054336379517135, 'kernel': 'rbf', 'gamma': 3.9810717055349695e-05}
0.747 (+/-0.502) for {'C': 28.054336379517135, 'kernel': 'rbf', 'gamma': 0.0003981071705534969}
0.747 (+/-0.502) for {'C': 28.054336379517135, 'kernel': 'rbf', 'gamma': 0.003981071705534969}
0.698 (+/-0.383) for {'C': 28.054336379517135, 'kernel': 'rbf', 'gamma': 0.03981071705534969}
0.631 (+/-0.319) for {'C': 28.054336379517135, 'kernel': 'rbf', 'gamma': 0.3981071705534969}
0.496 (+/-0.001) for {'C': 28.054336379517135, 'kernel': 'rbf', 'gamma': 3.981071705534969}
0.496 (+/-0.001) for {'C': 28.054336379517135, 'kernel': 'rbf', 'gamma': 39.81071705534969}
0.496 (+/-0.001) for {'C': 28.054336379517135, 'kernel': 'rbf', 'gamma': 398.1071705534969}
0.496 (+/-0.001) for {'C': 28.054336379517135, 'kernel': 'rbf', 'gamma': 3981.0717055349733}
0.496 (+/-0.001) for {'C': 28.57590543374946, 'kernel': 'rbf', 'gamma': 3.981071705534969e-07}
0.496 (+/-0.001) for {'C': 28.57590543374946, 'kernel': 'rbf', 'gamma': 3.981071705534969e-06}
0.496 (+/-0.001) for {'C': 28.57590543374946, 'kernel': 'rbf', 'gamma': 3.9810717055349695e-05}
0.747 (+/-0.502) for {'C': 28.57590543374946, 'kernel': 'rbf', 'gamma': 0.0003981071705534969}
0.747 (+/-0.502) for {'C': 28.57590543374946, 'kernel': 'rbf', 'gamma': 0.003981071705534969}
0.698 (+/-0.383) for {'C': 28.57590543374946, 'kernel': 'rbf', 'gamma': 0.03981071705534969}
0.631 (+/-0.319) for {'C': 28.57590543374946, 'kernel': 'rbf', 'gamma': 0.3981071705534969}
0.496 (+/-0.001) for {'C': 28.57590543374946, 'kernel': 'rbf', 'gamma': 3.981071705534969}
0.496 (+/-0.001) for {'C': 28.57590543374946, 'kernel': 'rbf', 'gamma': 39.81071705534969}
0.496 (+/-0.001) for {'C': 28.57590543374946, 'kernel': 'rbf', 'gamma': 398.1071705534969}
0.496 (+/-0.001) for {'C': 28.57590543374946, 'kernel': 'rbf', 'gamma': 3981.0717055349733}
0.496 (+/-0.001) for {'C': 29.10717118066605, 'kernel': 'rbf', 'gamma': 3.981071705534969e-07}
0.496 (+/-0.001) for {'C': 29.10717118066605, 'kernel': 'rbf', 'gamma': 3.981071705534969e-06}
0.496 (+/-0.001) for {'C': 29.10717118066605, 'kernel': 'rbf', 'gamma': 3.9810717055349695e-05}
0.747 (+/-0.502) for {'C': 29.10717118066605, 'kernel': 'rbf', 'gamma': 0.0003981071705534969}
0.747 (+/-0.502) for {'C': 29.10717118066605, 'kernel': 'rbf', 'gamma': 0.003981071705534969}
0.698 (+/-0.383) for {'C': 29.10717118066605, 'kernel': 'rbf', 'gamma': 0.03981071705534969}
0.631 (+/-0.319) for {'C': 29.10717118066605, 'kernel': 'rbf', 'gamma': 0.3981071705534969}
0.496 (+/-0.001) for {'C': 29.10717118066605, 'kernel': 'rbf', 'gamma': 3.981071705534969}
0.496 (+/-0.001) for {'C': 29.10717118066605, 'kernel': 'rbf', 'gamma': 39.81071705534969}
0.496 (+/-0.001) for {'C': 29.10717118066605, 'kernel': 'rbf', 'gamma': 398.1071705534969}
0.496 (+/-0.001) for {'C': 29.10717118066605, 'kernel': 'rbf', 'gamma': 3981.0717055349733}
0.496 (+/-0.001) for {'C': 29.648313895243408, 'kernel': 'rbf', 'gamma': 3.981071705534969e-07}
0.496 (+/-0.001) for {'C': 29.648313895243408, 'kernel': 'rbf', 'gamma': 3.981071705534969e-06}
0.496 (+/-0.001) for {'C': 29.648313895243408, 'kernel': 'rbf', 'gamma': 3.9810717055349695e-05}
0.747 (+/-0.502) for {'C': 29.648313895243408, 'kernel': 'rbf', 'gamma': 0.0003981071705534969}
0.747 (+/-0.502) for {'C': 29.648313895243408, 'kernel': 'rbf', 'gamma': 0.003981071705534969}
0.698 (+/-0.383) for {'C': 29.648313895243408, 'kernel': 'rbf', 'gamma': 0.03981071705534969}
0.631 (+/-0.319) for {'C': 29.648313895243408, 'kernel': 'rbf', 'gamma': 0.3981071705534969}
0.496 (+/-0.001) for {'C': 29.648313895243408, 'kernel': 'rbf', 'gamma': 3.981071705534969}
0.496 (+/-0.001) for {'C': 29.648313895243408, 'kernel': 'rbf', 'gamma': 39.81071705534969}
0.496 (+/-0.001) for {'C': 29.648313895243408, 'kernel': 'rbf', 'gamma': 398.1071705534969}
0.496 (+/-0.001) for {'C': 29.648313895243408, 'kernel': 'rbf', 'gamma': 3981.0717055349733}
0.496 (+/-0.001) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 3.981071705534969e-07}
0.496 (+/-0.001) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 3.981071705534969e-06}
0.496 (+/-0.001) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 3.9810717055349695e-05}
0.747 (+/-0.502) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 0.0003981071705534969}
0.747 (+/-0.502) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 0.003981071705534969}
0.698 (+/-0.383) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 0.03981071705534969}
0.631 (+/-0.319) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 0.3981071705534969}
0.496 (+/-0.001) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 3.981071705534969}
0.496 (+/-0.001) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 39.81071705534969}
0.496 (+/-0.001) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 398.1071705534969}
0.496 (+/-0.001) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 3981.0717055349733}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.722 (+/-0.417) for {'C': 1.0, 'kernel': 'linear'}
0.654 (+/-0.379) for {'C': 10.0, 'kernel': 'linear'}
0.656 (+/-0.384) for {'C': 100.0, 'kernel': 'linear'}
0.651 (+/-0.385) for {'C': 1000.0, 'kernel': 'linear'}
0.651 (+/-0.385) for {'C': 10000.0, 'kernel': 'linear'}
0.651 (+/-0.385) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      1.00       623

avg / total       0.98      0.99      0.99       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 3.981071705534969e-07}
0.500 (+/-0.000) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 3.981071705534969e-06}
0.500 (+/-0.000) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 3.9810717055349695e-05}
0.617 (+/-0.238) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.0003981071705534969}
0.617 (+/-0.238) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.003981071705534969}
0.641 (+/-0.235) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.03981071705534969}
0.613 (+/-0.240) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.3981071705534969}
0.499 (+/-0.002) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 3.981071705534969}
0.500 (+/-0.000) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 39.81071705534969}
0.500 (+/-0.000) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 398.1071705534969}
0.500 (+/-0.000) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 3981.0717055349733}
0.500 (+/-0.000) for {'C': 25.58585886905646, 'kernel': 'rbf', 'gamma': 3.981071705534969e-07}
0.500 (+/-0.000) for {'C': 25.58585886905646, 'kernel': 'rbf', 'gamma': 3.981071705534969e-06}
0.500 (+/-0.000) for {'C': 25.58585886905646, 'kernel': 'rbf', 'gamma': 3.9810717055349695e-05}
0.617 (+/-0.238) for {'C': 25.58585886905646, 'kernel': 'rbf', 'gamma': 0.0003981071705534969}
0.617 (+/-0.238) for {'C': 25.58585886905646, 'kernel': 'rbf', 'gamma': 0.003981071705534969}
0.641 (+/-0.235) for {'C': 25.58585886905646, 'kernel': 'rbf', 'gamma': 0.03981071705534969}
0.613 (+/-0.240) for {'C': 25.58585886905646, 'kernel': 'rbf', 'gamma': 0.3981071705534969}
0.499 (+/-0.002) for {'C': 25.58585886905646, 'kernel': 'rbf', 'gamma': 3.981071705534969}
0.500 (+/-0.000) for {'C': 25.58585886905646, 'kernel': 'rbf', 'gamma': 39.81071705534969}
0.500 (+/-0.000) for {'C': 25.58585886905646, 'kernel': 'rbf', 'gamma': 398.1071705534969}
0.500 (+/-0.000) for {'C': 25.58585886905646, 'kernel': 'rbf', 'gamma': 3981.0717055349733}
0.500 (+/-0.000) for {'C': 26.061535499988945, 'kernel': 'rbf', 'gamma': 3.981071705534969e-07}
0.500 (+/-0.000) for {'C': 26.061535499988945, 'kernel': 'rbf', 'gamma': 3.981071705534969e-06}
0.500 (+/-0.000) for {'C': 26.061535499988945, 'kernel': 'rbf', 'gamma': 3.9810717055349695e-05}
0.617 (+/-0.238) for {'C': 26.061535499988945, 'kernel': 'rbf', 'gamma': 0.0003981071705534969}
0.617 (+/-0.238) for {'C': 26.061535499988945, 'kernel': 'rbf', 'gamma': 0.003981071705534969}
0.641 (+/-0.235) for {'C': 26.061535499988945, 'kernel': 'rbf', 'gamma': 0.03981071705534969}
0.613 (+/-0.240) for {'C': 26.061535499988945, 'kernel': 'rbf', 'gamma': 0.3981071705534969}
0.499 (+/-0.002) for {'C': 26.061535499988945, 'kernel': 'rbf', 'gamma': 3.981071705534969}
0.500 (+/-0.000) for {'C': 26.061535499988945, 'kernel': 'rbf', 'gamma': 39.81071705534969}
0.500 (+/-0.000) for {'C': 26.061535499988945, 'kernel': 'rbf', 'gamma': 398.1071705534969}
0.500 (+/-0.000) for {'C': 26.061535499988945, 'kernel': 'rbf', 'gamma': 3981.0717055349733}
0.500 (+/-0.000) for {'C': 26.54605561975539, 'kernel': 'rbf', 'gamma': 3.981071705534969e-07}
0.500 (+/-0.000) for {'C': 26.54605561975539, 'kernel': 'rbf', 'gamma': 3.981071705534969e-06}
0.500 (+/-0.000) for {'C': 26.54605561975539, 'kernel': 'rbf', 'gamma': 3.9810717055349695e-05}
0.617 (+/-0.238) for {'C': 26.54605561975539, 'kernel': 'rbf', 'gamma': 0.0003981071705534969}
0.617 (+/-0.238) for {'C': 26.54605561975539, 'kernel': 'rbf', 'gamma': 0.003981071705534969}
0.641 (+/-0.235) for {'C': 26.54605561975539, 'kernel': 'rbf', 'gamma': 0.03981071705534969}
0.614 (+/-0.240) for {'C': 26.54605561975539, 'kernel': 'rbf', 'gamma': 0.3981071705534969}
0.499 (+/-0.002) for {'C': 26.54605561975539, 'kernel': 'rbf', 'gamma': 3.981071705534969}
0.500 (+/-0.000) for {'C': 26.54605561975539, 'kernel': 'rbf', 'gamma': 39.81071705534969}
0.500 (+/-0.000) for {'C': 26.54605561975539, 'kernel': 'rbf', 'gamma': 398.1071705534969}
0.500 (+/-0.000) for {'C': 26.54605561975539, 'kernel': 'rbf', 'gamma': 3981.0717055349733}
0.500 (+/-0.000) for {'C': 27.039583641088424, 'kernel': 'rbf', 'gamma': 3.981071705534969e-07}
0.500 (+/-0.000) for {'C': 27.039583641088424, 'kernel': 'rbf', 'gamma': 3.981071705534969e-06}
0.500 (+/-0.000) for {'C': 27.039583641088424, 'kernel': 'rbf', 'gamma': 3.9810717055349695e-05}
0.617 (+/-0.238) for {'C': 27.039583641088424, 'kernel': 'rbf', 'gamma': 0.0003981071705534969}
0.617 (+/-0.238) for {'C': 27.039583641088424, 'kernel': 'rbf', 'gamma': 0.003981071705534969}
0.666 (+/-0.315) for {'C': 27.039583641088424, 'kernel': 'rbf', 'gamma': 0.03981071705534969}
0.614 (+/-0.240) for {'C': 27.039583641088424, 'kernel': 'rbf', 'gamma': 0.3981071705534969}
0.499 (+/-0.002) for {'C': 27.039583641088424, 'kernel': 'rbf', 'gamma': 3.981071705534969}
0.500 (+/-0.000) for {'C': 27.039583641088424, 'kernel': 'rbf', 'gamma': 39.81071705534969}
0.500 (+/-0.000) for {'C': 27.039583641088424, 'kernel': 'rbf', 'gamma': 398.1071705534969}
0.500 (+/-0.000) for {'C': 27.039583641088424, 'kernel': 'rbf', 'gamma': 3981.0717055349733}
0.500 (+/-0.000) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 3.981071705534969e-07}
0.500 (+/-0.000) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 3.981071705534969e-06}
0.500 (+/-0.000) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 3.9810717055349695e-05}
0.617 (+/-0.238) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 0.0003981071705534969}
0.617 (+/-0.238) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 0.003981071705534969}
0.666 (+/-0.315) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 0.03981071705534969}
0.614 (+/-0.239) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 0.3981071705534969}
0.499 (+/-0.002) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 3.981071705534969}
0.500 (+/-0.000) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 39.81071705534969}
0.500 (+/-0.000) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 398.1071705534969}
0.500 (+/-0.000) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 3981.0717055349733}
0.500 (+/-0.000) for {'C': 28.054336379517135, 'kernel': 'rbf', 'gamma': 3.981071705534969e-07}
0.500 (+/-0.000) for {'C': 28.054336379517135, 'kernel': 'rbf', 'gamma': 3.981071705534969e-06}
0.500 (+/-0.000) for {'C': 28.054336379517135, 'kernel': 'rbf', 'gamma': 3.9810717055349695e-05}
0.617 (+/-0.238) for {'C': 28.054336379517135, 'kernel': 'rbf', 'gamma': 0.0003981071705534969}
0.617 (+/-0.238) for {'C': 28.054336379517135, 'kernel': 'rbf', 'gamma': 0.003981071705534969}
0.666 (+/-0.315) for {'C': 28.054336379517135, 'kernel': 'rbf', 'gamma': 0.03981071705534969}
0.614 (+/-0.239) for {'C': 28.054336379517135, 'kernel': 'rbf', 'gamma': 0.3981071705534969}
0.499 (+/-0.002) for {'C': 28.054336379517135, 'kernel': 'rbf', 'gamma': 3.981071705534969}
0.500 (+/-0.000) for {'C': 28.054336379517135, 'kernel': 'rbf', 'gamma': 39.81071705534969}
0.500 (+/-0.000) for {'C': 28.054336379517135, 'kernel': 'rbf', 'gamma': 398.1071705534969}
0.500 (+/-0.000) for {'C': 28.054336379517135, 'kernel': 'rbf', 'gamma': 3981.0717055349733}
0.500 (+/-0.000) for {'C': 28.57590543374946, 'kernel': 'rbf', 'gamma': 3.981071705534969e-07}
0.500 (+/-0.000) for {'C': 28.57590543374946, 'kernel': 'rbf', 'gamma': 3.981071705534969e-06}
0.500 (+/-0.000) for {'C': 28.57590543374946, 'kernel': 'rbf', 'gamma': 3.9810717055349695e-05}
0.617 (+/-0.238) for {'C': 28.57590543374946, 'kernel': 'rbf', 'gamma': 0.0003981071705534969}
0.617 (+/-0.238) for {'C': 28.57590543374946, 'kernel': 'rbf', 'gamma': 0.003981071705534969}
0.666 (+/-0.315) for {'C': 28.57590543374946, 'kernel': 'rbf', 'gamma': 0.03981071705534969}
0.614 (+/-0.239) for {'C': 28.57590543374946, 'kernel': 'rbf', 'gamma': 0.3981071705534969}
0.499 (+/-0.002) for {'C': 28.57590543374946, 'kernel': 'rbf', 'gamma': 3.981071705534969}
0.500 (+/-0.000) for {'C': 28.57590543374946, 'kernel': 'rbf', 'gamma': 39.81071705534969}
0.500 (+/-0.000) for {'C': 28.57590543374946, 'kernel': 'rbf', 'gamma': 398.1071705534969}
0.500 (+/-0.000) for {'C': 28.57590543374946, 'kernel': 'rbf', 'gamma': 3981.0717055349733}
0.500 (+/-0.000) for {'C': 29.10717118066605, 'kernel': 'rbf', 'gamma': 3.981071705534969e-07}
0.500 (+/-0.000) for {'C': 29.10717118066605, 'kernel': 'rbf', 'gamma': 3.981071705534969e-06}
0.500 (+/-0.000) for {'C': 29.10717118066605, 'kernel': 'rbf', 'gamma': 3.9810717055349695e-05}
0.617 (+/-0.238) for {'C': 29.10717118066605, 'kernel': 'rbf', 'gamma': 0.0003981071705534969}
0.617 (+/-0.238) for {'C': 29.10717118066605, 'kernel': 'rbf', 'gamma': 0.003981071705534969}
0.666 (+/-0.315) for {'C': 29.10717118066605, 'kernel': 'rbf', 'gamma': 0.03981071705534969}
0.614 (+/-0.239) for {'C': 29.10717118066605, 'kernel': 'rbf', 'gamma': 0.3981071705534969}
0.499 (+/-0.002) for {'C': 29.10717118066605, 'kernel': 'rbf', 'gamma': 3.981071705534969}
0.500 (+/-0.000) for {'C': 29.10717118066605, 'kernel': 'rbf', 'gamma': 39.81071705534969}
0.500 (+/-0.000) for {'C': 29.10717118066605, 'kernel': 'rbf', 'gamma': 398.1071705534969}
0.500 (+/-0.000) for {'C': 29.10717118066605, 'kernel': 'rbf', 'gamma': 3981.0717055349733}
0.500 (+/-0.000) for {'C': 29.648313895243408, 'kernel': 'rbf', 'gamma': 3.981071705534969e-07}
0.500 (+/-0.000) for {'C': 29.648313895243408, 'kernel': 'rbf', 'gamma': 3.981071705534969e-06}
0.500 (+/-0.000) for {'C': 29.648313895243408, 'kernel': 'rbf', 'gamma': 3.9810717055349695e-05}
0.617 (+/-0.238) for {'C': 29.648313895243408, 'kernel': 'rbf', 'gamma': 0.0003981071705534969}
0.617 (+/-0.238) for {'C': 29.648313895243408, 'kernel': 'rbf', 'gamma': 0.003981071705534969}
0.666 (+/-0.315) for {'C': 29.648313895243408, 'kernel': 'rbf', 'gamma': 0.03981071705534969}
0.614 (+/-0.239) for {'C': 29.648313895243408, 'kernel': 'rbf', 'gamma': 0.3981071705534969}
0.499 (+/-0.002) for {'C': 29.648313895243408, 'kernel': 'rbf', 'gamma': 3.981071705534969}
0.500 (+/-0.000) for {'C': 29.648313895243408, 'kernel': 'rbf', 'gamma': 39.81071705534969}
0.500 (+/-0.000) for {'C': 29.648313895243408, 'kernel': 'rbf', 'gamma': 398.1071705534969}
0.500 (+/-0.000) for {'C': 29.648313895243408, 'kernel': 'rbf', 'gamma': 3981.0717055349733}
0.500 (+/-0.000) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 3.981071705534969e-07}
0.500 (+/-0.000) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 3.981071705534969e-06}
0.500 (+/-0.000) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 3.9810717055349695e-05}
0.617 (+/-0.238) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 0.0003981071705534969}
0.617 (+/-0.238) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 0.003981071705534969}
0.666 (+/-0.315) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 0.03981071705534969}
0.614 (+/-0.239) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 0.3981071705534969}
0.499 (+/-0.002) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 3.981071705534969}
0.500 (+/-0.000) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 39.81071705534969}
0.500 (+/-0.000) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 398.1071705534969}
0.500 (+/-0.000) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 3981.0717055349733}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.641 (+/-0.235) for {'C': 1.0, 'kernel': 'linear'}
0.660 (+/-0.315) for {'C': 10.0, 'kernel': 'linear'}
0.613 (+/-0.242) for {'C': 100.0, 'kernel': 'linear'}
0.613 (+/-0.241) for {'C': 1000.0, 'kernel': 'linear'}
0.613 (+/-0.241) for {'C': 10000.0, 'kernel': 'linear'}
0.613 (+/-0.241) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 27.039583641088424, 'kernel': 'rbf', 'gamma': 0.03981071705534969}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6658653846153846
第1轮loop中，tunemodel函数得到的最优参数是： ([{'C': array([26.54605562, 26.64403528, 26.74237657, 26.84108084, 26.94014941,
       27.03958364, 27.13938488, 27.23955447, 27.34009379, 27.44100418,
       27.54228703]), 'kernel': ['rbf'], 'gamma': array([0.00398107, 0.00630957, 0.01      , 0.01584893, 0.02511886,
       0.03981072, 0.06309573, 0.1       , 0.15848932, 0.25118864,
       0.39810717])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}], {'C': 27.039583641088424, 'kernel': 'rbf', 'gamma': 0.03981071705534969}, 0.6658653846153846)
这是第1次迭代微调C和gamma。
第1次迭代，得到delta： [5.02703392e-01 4.16333634e-17]
SVC模型clf_op_iter的参数是： {'kernel': 'rbf', 'decision_function_shape': 'ovr', 'class_weight': {-1: 15}, 'tol': 0.001, 'gamma': 0.03981071705534969, 'random_state': 0, 'cache_size': 200, 'verbose': False, 'degree': 3, 'C': 27.039583641088424, 'probability': False, 'shrinking': True, 'coef0': 0.0, 'max_iter': -1}

训练模型SVC对训练样本的预测准确率： 0.9468462083628633

测试集中，预测为舞弊样本的有： (array([ 370, 1246, 1247, 1248, 1251, 1252, 1255, 1256], dtype=int64),) 
测试集中，实际为舞弊样本的有： (array([1246, 1247, 1248, 1249, 1250, 1251, 1252, 1253, 1254, 1255, 1256],
      dtype=int64),) 
预测的舞弊样本数目： 8

训练模型SVC对测试样本的预测准确率： 0.9567682494684621
以上是第16次特征筛选。
第16次特征筛选，AUC值是： 0.8177805340726688
X_train_iter_svc.shape is: (1257, 36)
X_test_iter_svc.shape is: (1257, 36)
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.622 (+/-0.336) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.590 (+/-0.230) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6166666666666667
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.723 (+/-0.417) for {'C': 1.0, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.630 (+/-0.310) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6661858974358974
RBF的粗grid search最佳超参： {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0} RBF的粗grid search最高分值： 0.6166666666666667
linear的粗grid search最佳超参： {'C': 1.0, 'kernel': 'linear'} linear的粗grid search最高分值： 0.6661858974358974
粗grid search得到的parameter是：
 [{'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05]), 'kernel': ['rbf'], 'gamma': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.629 (+/-0.329) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.622 (+/-0.336) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.372) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.319) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.622 (+/-0.336) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.674 (+/-0.375) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.310) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.622 (+/-0.336) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.674 (+/-0.375) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.630 (+/-0.310) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.310) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.622 (+/-0.336) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.689 (+/-0.436) for {'C': 10.0, 'kernel': 'linear'}
0.630 (+/-0.310) for {'C': 100.0, 'kernel': 'linear'}
0.630 (+/-0.310) for {'C': 1000.0, 'kernel': 'linear'}
0.630 (+/-0.310) for {'C': 10000.0, 'kernel': 'linear'}
0.630 (+/-0.310) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      1.00       623

avg / total       0.98      0.99      0.99       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.590 (+/-0.230) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.639 (+/-0.236) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.241) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.590 (+/-0.230) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.590 (+/-0.230) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.639 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.637 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.590 (+/-0.230) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.616 (+/-0.238) for {'C': 10.0, 'kernel': 'linear'}
0.637 (+/-0.239) for {'C': 100.0, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 10000.0, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.12      0.17      0.14         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6392628205128205
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.664 (+/-0.388) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.629 (+/-0.329) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.383) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.622 (+/-0.336) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.698 (+/-0.383) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.646 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.319) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.622 (+/-0.336) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.698 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.654 (+/-0.393) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.310) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.622 (+/-0.336) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.698 (+/-0.383) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.665 (+/-0.381) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.630 (+/-0.310) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.310) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.622 (+/-0.336) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.723 (+/-0.417) for {'C': 1.0, 'kernel': 'linear'}
0.654 (+/-0.379) for {'C': 10.0, 'kernel': 'linear'}
0.630 (+/-0.310) for {'C': 100.0, 'kernel': 'linear'}
0.630 (+/-0.310) for {'C': 1000.0, 'kernel': 'linear'}
0.630 (+/-0.310) for {'C': 10000.0, 'kernel': 'linear'}
0.630 (+/-0.310) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      1.00       623

avg / total       0.98      0.99      0.99       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.315) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.587 (+/-0.235) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.590 (+/-0.230) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.666 (+/-0.315) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.611 (+/-0.243) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.241) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.590 (+/-0.230) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.315) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.611 (+/-0.243) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.590 (+/-0.230) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.661 (+/-0.316) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.637 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.590 (+/-0.230) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.659 (+/-0.318) for {'C': 10.0, 'kernel': 'linear'}
0.637 (+/-0.239) for {'C': 100.0, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 10000.0, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6661858974358974
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.629 (+/-0.329) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.622 (+/-0.336) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.372) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.319) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.622 (+/-0.336) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.674 (+/-0.375) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.310) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.622 (+/-0.336) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.674 (+/-0.375) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.630 (+/-0.310) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.310) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.622 (+/-0.336) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.664 (+/-0.388) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.689 (+/-0.436) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.689 (+/-0.436) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      1.00       623

avg / total       0.98      0.99      0.99       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.590 (+/-0.230) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.639 (+/-0.236) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.241) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.590 (+/-0.230) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.590 (+/-0.230) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.639 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.637 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.590 (+/-0.230) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.616 (+/-0.238) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.12      0.17      0.14         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6392628205128205
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.664 (+/-0.388) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.629 (+/-0.329) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.383) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.622 (+/-0.336) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.698 (+/-0.383) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.646 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.319) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.622 (+/-0.336) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.698 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.654 (+/-0.393) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.310) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.622 (+/-0.336) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.698 (+/-0.383) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.665 (+/-0.381) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.630 (+/-0.310) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.310) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.622 (+/-0.336) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.723 (+/-0.417) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.689 (+/-0.374) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.698 (+/-0.383) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.681 (+/-0.372) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.662 (+/-0.373) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.654 (+/-0.379) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      1.00       623

avg / total       0.98      0.99      0.99       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.315) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.587 (+/-0.235) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.590 (+/-0.230) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.666 (+/-0.315) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.611 (+/-0.243) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.241) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.590 (+/-0.230) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.315) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.611 (+/-0.243) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.590 (+/-0.230) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.661 (+/-0.316) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.637 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.590 (+/-0.230) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.666 (+/-0.314) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.666 (+/-0.315) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.665 (+/-0.314) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.663 (+/-0.315) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.659 (+/-0.318) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 0.9999999999999997, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6661858974358974
循环迭代之前，delta is: [3.33066907e-16]
SVC模型clf_op_iter的参数是： {'kernel': 'linear', 'decision_function_shape': 'ovr', 'class_weight': {-1: 30}, 'tol': 0.001, 'gamma': 'auto', 'random_state': 0, 'cache_size': 200, 'verbose': False, 'degree': 3, 'C': 0.9999999999999997, 'probability': False, 'shrinking': True, 'coef0': 0.0, 'max_iter': -1}

训练模型SVC对训练样本的预测准确率： 0.9016497461928934

测试集中，预测为舞弊样本的有： (array([ 370, 1248, 1251, 1252, 1255, 1256], dtype=int64),) 
测试集中，实际为舞弊样本的有： (array([1246, 1247, 1248, 1249, 1250, 1251, 1252, 1253, 1254, 1255, 1256],
      dtype=int64),) 
预测的舞弊样本数目： 6

训练模型SVC对测试样本的预测准确率： 0.8851522842639594
以上是第17次特征筛选。
第17次特征筛选，AUC值是： 0.726871443163578
X_train_iter_svc.shape is: (1257, 35)
X_test_iter_svc.shape is: (1257, 35)
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.622 (+/-0.336) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.590 (+/-0.230) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6166666666666667
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.723 (+/-0.417) for {'C': 1.0, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.630 (+/-0.310) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6661858974358974
RBF的粗grid search最佳超参： {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0} RBF的粗grid search最高分值： 0.6166666666666667
linear的粗grid search最佳超参： {'C': 1.0, 'kernel': 'linear'} linear的粗grid search最高分值： 0.6661858974358974
粗grid search得到的parameter是：
 [{'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05]), 'kernel': ['rbf'], 'gamma': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.629 (+/-0.329) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.622 (+/-0.336) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.372) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.319) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.622 (+/-0.336) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.674 (+/-0.375) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.310) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.622 (+/-0.336) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.674 (+/-0.375) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.630 (+/-0.310) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.310) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.622 (+/-0.336) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.689 (+/-0.436) for {'C': 10.0, 'kernel': 'linear'}
0.630 (+/-0.310) for {'C': 100.0, 'kernel': 'linear'}
0.630 (+/-0.310) for {'C': 1000.0, 'kernel': 'linear'}
0.630 (+/-0.310) for {'C': 10000.0, 'kernel': 'linear'}
0.630 (+/-0.310) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      1.00       623

avg / total       0.98      0.99      0.99       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.590 (+/-0.230) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.639 (+/-0.236) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.241) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.590 (+/-0.230) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.590 (+/-0.230) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.639 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.637 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.590 (+/-0.230) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.616 (+/-0.238) for {'C': 10.0, 'kernel': 'linear'}
0.637 (+/-0.239) for {'C': 100.0, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 10000.0, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.12      0.17      0.14         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6392628205128205
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.664 (+/-0.388) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.629 (+/-0.329) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.383) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.622 (+/-0.336) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.698 (+/-0.383) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.646 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.319) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.622 (+/-0.336) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.698 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.654 (+/-0.393) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.310) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.622 (+/-0.336) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.698 (+/-0.383) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.665 (+/-0.381) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.630 (+/-0.310) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.310) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.622 (+/-0.336) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.723 (+/-0.417) for {'C': 1.0, 'kernel': 'linear'}
0.654 (+/-0.379) for {'C': 10.0, 'kernel': 'linear'}
0.630 (+/-0.310) for {'C': 100.0, 'kernel': 'linear'}
0.630 (+/-0.310) for {'C': 1000.0, 'kernel': 'linear'}
0.630 (+/-0.310) for {'C': 10000.0, 'kernel': 'linear'}
0.630 (+/-0.310) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      1.00       623

avg / total       0.98      0.99      0.99       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.315) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.587 (+/-0.235) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.590 (+/-0.230) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.666 (+/-0.315) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.611 (+/-0.243) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.241) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.590 (+/-0.230) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.315) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.611 (+/-0.243) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.590 (+/-0.230) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.661 (+/-0.316) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.637 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.590 (+/-0.230) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.659 (+/-0.318) for {'C': 10.0, 'kernel': 'linear'}
0.637 (+/-0.239) for {'C': 100.0, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 10000.0, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6661858974358974
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.629 (+/-0.329) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.622 (+/-0.336) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.372) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.319) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.622 (+/-0.336) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.674 (+/-0.375) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.310) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.622 (+/-0.336) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.674 (+/-0.375) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.630 (+/-0.310) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.310) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.622 (+/-0.336) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.664 (+/-0.388) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.689 (+/-0.436) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.689 (+/-0.436) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      1.00       623

avg / total       0.98      0.99      0.99       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.590 (+/-0.230) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.639 (+/-0.236) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.241) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.590 (+/-0.230) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.590 (+/-0.230) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.639 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.637 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.590 (+/-0.230) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.616 (+/-0.238) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.12      0.17      0.14         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6392628205128205
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.664 (+/-0.388) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.629 (+/-0.329) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.383) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.622 (+/-0.336) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.698 (+/-0.383) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.646 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.319) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.622 (+/-0.336) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.698 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.654 (+/-0.393) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.310) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.622 (+/-0.336) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.698 (+/-0.383) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.665 (+/-0.381) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.630 (+/-0.310) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.310) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.622 (+/-0.336) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.723 (+/-0.417) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.689 (+/-0.374) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.698 (+/-0.383) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.681 (+/-0.372) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.662 (+/-0.373) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.654 (+/-0.379) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      1.00       623

avg / total       0.98      0.99      0.99       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.315) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.587 (+/-0.235) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.590 (+/-0.230) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.666 (+/-0.315) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.611 (+/-0.243) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.241) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.590 (+/-0.230) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.315) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.611 (+/-0.243) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.590 (+/-0.230) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.661 (+/-0.316) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.637 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.590 (+/-0.230) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.666 (+/-0.314) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.666 (+/-0.315) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.665 (+/-0.314) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.663 (+/-0.315) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.659 (+/-0.318) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 0.9999999999999997, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6661858974358974
循环迭代之前，delta is: [3.33066907e-16]
SVC模型clf_op_iter的参数是： {'kernel': 'linear', 'decision_function_shape': 'ovr', 'class_weight': {-1: 30}, 'tol': 0.001, 'gamma': 'auto', 'random_state': 0, 'cache_size': 200, 'verbose': False, 'degree': 3, 'C': 0.9999999999999997, 'probability': False, 'shrinking': True, 'coef0': 0.0, 'max_iter': -1}

训练模型SVC对训练样本的预测准确率： 0.9016497461928934

测试集中，预测为舞弊样本的有： (array([ 370, 1248, 1252, 1255, 1256], dtype=int64),) 
测试集中，实际为舞弊样本的有： (array([1246, 1247, 1248, 1249, 1250, 1251, 1252, 1253, 1254, 1255, 1256],
      dtype=int64),) 
预测的舞弊样本数目： 5

训练模型SVC对测试样本的预测准确率： 0.8661167512690355
以上是第18次特征筛选。
第18次特征筛选，AUC值是： 0.6814168977090325
X_train_iter_svc.shape is: (1257, 34)
X_test_iter_svc.shape is: (1257, 34)
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.660 (+/-0.389) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.614 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6166666666666667
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.638 (+/-0.318) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6663461538461538
RBF的粗grid search最佳超参： {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0} RBF的粗grid search最高分值： 0.6166666666666667
linear的粗grid search最佳超参： {'C': 1.0, 'kernel': 'linear'} linear的粗grid search最高分值： 0.6663461538461538
粗grid search得到的parameter是：
 [{'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05]), 'kernel': ['rbf'], 'gamma': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.656 (+/-0.392) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.664 (+/-0.388) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.632 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.662 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.632 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.660 (+/-0.389) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.637 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.663 (+/-0.382) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.632 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'linear'}
0.638 (+/-0.318) for {'C': 100.0, 'kernel': 'linear'}
0.638 (+/-0.318) for {'C': 1000.0, 'kernel': 'linear'}
0.638 (+/-0.318) for {'C': 10000.0, 'kernel': 'linear'}
0.638 (+/-0.318) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      1.00       623

avg / total       0.98      0.99      0.99       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.241) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.614 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.637 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.616 (+/-0.238) for {'C': 10.0, 'kernel': 'linear'}
0.637 (+/-0.237) for {'C': 100.0, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 10000.0, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 100.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6375
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.664 (+/-0.388) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.656 (+/-0.392) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.383) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.647 (+/-0.401) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.698 (+/-0.383) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.654 (+/-0.393) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.632 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.698 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.653 (+/-0.394) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.662 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.632 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.723 (+/-0.423) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.654 (+/-0.393) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.637 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.663 (+/-0.382) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.632 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.660 (+/-0.384) for {'C': 10.0, 'kernel': 'linear'}
0.638 (+/-0.318) for {'C': 100.0, 'kernel': 'linear'}
0.638 (+/-0.318) for {'C': 1000.0, 'kernel': 'linear'}
0.638 (+/-0.318) for {'C': 10000.0, 'kernel': 'linear'}
0.638 (+/-0.318) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.240) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.315) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.587 (+/-0.236) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.666 (+/-0.315) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.612 (+/-0.242) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.241) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.315) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.611 (+/-0.242) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.611 (+/-0.243) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.637 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.660 (+/-0.313) for {'C': 10.0, 'kernel': 'linear'}
0.637 (+/-0.237) for {'C': 100.0, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 10000.0, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6663461538461538
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.656 (+/-0.392) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.664 (+/-0.388) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.632 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.662 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.632 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.660 (+/-0.389) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.637 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.663 (+/-0.382) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.632 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.689 (+/-0.436) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.689 (+/-0.436) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      1.00       623

avg / total       0.98      0.99      0.99       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.241) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.614 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.637 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.616 (+/-0.238) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6373397435897435
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.664 (+/-0.388) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.656 (+/-0.392) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.383) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.647 (+/-0.401) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.698 (+/-0.383) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.654 (+/-0.393) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.632 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.698 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.653 (+/-0.394) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.662 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.632 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.723 (+/-0.423) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.654 (+/-0.393) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.637 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.663 (+/-0.382) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.632 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.698 (+/-0.383) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.723 (+/-0.423) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.681 (+/-0.372) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.666 (+/-0.371) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.660 (+/-0.384) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.240) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.315) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.587 (+/-0.236) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.666 (+/-0.315) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.612 (+/-0.242) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.241) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.315) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.611 (+/-0.242) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.611 (+/-0.243) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.637 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.666 (+/-0.315) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.666 (+/-0.315) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.665 (+/-0.314) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.663 (+/-0.316) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.660 (+/-0.313) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 0.9999999999999997, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6663461538461538
循环迭代之前，delta is: [3.33066907e-16]
SVC模型clf_op_iter的参数是： {'kernel': 'linear', 'decision_function_shape': 'ovr', 'class_weight': {-1: 30}, 'tol': 0.001, 'gamma': 'auto', 'random_state': 0, 'cache_size': 200, 'verbose': False, 'degree': 3, 'C': 0.9999999999999997, 'probability': False, 'shrinking': True, 'coef0': 0.0, 'max_iter': -1}

训练模型SVC对训练样本的预测准确率： 0.9016497461928934

测试集中，预测为舞弊样本的有： (array([ 370, 1248, 1252, 1255, 1256], dtype=int64),) 
测试集中，实际为舞弊样本的有： (array([1246, 1247, 1248, 1249, 1250, 1251, 1252, 1253, 1254, 1255, 1256],
      dtype=int64),) 
预测的舞弊样本数目： 5

训练模型SVC对测试样本的预测准确率： 0.8661167512690355
以上是第19次特征筛选。
第19次特征筛选，AUC值是： 0.6814168977090325
X_train_iter_svc.shape is: (1257, 33)
X_test_iter_svc.shape is: (1257, 33)
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.660 (+/-0.389) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.614 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6166666666666667
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.638 (+/-0.318) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6663461538461538
RBF的粗grid search最佳超参： {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0} RBF的粗grid search最高分值： 0.6166666666666667
linear的粗grid search最佳超参： {'C': 1.0, 'kernel': 'linear'} linear的粗grid search最高分值： 0.6663461538461538
粗grid search得到的parameter是：
 [{'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05]), 'kernel': ['rbf'], 'gamma': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.656 (+/-0.392) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.664 (+/-0.388) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.632 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.662 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.632 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.660 (+/-0.389) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.637 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.663 (+/-0.382) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.632 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'linear'}
0.638 (+/-0.318) for {'C': 100.0, 'kernel': 'linear'}
0.638 (+/-0.318) for {'C': 1000.0, 'kernel': 'linear'}
0.638 (+/-0.318) for {'C': 10000.0, 'kernel': 'linear'}
0.638 (+/-0.318) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      1.00       623

avg / total       0.98      0.99      0.99       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.241) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.614 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.637 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.616 (+/-0.238) for {'C': 10.0, 'kernel': 'linear'}
0.637 (+/-0.237) for {'C': 100.0, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 10000.0, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 100.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6375
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.664 (+/-0.388) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.656 (+/-0.392) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.383) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.647 (+/-0.401) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.698 (+/-0.383) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.654 (+/-0.393) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.632 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.698 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.653 (+/-0.394) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.662 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.632 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.723 (+/-0.423) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.654 (+/-0.393) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.637 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.663 (+/-0.382) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.632 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.660 (+/-0.384) for {'C': 10.0, 'kernel': 'linear'}
0.638 (+/-0.318) for {'C': 100.0, 'kernel': 'linear'}
0.638 (+/-0.318) for {'C': 1000.0, 'kernel': 'linear'}
0.638 (+/-0.318) for {'C': 10000.0, 'kernel': 'linear'}
0.638 (+/-0.318) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.240) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.315) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.587 (+/-0.236) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.666 (+/-0.315) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.612 (+/-0.242) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.241) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.315) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.611 (+/-0.242) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.611 (+/-0.243) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.637 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.660 (+/-0.313) for {'C': 10.0, 'kernel': 'linear'}
0.637 (+/-0.237) for {'C': 100.0, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 10000.0, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6663461538461538
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.656 (+/-0.392) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.664 (+/-0.388) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.632 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.662 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.632 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.660 (+/-0.389) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.637 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.663 (+/-0.382) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.632 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.689 (+/-0.436) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.689 (+/-0.436) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      1.00       623

avg / total       0.98      0.99      0.99       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.241) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.614 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.637 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.616 (+/-0.238) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6373397435897435
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.664 (+/-0.388) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.656 (+/-0.392) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.383) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.647 (+/-0.401) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.698 (+/-0.383) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.654 (+/-0.393) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.632 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.698 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.653 (+/-0.394) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.662 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.632 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.723 (+/-0.423) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.654 (+/-0.393) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.637 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.663 (+/-0.382) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.632 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.698 (+/-0.383) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.698 (+/-0.383) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.681 (+/-0.372) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.666 (+/-0.371) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.660 (+/-0.384) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.240) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.315) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.587 (+/-0.236) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.666 (+/-0.315) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.612 (+/-0.242) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.241) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.315) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.611 (+/-0.242) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.611 (+/-0.243) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.637 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.666 (+/-0.315) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.666 (+/-0.315) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.665 (+/-0.314) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.663 (+/-0.316) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.660 (+/-0.313) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 0.9999999999999997, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6663461538461538
循环迭代之前，delta is: [3.33066907e-16]
SVC模型clf_op_iter的参数是： {'kernel': 'linear', 'decision_function_shape': 'ovr', 'class_weight': {-1: 30}, 'tol': 0.001, 'gamma': 'auto', 'random_state': 0, 'cache_size': 200, 'verbose': False, 'degree': 3, 'C': 0.9999999999999997, 'probability': False, 'shrinking': True, 'coef0': 0.0, 'max_iter': -1}

训练模型SVC对训练样本的预测准确率： 0.9016497461928934

测试集中，预测为舞弊样本的有： (array([ 370, 1248, 1252, 1255, 1256], dtype=int64),) 
测试集中，实际为舞弊样本的有： (array([1246, 1247, 1248, 1249, 1250, 1251, 1252, 1253, 1254, 1255, 1256],
      dtype=int64),) 
预测的舞弊样本数目： 5

训练模型SVC对测试样本的预测准确率： 0.8661167512690355
以上是第20次特征筛选。
第20次特征筛选，AUC值是： 0.6814168977090325
X_train_iter_svc.shape is: (1257, 32)
X_test_iter_svc.shape is: (1257, 32)
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.660 (+/-0.389) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.614 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6166666666666667
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.638 (+/-0.318) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6663461538461538
RBF的粗grid search最佳超参： {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0} RBF的粗grid search最高分值： 0.6166666666666667
linear的粗grid search最佳超参： {'C': 1.0, 'kernel': 'linear'} linear的粗grid search最高分值： 0.6663461538461538
粗grid search得到的parameter是：
 [{'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05]), 'kernel': ['rbf'], 'gamma': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.656 (+/-0.392) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.664 (+/-0.388) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.632 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.662 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.632 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.660 (+/-0.389) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.637 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.663 (+/-0.382) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.632 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'linear'}
0.638 (+/-0.318) for {'C': 100.0, 'kernel': 'linear'}
0.638 (+/-0.318) for {'C': 1000.0, 'kernel': 'linear'}
0.638 (+/-0.318) for {'C': 10000.0, 'kernel': 'linear'}
0.638 (+/-0.318) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      1.00       623

avg / total       0.98      0.99      0.99       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.241) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.614 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.637 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.616 (+/-0.238) for {'C': 10.0, 'kernel': 'linear'}
0.637 (+/-0.237) for {'C': 100.0, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 10000.0, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 100.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6375
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.664 (+/-0.388) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.656 (+/-0.392) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.383) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.647 (+/-0.401) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.698 (+/-0.383) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.654 (+/-0.393) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.632 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.698 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.653 (+/-0.394) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.662 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.632 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.723 (+/-0.423) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.653 (+/-0.394) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.637 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.663 (+/-0.382) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.632 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.660 (+/-0.384) for {'C': 10.0, 'kernel': 'linear'}
0.638 (+/-0.318) for {'C': 100.0, 'kernel': 'linear'}
0.638 (+/-0.318) for {'C': 1000.0, 'kernel': 'linear'}
0.638 (+/-0.318) for {'C': 10000.0, 'kernel': 'linear'}
0.638 (+/-0.318) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.240) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.315) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.587 (+/-0.236) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.666 (+/-0.315) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.612 (+/-0.242) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.241) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.315) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.611 (+/-0.242) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.611 (+/-0.242) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.637 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.660 (+/-0.313) for {'C': 10.0, 'kernel': 'linear'}
0.637 (+/-0.237) for {'C': 100.0, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 10000.0, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6663461538461538
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.656 (+/-0.392) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.664 (+/-0.388) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.632 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.662 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.632 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.660 (+/-0.389) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.637 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.663 (+/-0.382) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.632 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.689 (+/-0.436) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.689 (+/-0.436) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      1.00       623

avg / total       0.98      0.99      0.99       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.241) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.614 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.637 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.616 (+/-0.238) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6373397435897435
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.664 (+/-0.388) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.656 (+/-0.392) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.383) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.647 (+/-0.401) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.698 (+/-0.383) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.654 (+/-0.393) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.632 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.698 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.653 (+/-0.394) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.662 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.632 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.723 (+/-0.423) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.653 (+/-0.394) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.637 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.663 (+/-0.382) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.632 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.698 (+/-0.383) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.698 (+/-0.383) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.706 (+/-0.418) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.666 (+/-0.371) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.660 (+/-0.384) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.240) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.315) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.587 (+/-0.236) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.666 (+/-0.315) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.612 (+/-0.242) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.241) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.315) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.611 (+/-0.242) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.611 (+/-0.242) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.637 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.666 (+/-0.315) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.666 (+/-0.315) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.665 (+/-0.314) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.663 (+/-0.316) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.660 (+/-0.313) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 0.9999999999999997, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6663461538461538
循环迭代之前，delta is: [3.33066907e-16]
SVC模型clf_op_iter的参数是： {'kernel': 'linear', 'decision_function_shape': 'ovr', 'class_weight': {-1: 30}, 'tol': 0.001, 'gamma': 'auto', 'random_state': 0, 'cache_size': 200, 'verbose': False, 'degree': 3, 'C': 0.9999999999999997, 'probability': False, 'shrinking': True, 'coef0': 0.0, 'max_iter': -1}

训练模型SVC对训练样本的预测准确率： 0.9016497461928934

测试集中，预测为舞弊样本的有： (array([ 370, 1248, 1252, 1255, 1256], dtype=int64),) 
测试集中，实际为舞弊样本的有： (array([1246, 1247, 1248, 1249, 1250, 1251, 1252, 1253, 1254, 1255, 1256],
      dtype=int64),) 
预测的舞弊样本数目： 5

训练模型SVC对测试样本的预测准确率： 0.8661167512690355
以上是第21次特征筛选。
第21次特征筛选，AUC值是： 0.6814168977090325
X_train_iter_svc.shape is: (1257, 31)
X_test_iter_svc.shape is: (1257, 31)
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.660 (+/-0.389) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.614 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6166666666666667
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.638 (+/-0.318) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6663461538461538
RBF的粗grid search最佳超参： {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0} RBF的粗grid search最高分值： 0.6166666666666667
linear的粗grid search最佳超参： {'C': 1.0, 'kernel': 'linear'} linear的粗grid search最高分值： 0.6663461538461538
粗grid search得到的parameter是：
 [{'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05]), 'kernel': ['rbf'], 'gamma': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.656 (+/-0.392) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.664 (+/-0.388) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.632 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.662 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.632 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.660 (+/-0.389) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.637 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.654 (+/-0.392) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.632 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'linear'}
0.638 (+/-0.318) for {'C': 100.0, 'kernel': 'linear'}
0.638 (+/-0.318) for {'C': 1000.0, 'kernel': 'linear'}
0.638 (+/-0.318) for {'C': 10000.0, 'kernel': 'linear'}
0.638 (+/-0.318) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      1.00       623

avg / total       0.98      0.99      0.99       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.241) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.615 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.637 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.612 (+/-0.241) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.616 (+/-0.238) for {'C': 10.0, 'kernel': 'linear'}
0.637 (+/-0.237) for {'C': 100.0, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 10000.0, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 100.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6375
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.664 (+/-0.388) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.656 (+/-0.392) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.383) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.647 (+/-0.401) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.698 (+/-0.383) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.654 (+/-0.393) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.632 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.698 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.653 (+/-0.394) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.662 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.632 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.698 (+/-0.383) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.653 (+/-0.394) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.637 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.654 (+/-0.392) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.632 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.661 (+/-0.384) for {'C': 10.0, 'kernel': 'linear'}
0.638 (+/-0.318) for {'C': 100.0, 'kernel': 'linear'}
0.638 (+/-0.318) for {'C': 1000.0, 'kernel': 'linear'}
0.638 (+/-0.318) for {'C': 10000.0, 'kernel': 'linear'}
0.638 (+/-0.318) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.240) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.315) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.587 (+/-0.236) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.666 (+/-0.315) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.611 (+/-0.243) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.241) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.315) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.611 (+/-0.242) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.611 (+/-0.242) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.637 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.612 (+/-0.241) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.660 (+/-0.314) for {'C': 10.0, 'kernel': 'linear'}
0.637 (+/-0.237) for {'C': 100.0, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 10000.0, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6663461538461538
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.656 (+/-0.392) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.664 (+/-0.388) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.632 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.662 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.632 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.660 (+/-0.389) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.637 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.654 (+/-0.392) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.632 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.689 (+/-0.436) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.689 (+/-0.436) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      1.00       623

avg / total       0.98      0.99      0.99       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.241) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.615 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.637 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.612 (+/-0.241) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.616 (+/-0.238) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6371794871794872
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.664 (+/-0.388) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.656 (+/-0.392) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.383) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.647 (+/-0.401) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.698 (+/-0.383) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.654 (+/-0.393) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.632 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.698 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.653 (+/-0.394) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.662 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.632 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.698 (+/-0.383) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.653 (+/-0.394) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.637 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.654 (+/-0.392) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.632 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.698 (+/-0.383) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.698 (+/-0.383) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.706 (+/-0.418) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.666 (+/-0.371) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.661 (+/-0.384) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.240) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.315) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.587 (+/-0.236) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.666 (+/-0.315) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.611 (+/-0.243) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.241) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.315) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.611 (+/-0.242) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.611 (+/-0.242) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.637 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.612 (+/-0.241) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.666 (+/-0.315) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.666 (+/-0.315) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.665 (+/-0.314) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.663 (+/-0.316) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.660 (+/-0.314) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 0.9999999999999997, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6663461538461538
循环迭代之前，delta is: [3.33066907e-16]
SVC模型clf_op_iter的参数是： {'kernel': 'linear', 'decision_function_shape': 'ovr', 'class_weight': {-1: 30}, 'tol': 0.001, 'gamma': 'auto', 'random_state': 0, 'cache_size': 200, 'verbose': False, 'degree': 3, 'C': 0.9999999999999997, 'probability': False, 'shrinking': True, 'coef0': 0.0, 'max_iter': -1}

训练模型SVC对训练样本的预测准确率： 0.9016497461928934

测试集中，预测为舞弊样本的有： (array([1248, 1252, 1255, 1256], dtype=int64),) 
测试集中，实际为舞弊样本的有： (array([1246, 1247, 1248, 1249, 1250, 1251, 1252, 1253, 1254, 1255, 1256],
      dtype=int64),) 
预测的舞弊样本数目： 4

训练模型SVC对测试样本的预测准确率： 0.866751269035533
以上是第22次特征筛选。
第22次特征筛选，AUC值是： 0.6818181818181819
X_train_iter_svc.shape is: (1257, 30)
X_test_iter_svc.shape is: (1257, 30)
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.660 (+/-0.389) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.614 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6166666666666667
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.637 (+/-0.319) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6663461538461538
RBF的粗grid search最佳超参： {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0} RBF的粗grid search最高分值： 0.6166666666666667
linear的粗grid search最佳超参： {'C': 1.0, 'kernel': 'linear'} linear的粗grid search最高分值： 0.6663461538461538
粗grid search得到的parameter是：
 [{'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05]), 'kernel': ['rbf'], 'gamma': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.656 (+/-0.392) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.664 (+/-0.388) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.632 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.662 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.632 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.660 (+/-0.389) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.638 (+/-0.318) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.654 (+/-0.392) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.632 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'linear'}
0.638 (+/-0.318) for {'C': 100.0, 'kernel': 'linear'}
0.637 (+/-0.319) for {'C': 1000.0, 'kernel': 'linear'}
0.637 (+/-0.319) for {'C': 10000.0, 'kernel': 'linear'}
0.637 (+/-0.319) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      1.00       623

avg / total       0.98      0.99      0.99       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.241) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.240) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.614 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.637 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.612 (+/-0.241) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.616 (+/-0.238) for {'C': 10.0, 'kernel': 'linear'}
0.637 (+/-0.237) for {'C': 100.0, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.637 (+/-0.237) for {'C': 10000.0, 'kernel': 'linear'}
0.637 (+/-0.237) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.98      0.99       623

avg / total       0.98      0.97      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 100.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6375
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.664 (+/-0.388) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.654 (+/-0.392) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.383) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.647 (+/-0.401) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.698 (+/-0.383) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.654 (+/-0.393) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.632 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.698 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.653 (+/-0.394) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.662 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.632 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.698 (+/-0.383) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.653 (+/-0.394) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.638 (+/-0.318) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.654 (+/-0.392) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.632 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.661 (+/-0.384) for {'C': 10.0, 'kernel': 'linear'}
0.638 (+/-0.318) for {'C': 100.0, 'kernel': 'linear'}
0.637 (+/-0.319) for {'C': 1000.0, 'kernel': 'linear'}
0.637 (+/-0.319) for {'C': 10000.0, 'kernel': 'linear'}
0.637 (+/-0.319) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.315) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.587 (+/-0.235) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.666 (+/-0.315) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.612 (+/-0.242) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.241) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.315) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.611 (+/-0.243) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.240) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.611 (+/-0.243) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.637 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.612 (+/-0.241) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.660 (+/-0.314) for {'C': 10.0, 'kernel': 'linear'}
0.637 (+/-0.237) for {'C': 100.0, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.637 (+/-0.237) for {'C': 10000.0, 'kernel': 'linear'}
0.637 (+/-0.237) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6663461538461538
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.656 (+/-0.392) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.664 (+/-0.388) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.632 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.662 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.632 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.660 (+/-0.389) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.638 (+/-0.318) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.654 (+/-0.392) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.632 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.689 (+/-0.436) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.689 (+/-0.436) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      1.00       623

avg / total       0.98      0.99      0.99       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.241) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.240) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.614 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.637 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.612 (+/-0.241) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.616 (+/-0.238) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6371794871794872
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.664 (+/-0.388) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.654 (+/-0.392) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.383) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.647 (+/-0.401) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.698 (+/-0.383) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.654 (+/-0.393) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.632 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.698 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.653 (+/-0.394) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.662 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.632 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.698 (+/-0.383) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.653 (+/-0.394) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.638 (+/-0.318) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.654 (+/-0.392) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.632 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.698 (+/-0.383) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.698 (+/-0.383) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.706 (+/-0.418) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.670 (+/-0.370) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.661 (+/-0.384) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.315) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.587 (+/-0.235) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.666 (+/-0.315) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.612 (+/-0.242) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.241) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.315) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.611 (+/-0.243) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.240) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.611 (+/-0.243) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.637 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.612 (+/-0.241) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.666 (+/-0.315) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.666 (+/-0.315) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.665 (+/-0.314) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.663 (+/-0.315) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.660 (+/-0.314) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 0.9999999999999997, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6663461538461538
循环迭代之前，delta is: [3.33066907e-16]
SVC模型clf_op_iter的参数是： {'kernel': 'linear', 'decision_function_shape': 'ovr', 'class_weight': {-1: 30}, 'tol': 0.001, 'gamma': 'auto', 'random_state': 0, 'cache_size': 200, 'verbose': False, 'degree': 3, 'C': 0.9999999999999997, 'probability': False, 'shrinking': True, 'coef0': 0.0, 'max_iter': -1}

训练模型SVC对训练样本的预测准确率： 0.9016497461928934

测试集中，预测为舞弊样本的有： (array([1248, 1252, 1255, 1256], dtype=int64),) 
测试集中，实际为舞弊样本的有： (array([1246, 1247, 1248, 1249, 1250, 1251, 1252, 1253, 1254, 1255, 1256],
      dtype=int64),) 
预测的舞弊样本数目： 4

训练模型SVC对测试样本的预测准确率： 0.866751269035533
以上是第23次特征筛选。
第23次特征筛选，AUC值是： 0.6818181818181819
X_train_iter_svc.shape is: (1257, 29)
X_test_iter_svc.shape is: (1257, 29)
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.631 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.614 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6166666666666667
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.643 (+/-0.306) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.637 (+/-0.241) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6663461538461538
RBF的粗grid search最佳超参： {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0} RBF的粗grid search最高分值： 0.6166666666666667
linear的粗grid search最佳超参： {'C': 1.0, 'kernel': 'linear'} linear的粗grid search最高分值： 0.6663461538461538
粗grid search得到的parameter是：
 [{'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05]), 'kernel': ['rbf'], 'gamma': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.632 (+/-0.327) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.672 (+/-0.392) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.631 (+/-0.327) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.706 (+/-0.424) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.631 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.706 (+/-0.424) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.639 (+/-0.326) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.631 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.706 (+/-0.424) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.639 (+/-0.306) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.631 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'linear'}
0.631 (+/-0.319) for {'C': 100.0, 'kernel': 'linear'}
0.643 (+/-0.306) for {'C': 1000.0, 'kernel': 'linear'}
0.643 (+/-0.306) for {'C': 10000.0, 'kernel': 'linear'}
0.643 (+/-0.306) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      1.00       623

avg / total       0.98      0.99      0.99       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.240) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.240) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.236) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.242) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.236) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.613 (+/-0.241) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.236) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.613 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.638 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.616 (+/-0.238) for {'C': 10.0, 'kernel': 'linear'}
0.613 (+/-0.240) for {'C': 100.0, 'kernel': 'linear'}
0.637 (+/-0.241) for {'C': 1000.0, 'kernel': 'linear'}
0.638 (+/-0.240) for {'C': 10000.0, 'kernel': 'linear'}
0.638 (+/-0.240) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.17      0.17      0.17         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6399038461538461
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.664 (+/-0.388) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.629 (+/-0.328) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.383) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.647 (+/-0.401) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.631 (+/-0.327) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.698 (+/-0.383) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.654 (+/-0.393) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.631 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.698 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.653 (+/-0.394) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.639 (+/-0.326) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.631 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.698 (+/-0.383) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.653 (+/-0.394) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.639 (+/-0.306) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.631 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.660 (+/-0.385) for {'C': 10.0, 'kernel': 'linear'}
0.631 (+/-0.319) for {'C': 100.0, 'kernel': 'linear'}
0.643 (+/-0.306) for {'C': 1000.0, 'kernel': 'linear'}
0.643 (+/-0.306) for {'C': 10000.0, 'kernel': 'linear'}
0.643 (+/-0.306) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.240) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.315) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.587 (+/-0.236) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.666 (+/-0.315) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.612 (+/-0.242) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.242) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.315) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.611 (+/-0.242) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.613 (+/-0.241) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.611 (+/-0.242) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.613 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.638 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.660 (+/-0.314) for {'C': 10.0, 'kernel': 'linear'}
0.613 (+/-0.240) for {'C': 100.0, 'kernel': 'linear'}
0.637 (+/-0.241) for {'C': 1000.0, 'kernel': 'linear'}
0.638 (+/-0.240) for {'C': 10000.0, 'kernel': 'linear'}
0.638 (+/-0.240) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6663461538461538
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.632 (+/-0.327) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.672 (+/-0.392) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.631 (+/-0.327) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.706 (+/-0.424) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.631 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.706 (+/-0.424) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.639 (+/-0.326) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.631 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.706 (+/-0.424) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.639 (+/-0.306) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.631 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.689 (+/-0.436) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      1.00       623

avg / total       0.98      0.99      0.99       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.240) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.240) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.236) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.242) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.236) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.613 (+/-0.241) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.236) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.613 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.638 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.616 (+/-0.238) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.616 (+/-0.238) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.17      0.17      0.17         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6399038461538461
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.664 (+/-0.388) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.629 (+/-0.328) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.383) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.647 (+/-0.401) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.631 (+/-0.327) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.698 (+/-0.383) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.654 (+/-0.393) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.631 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.698 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.653 (+/-0.394) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.639 (+/-0.326) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.631 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.698 (+/-0.383) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.653 (+/-0.394) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.639 (+/-0.306) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.631 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.698 (+/-0.383) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.689 (+/-0.374) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.706 (+/-0.418) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.679 (+/-0.373) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.660 (+/-0.385) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.240) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.315) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.587 (+/-0.236) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.666 (+/-0.315) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.612 (+/-0.242) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.242) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.315) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.611 (+/-0.242) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.613 (+/-0.241) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.611 (+/-0.242) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.613 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.638 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.666 (+/-0.315) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.666 (+/-0.315) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.665 (+/-0.314) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.664 (+/-0.315) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.660 (+/-0.314) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 0.9999999999999997, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6663461538461538
循环迭代之前，delta is: [3.33066907e-16]
SVC模型clf_op_iter的参数是： {'kernel': 'linear', 'decision_function_shape': 'ovr', 'class_weight': {-1: 30}, 'tol': 0.001, 'gamma': 'auto', 'random_state': 0, 'cache_size': 200, 'verbose': False, 'degree': 3, 'C': 0.9999999999999997, 'probability': False, 'shrinking': True, 'coef0': 0.0, 'max_iter': -1}

训练模型SVC对训练样本的预测准确率： 0.9016497461928934

测试集中，预测为舞弊样本的有： (array([1248, 1252, 1255, 1256], dtype=int64),) 
测试集中，实际为舞弊样本的有： (array([1246, 1247, 1248, 1249, 1250, 1251, 1252, 1253, 1254, 1255, 1256],
      dtype=int64),) 
预测的舞弊样本数目： 4

训练模型SVC对测试样本的预测准确率： 0.866751269035533
以上是第24次特征筛选。
第24次特征筛选，AUC值是： 0.6818181818181819
X_train_iter_svc.shape is: (1257, 28)
X_test_iter_svc.shape is: (1257, 28)
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.632 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.614 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6166666666666667
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.631 (+/-0.319) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.613 (+/-0.240) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6663461538461538
RBF的粗grid search最佳超参： {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0} RBF的粗grid search最高分值： 0.6166666666666667
linear的粗grid search最佳超参： {'C': 1.0, 'kernel': 'linear'} linear的粗grid search最高分值： 0.6663461538461538
粗grid search得到的parameter是：
 [{'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05]), 'kernel': ['rbf'], 'gamma': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.632 (+/-0.327) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.672 (+/-0.392) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.632 (+/-0.327) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.632 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.639 (+/-0.326) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.632 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.632 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'linear'}
0.643 (+/-0.306) for {'C': 100.0, 'kernel': 'linear'}
0.631 (+/-0.319) for {'C': 1000.0, 'kernel': 'linear'}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'linear'}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      1.00       623

avg / total       0.98      0.99      0.99       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.005) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.240) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.240) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.240) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.232) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.240) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.588 (+/-0.232) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.615 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.613 (+/-0.241) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.589 (+/-0.231) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.616 (+/-0.238) for {'C': 10.0, 'kernel': 'linear'}
0.638 (+/-0.239) for {'C': 100.0, 'kernel': 'linear'}
0.613 (+/-0.240) for {'C': 1000.0, 'kernel': 'linear'}
0.613 (+/-0.240) for {'C': 10000.0, 'kernel': 'linear'}
0.613 (+/-0.240) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 100.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6381410256410257
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.664 (+/-0.388) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.631 (+/-0.327) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.383) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.647 (+/-0.401) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.632 (+/-0.327) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.698 (+/-0.383) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.653 (+/-0.394) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.632 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.698 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.652 (+/-0.395) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.639 (+/-0.326) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.632 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.698 (+/-0.383) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.652 (+/-0.395) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.632 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.655 (+/-0.392) for {'C': 10.0, 'kernel': 'linear'}
0.643 (+/-0.306) for {'C': 100.0, 'kernel': 'linear'}
0.631 (+/-0.319) for {'C': 1000.0, 'kernel': 'linear'}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'linear'}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.005) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.240) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.315) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.587 (+/-0.236) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.666 (+/-0.315) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.611 (+/-0.242) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.232) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.315) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.611 (+/-0.242) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.588 (+/-0.232) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.611 (+/-0.242) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.613 (+/-0.241) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.589 (+/-0.231) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.635 (+/-0.324) for {'C': 10.0, 'kernel': 'linear'}
0.638 (+/-0.239) for {'C': 100.0, 'kernel': 'linear'}
0.613 (+/-0.240) for {'C': 1000.0, 'kernel': 'linear'}
0.613 (+/-0.240) for {'C': 10000.0, 'kernel': 'linear'}
0.613 (+/-0.240) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6663461538461538
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.632 (+/-0.327) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.672 (+/-0.392) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.632 (+/-0.327) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.632 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.639 (+/-0.326) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.632 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.632 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.689 (+/-0.436) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      1.00       623

avg / total       0.98      0.99      0.99       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.005) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.240) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.240) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.240) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.232) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.240) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.588 (+/-0.232) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.615 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.613 (+/-0.241) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.589 (+/-0.231) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.616 (+/-0.238) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.616 (+/-0.238) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      1.00       623

avg / total       0.98      0.99      0.99       629

本轮grid search结果，得到最好的参数选择是： {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6166666666666667
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.664 (+/-0.388) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.631 (+/-0.327) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.383) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.647 (+/-0.401) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.632 (+/-0.327) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.698 (+/-0.383) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.653 (+/-0.394) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.632 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.698 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.652 (+/-0.395) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.639 (+/-0.326) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.632 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.698 (+/-0.383) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.652 (+/-0.395) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.632 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.698 (+/-0.383) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.698 (+/-0.383) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.706 (+/-0.418) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.679 (+/-0.373) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.655 (+/-0.392) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.005) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.240) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.315) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.587 (+/-0.236) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.666 (+/-0.315) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.611 (+/-0.242) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.232) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.315) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.611 (+/-0.242) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.588 (+/-0.232) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.611 (+/-0.242) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.613 (+/-0.241) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.589 (+/-0.231) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.666 (+/-0.315) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.666 (+/-0.315) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.665 (+/-0.313) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.664 (+/-0.314) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.635 (+/-0.324) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 0.9999999999999997, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6663461538461538
循环迭代之前，delta is: [3.33066907e-16]
SVC模型clf_op_iter的参数是： {'kernel': 'linear', 'decision_function_shape': 'ovr', 'class_weight': {-1: 30}, 'tol': 0.001, 'gamma': 'auto', 'random_state': 0, 'cache_size': 200, 'verbose': False, 'degree': 3, 'C': 0.9999999999999997, 'probability': False, 'shrinking': True, 'coef0': 0.0, 'max_iter': -1}

训练模型SVC对训练样本的预测准确率： 0.9022842639593909

测试集中，预测为舞弊样本的有： (array([1248, 1252, 1255, 1256], dtype=int64),) 
测试集中，实际为舞弊样本的有： (array([1246, 1247, 1248, 1249, 1250, 1251, 1252, 1253, 1254, 1255, 1256],
      dtype=int64),) 
预测的舞弊样本数目： 4

训练模型SVC对测试样本的预测准确率： 0.866751269035533
以上是第25次特征筛选。
第25次特征筛选，AUC值是： 0.6818181818181819
X_train_iter_svc.shape is: (1257, 27)
X_test_iter_svc.shape is: (1257, 27)
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.632 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.747 (+/-0.449) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.614 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.641 (+/-0.236) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6413461538461539
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.626 (+/-0.318) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.612 (+/-0.242) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6663461538461538
RBF的粗grid search最佳超参： {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001} RBF的粗grid search最高分值： 0.6413461538461539
linear的粗grid search最佳超参： {'C': 1.0, 'kernel': 'linear'} linear的粗grid search最高分值： 0.6663461538461538
粗grid search得到的parameter是：
 [{'C': array([1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03, 1.e+04, 1.e+05,
       1.e+06, 1.e+07, 1.e+08]), 'kernel': ['rbf'], 'gamma': array([1.e-08, 1.e-07, 1.e-06, 1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01,
       1.e+00, 1.e+01, 1.e+02])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.632 (+/-0.327) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.672 (+/-0.392) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.632 (+/-0.327) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.449) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.632 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.449) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.639 (+/-0.326) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.632 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.747 (+/-0.502) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.710 (+/-0.420) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.632 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.747 (+/-0.502) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.798 (+/-0.437) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.664 (+/-0.388) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.631 (+/-0.319) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.626 (+/-0.318) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.639 (+/-0.326) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.632 (+/-0.327) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.649 (+/-0.778) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.747 (+/-0.502) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.695 (+/-0.353) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.632 (+/-0.311) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.624 (+/-0.319) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.626 (+/-0.318) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.639 (+/-0.326) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.632 (+/-0.327) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.699 (+/-0.797) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.797 (+/-0.492) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.675 (+/-0.375) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.626 (+/-0.318) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.624 (+/-0.319) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.626 (+/-0.318) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.639 (+/-0.326) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.632 (+/-0.327) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.722 (+/-0.417) for {'C': 10.0, 'kernel': 'linear'}
0.643 (+/-0.306) for {'C': 100.0, 'kernel': 'linear'}
0.626 (+/-0.318) for {'C': 1000.0, 'kernel': 'linear'}
0.626 (+/-0.318) for {'C': 10000.0, 'kernel': 'linear'}
0.626 (+/-0.318) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.005) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.240) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.240) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.641 (+/-0.236) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.240) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.232) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.641 (+/-0.236) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.240) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.588 (+/-0.232) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.241) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.617 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.236) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.613 (+/-0.241) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.588 (+/-0.232) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.241) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.617 (+/-0.238) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.683 (+/-0.296) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.615 (+/-0.239) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.613 (+/-0.241) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.612 (+/-0.242) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.588 (+/-0.232) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.241) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.617 (+/-0.238) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.617 (+/-0.238) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.681 (+/-0.293) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.636 (+/-0.240) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.613 (+/-0.241) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.612 (+/-0.242) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.588 (+/-0.232) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.241) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.642 (+/-0.236) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.642 (+/-0.236) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.664 (+/-0.312) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.612 (+/-0.243) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.613 (+/-0.241) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.612 (+/-0.242) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.588 (+/-0.232) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.241) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.641 (+/-0.236) for {'C': 10.0, 'kernel': 'linear'}
0.638 (+/-0.239) for {'C': 100.0, 'kernel': 'linear'}
0.612 (+/-0.242) for {'C': 1000.0, 'kernel': 'linear'}
0.613 (+/-0.241) for {'C': 10000.0, 'kernel': 'linear'}
0.613 (+/-0.241) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6830128205128205
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.689 (+/-0.374) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.629 (+/-0.328) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.383) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.647 (+/-0.401) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.632 (+/-0.327) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.698 (+/-0.383) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.653 (+/-0.394) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.632 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.698 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.653 (+/-0.394) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.639 (+/-0.326) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.632 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.747 (+/-0.502) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.797 (+/-0.492) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.698 (+/-0.383) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.653 (+/-0.394) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.632 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.024) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.797 (+/-0.492) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.678 (+/-0.357) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.655 (+/-0.392) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.631 (+/-0.319) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.626 (+/-0.318) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.639 (+/-0.326) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.632 (+/-0.327) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.699 (+/-0.797) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.660 (+/-0.367) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.648 (+/-0.297) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.632 (+/-0.311) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.624 (+/-0.319) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.626 (+/-0.318) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.639 (+/-0.326) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.632 (+/-0.327) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.648 (+/-0.778) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.684 (+/-0.426) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.627 (+/-0.313) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.626 (+/-0.318) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.624 (+/-0.319) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.626 (+/-0.318) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.639 (+/-0.326) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.632 (+/-0.327) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.655 (+/-0.392) for {'C': 10.0, 'kernel': 'linear'}
0.643 (+/-0.306) for {'C': 100.0, 'kernel': 'linear'}
0.626 (+/-0.318) for {'C': 1000.0, 'kernel': 'linear'}
0.626 (+/-0.318) for {'C': 10000.0, 'kernel': 'linear'}
0.626 (+/-0.318) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.005) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.641 (+/-0.235) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.240) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.315) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.587 (+/-0.236) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.666 (+/-0.315) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.611 (+/-0.242) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.232) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.315) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.611 (+/-0.242) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.588 (+/-0.232) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.241) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.617 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.642 (+/-0.236) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.611 (+/-0.243) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.613 (+/-0.241) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.588 (+/-0.232) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.241) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.523 (+/-0.138) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.642 (+/-0.236) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.680 (+/-0.291) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.636 (+/-0.323) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.613 (+/-0.241) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.612 (+/-0.242) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.588 (+/-0.232) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.241) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.642 (+/-0.236) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.665 (+/-0.312) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.677 (+/-0.294) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.636 (+/-0.240) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.613 (+/-0.241) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.612 (+/-0.242) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.588 (+/-0.232) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.241) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.591 (+/-0.324) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.665 (+/-0.314) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.658 (+/-0.314) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.612 (+/-0.243) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.613 (+/-0.241) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.612 (+/-0.242) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.588 (+/-0.232) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.241) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.635 (+/-0.325) for {'C': 10.0, 'kernel': 'linear'}
0.638 (+/-0.239) for {'C': 100.0, 'kernel': 'linear'}
0.612 (+/-0.242) for {'C': 1000.0, 'kernel': 'linear'}
0.613 (+/-0.241) for {'C': 10000.0, 'kernel': 'linear'}
0.613 (+/-0.241) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6799669181301015
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.747 (+/-0.502) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.747 (+/-0.502) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.747 (+/-0.502) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.747 (+/-0.502) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.747 (+/-0.502) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.747 (+/-0.502) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.747 (+/-0.502) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.747 (+/-0.502) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.722 (+/-0.473) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.697 (+/-0.437) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.747 (+/-0.502) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.747 (+/-0.502) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.747 (+/-0.502) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.747 (+/-0.502) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.747 (+/-0.502) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.747 (+/-0.502) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.747 (+/-0.502) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.747 (+/-0.502) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.772 (+/-0.473) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.697 (+/-0.437) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.723 (+/-0.423) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.747 (+/-0.502) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.747 (+/-0.502) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.797 (+/-0.492) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.747 (+/-0.502) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.747 (+/-0.502) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.747 (+/-0.502) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.747 (+/-0.502) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.747 (+/-0.449) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.748 (+/-0.449) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.739 (+/-0.452) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.723 (+/-0.423) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.747 (+/-0.502) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.797 (+/-0.492) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.797 (+/-0.492) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.747 (+/-0.502) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.797 (+/-0.492) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.747 (+/-0.502) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.722 (+/-0.473) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.747 (+/-0.449) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.723 (+/-0.417) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.739 (+/-0.452) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.722 (+/-0.417) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.747 (+/-0.502) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.797 (+/-0.492) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.797 (+/-0.492) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.797 (+/-0.492) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.797 (+/-0.492) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.798 (+/-0.492) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.747 (+/-0.449) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.731 (+/-0.422) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.689 (+/-0.436) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.739 (+/-0.452) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.714 (+/-0.418) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.747 (+/-0.502) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.797 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.797 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.797 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.748 (+/-0.449) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.798 (+/-0.437) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.748 (+/-0.449) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.714 (+/-0.418) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.731 (+/-0.422) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.698 (+/-0.383) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.664 (+/-0.388) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.747 (+/-0.502) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.798 (+/-0.492) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.798 (+/-0.492) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.797 (+/-0.492) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.748 (+/-0.449) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.798 (+/-0.437) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.714 (+/-0.418) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.702 (+/-0.421) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.718 (+/-0.416) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.664 (+/-0.317) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.653 (+/-0.394) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.797 (+/-0.492) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.798 (+/-0.492) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.773 (+/-0.474) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.772 (+/-0.473) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.781 (+/-0.417) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.683 (+/-0.353) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.723 (+/-0.417) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.731 (+/-0.422) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.710 (+/-0.420) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.635 (+/-0.308) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.620 (+/-0.321) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.797 (+/-0.492) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.798 (+/-0.492) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.810 (+/-0.465) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.710 (+/-0.420) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.681 (+/-0.354) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.687 (+/-0.351) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.672 (+/-0.392) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.635 (+/-0.326) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.683 (+/-0.378) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.629 (+/-0.311) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.619 (+/-0.321) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.747 (+/-0.502) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.798 (+/-0.492) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.758 (+/-0.484) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.707 (+/-0.423) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.682 (+/-0.355) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.693 (+/-0.350) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.681 (+/-0.401) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.630 (+/-0.320) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.668 (+/-0.379) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.623 (+/-0.319) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.619 (+/-0.321) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.747 (+/-0.502) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.798 (+/-0.492) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.755 (+/-0.488) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.671 (+/-0.378) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.639 (+/-0.308) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.695 (+/-0.353) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.656 (+/-0.347) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.627 (+/-0.318) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.629 (+/-0.311) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.622 (+/-0.319) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.632 (+/-0.311) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.722 (+/-0.417) for {'C': 10.0, 'kernel': 'linear'}
0.643 (+/-0.306) for {'C': 100.0, 'kernel': 'linear'}
0.626 (+/-0.318) for {'C': 1000.0, 'kernel': 'linear'}
0.626 (+/-0.318) for {'C': 10000.0, 'kernel': 'linear'}
0.626 (+/-0.318) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.617 (+/-0.238) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.617 (+/-0.238) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.617 (+/-0.238) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.617 (+/-0.238) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.617 (+/-0.238) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.617 (+/-0.238) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.617 (+/-0.238) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.617 (+/-0.238) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.617 (+/-0.238) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.616 (+/-0.237) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.617 (+/-0.238) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.617 (+/-0.238) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.617 (+/-0.238) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.617 (+/-0.238) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.617 (+/-0.238) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.617 (+/-0.238) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.617 (+/-0.238) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.617 (+/-0.238) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.642 (+/-0.236) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.616 (+/-0.237) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.666 (+/-0.315) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.617 (+/-0.238) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.617 (+/-0.238) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.642 (+/-0.236) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.617 (+/-0.238) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.617 (+/-0.238) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.617 (+/-0.238) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.617 (+/-0.238) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.641 (+/-0.236) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.666 (+/-0.316) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.666 (+/-0.316) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.666 (+/-0.315) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.617 (+/-0.238) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.642 (+/-0.236) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.642 (+/-0.236) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.617 (+/-0.238) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.642 (+/-0.236) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.617 (+/-0.238) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.617 (+/-0.238) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.641 (+/-0.236) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.666 (+/-0.315) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.666 (+/-0.316) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.641 (+/-0.236) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.617 (+/-0.238) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.642 (+/-0.236) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.642 (+/-0.236) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.642 (+/-0.236) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.642 (+/-0.236) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.667 (+/-0.316) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.641 (+/-0.236) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.666 (+/-0.315) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.616 (+/-0.238) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.666 (+/-0.316) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.641 (+/-0.236) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.617 (+/-0.238) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.642 (+/-0.236) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.642 (+/-0.236) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.642 (+/-0.236) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.666 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.683 (+/-0.296) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.666 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.666 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.666 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.666 (+/-0.315) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.615 (+/-0.239) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.617 (+/-0.238) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.667 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.667 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.642 (+/-0.236) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.666 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.683 (+/-0.296) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.666 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.666 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.666 (+/-0.314) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.664 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.612 (+/-0.242) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.642 (+/-0.236) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.667 (+/-0.316) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.667 (+/-0.316) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.642 (+/-0.236) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.683 (+/-0.296) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.681 (+/-0.292) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.666 (+/-0.316) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.666 (+/-0.316) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.665 (+/-0.314) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.661 (+/-0.317) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.611 (+/-0.242) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.642 (+/-0.236) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.667 (+/-0.316) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.683 (+/-0.296) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.641 (+/-0.235) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.681 (+/-0.294) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.681 (+/-0.293) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.641 (+/-0.326) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.640 (+/-0.324) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.663 (+/-0.314) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.660 (+/-0.315) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.611 (+/-0.242) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.617 (+/-0.238) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.667 (+/-0.316) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.658 (+/-0.311) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.641 (+/-0.235) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.680 (+/-0.291) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.681 (+/-0.294) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.640 (+/-0.326) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.639 (+/-0.324) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.661 (+/-0.315) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.636 (+/-0.325) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.611 (+/-0.242) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.617 (+/-0.238) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.667 (+/-0.316) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.657 (+/-0.310) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.640 (+/-0.234) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.655 (+/-0.305) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.681 (+/-0.293) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.640 (+/-0.327) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.638 (+/-0.325) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.660 (+/-0.314) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.636 (+/-0.324) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.636 (+/-0.240) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.641 (+/-0.236) for {'C': 10.0, 'kernel': 'linear'}
0.638 (+/-0.239) for {'C': 100.0, 'kernel': 'linear'}
0.612 (+/-0.242) for {'C': 1000.0, 'kernel': 'linear'}
0.613 (+/-0.241) for {'C': 10000.0, 'kernel': 'linear'}
0.613 (+/-0.241) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6830128205128205
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.747 (+/-0.502) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.747 (+/-0.502) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.797 (+/-0.492) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.747 (+/-0.502) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.797 (+/-0.492) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.797 (+/-0.492) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.797 (+/-0.492) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.797 (+/-0.492) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.748 (+/-0.449) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.739 (+/-0.398) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.698 (+/-0.383) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.747 (+/-0.502) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.797 (+/-0.492) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.797 (+/-0.492) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.797 (+/-0.492) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.797 (+/-0.492) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.797 (+/-0.492) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.798 (+/-0.492) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.798 (+/-0.437) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.723 (+/-0.417) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.698 (+/-0.383) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.706 (+/-0.418) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.797 (+/-0.492) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.797 (+/-0.492) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.797 (+/-0.492) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.797 (+/-0.492) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.798 (+/-0.492) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.798 (+/-0.492) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.748 (+/-0.449) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.739 (+/-0.391) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.748 (+/-0.395) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.723 (+/-0.423) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.704 (+/-0.419) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.797 (+/-0.492) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.798 (+/-0.492) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.798 (+/-0.492) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.797 (+/-0.492) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.748 (+/-0.449) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.798 (+/-0.437) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.748 (+/-0.395) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.739 (+/-0.398) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.764 (+/-0.421) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.706 (+/-0.418) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.665 (+/-0.381) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.797 (+/-0.492) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.798 (+/-0.492) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.848 (+/-0.460) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.798 (+/-0.492) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.773 (+/-0.416) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.739 (+/-0.391) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.748 (+/-0.395) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.739 (+/-0.391) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.702 (+/-0.420) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.654 (+/-0.378) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.659 (+/-0.386) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.797 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.798 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.823 (+/-0.451) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.714 (+/-0.418) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.699 (+/-0.358) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.678 (+/-0.357) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.689 (+/-0.374) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.681 (+/-0.372) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.667 (+/-0.379) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.625 (+/-0.314) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.655 (+/-0.392) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.772 (+/-0.473) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.798 (+/-0.492) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.735 (+/-0.455) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.669 (+/-0.373) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.649 (+/-0.364) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.687 (+/-0.358) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.643 (+/-0.306) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.621 (+/-0.299) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.659 (+/-0.385) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.626 (+/-0.313) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.651 (+/-0.395) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.764 (+/-0.478) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.798 (+/-0.492) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.733 (+/-0.458) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.686 (+/-0.433) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.663 (+/-0.367) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.654 (+/-0.292) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.635 (+/-0.321) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.659 (+/-0.386) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.659 (+/-0.386) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.628 (+/-0.312) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.620 (+/-0.321) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.714 (+/-0.418) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.798 (+/-0.492) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.720 (+/-0.464) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.671 (+/-0.436) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.668 (+/-0.363) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.647 (+/-0.288) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.632 (+/-0.324) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.620 (+/-0.314) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.659 (+/-0.385) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.629 (+/-0.311) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.619 (+/-0.321) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.672 (+/-0.371) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.773 (+/-0.474) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.711 (+/-0.472) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.671 (+/-0.434) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.645 (+/-0.290) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.639 (+/-0.289) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.656 (+/-0.390) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.620 (+/-0.314) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.661 (+/-0.384) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.623 (+/-0.319) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.619 (+/-0.321) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.660 (+/-0.367) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.748 (+/-0.449) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.669 (+/-0.395) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.679 (+/-0.430) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.648 (+/-0.298) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.648 (+/-0.297) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.630 (+/-0.327) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.620 (+/-0.314) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.628 (+/-0.312) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.622 (+/-0.319) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.632 (+/-0.311) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.655 (+/-0.392) for {'C': 10.0, 'kernel': 'linear'}
0.643 (+/-0.306) for {'C': 100.0, 'kernel': 'linear'}
0.626 (+/-0.318) for {'C': 1000.0, 'kernel': 'linear'}
0.626 (+/-0.318) for {'C': 10000.0, 'kernel': 'linear'}
0.626 (+/-0.318) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.617 (+/-0.238) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.617 (+/-0.238) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.642 (+/-0.236) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.617 (+/-0.238) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.642 (+/-0.236) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.642 (+/-0.236) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.642 (+/-0.236) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.642 (+/-0.236) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.666 (+/-0.316) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.682 (+/-0.295) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.666 (+/-0.315) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.617 (+/-0.238) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.642 (+/-0.236) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.642 (+/-0.236) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.642 (+/-0.236) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.642 (+/-0.236) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.642 (+/-0.236) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.667 (+/-0.316) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.683 (+/-0.296) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.666 (+/-0.315) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.666 (+/-0.315) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.665 (+/-0.313) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.642 (+/-0.236) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.642 (+/-0.236) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.642 (+/-0.236) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.642 (+/-0.236) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.667 (+/-0.316) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.667 (+/-0.316) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.666 (+/-0.316) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.682 (+/-0.294) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.682 (+/-0.295) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.666 (+/-0.315) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.664 (+/-0.314) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.642 (+/-0.236) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.667 (+/-0.316) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.667 (+/-0.316) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.642 (+/-0.236) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.666 (+/-0.316) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.683 (+/-0.296) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.683 (+/-0.295) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.682 (+/-0.295) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.682 (+/-0.295) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.665 (+/-0.315) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.661 (+/-0.314) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.642 (+/-0.236) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.667 (+/-0.316) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.683 (+/-0.296) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.667 (+/-0.316) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.683 (+/-0.296) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.682 (+/-0.295) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.682 (+/-0.295) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.682 (+/-0.296) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.664 (+/-0.313) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.660 (+/-0.314) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.659 (+/-0.312) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.642 (+/-0.236) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.667 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.683 (+/-0.296) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.666 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.681 (+/-0.295) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.680 (+/-0.291) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.665 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.664 (+/-0.315) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.661 (+/-0.314) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.659 (+/-0.313) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.636 (+/-0.323) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.642 (+/-0.236) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.667 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.666 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.665 (+/-0.313) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.679 (+/-0.290) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.680 (+/-0.292) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.663 (+/-0.317) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.661 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.659 (+/-0.314) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.659 (+/-0.313) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.611 (+/-0.241) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.641 (+/-0.236) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.667 (+/-0.316) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.666 (+/-0.315) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.664 (+/-0.314) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.678 (+/-0.288) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.680 (+/-0.292) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.659 (+/-0.317) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.659 (+/-0.315) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.659 (+/-0.313) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.660 (+/-0.315) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.611 (+/-0.242) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.641 (+/-0.235) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.667 (+/-0.316) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.657 (+/-0.310) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.663 (+/-0.314) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.678 (+/-0.288) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.679 (+/-0.290) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.656 (+/-0.316) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.656 (+/-0.317) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.659 (+/-0.313) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.660 (+/-0.315) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.611 (+/-0.242) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.665 (+/-0.313) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.667 (+/-0.316) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.656 (+/-0.310) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.663 (+/-0.315) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.678 (+/-0.288) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.678 (+/-0.290) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.654 (+/-0.316) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.655 (+/-0.317) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.660 (+/-0.313) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.636 (+/-0.325) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.611 (+/-0.242) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.665 (+/-0.312) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.666 (+/-0.316) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.655 (+/-0.309) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.664 (+/-0.315) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.676 (+/-0.288) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.677 (+/-0.294) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.653 (+/-0.315) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.656 (+/-0.317) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.660 (+/-0.314) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.636 (+/-0.324) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.636 (+/-0.240) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.635 (+/-0.325) for {'C': 10.0, 'kernel': 'linear'}
0.638 (+/-0.239) for {'C': 100.0, 'kernel': 'linear'}
0.612 (+/-0.242) for {'C': 1000.0, 'kernel': 'linear'}
0.613 (+/-0.241) for {'C': 10000.0, 'kernel': 'linear'}
0.613 (+/-0.241) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6833333333333332
循环迭代之前，delta is: [3.69042656e+05 7.48811357e-07]
执行tunemodel()函数前，使用的grid search parameter是：
 [{'C': array([ 398107.1705535 ,  436515.83224017,  478630.09232264,
        524807.46024977,  575439.93733716,  630957.34448019,
        691830.97091894,  758577.57502918,  831763.77110267,
        912010.83935591, 1000000.        ]), 'kernel': ['rbf'], 'gamma': array([1.58489319e-07, 1.73780083e-07, 1.90546072e-07, 2.08929613e-07,
       2.29086765e-07, 2.51188643e-07, 2.75422870e-07, 3.01995172e-07,
       3.31131121e-07, 3.63078055e-07, 3.98107171e-07])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.797 (+/-0.492) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.797 (+/-0.492) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.747 (+/-0.502) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.797 (+/-0.492) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.797 (+/-0.492) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.797 (+/-0.492) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.747 (+/-0.502) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.747 (+/-0.502) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.747 (+/-0.502) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.747 (+/-0.502) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.797 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.797 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.747 (+/-0.502) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.797 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.797 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.797 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.747 (+/-0.502) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.747 (+/-0.502) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.747 (+/-0.502) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.797 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.797 (+/-0.492) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.797 (+/-0.492) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.747 (+/-0.502) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.797 (+/-0.492) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.797 (+/-0.492) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.797 (+/-0.492) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.747 (+/-0.502) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.747 (+/-0.502) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.747 (+/-0.502) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.797 (+/-0.492) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.797 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.797 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.747 (+/-0.502) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.797 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.797 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.797 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.747 (+/-0.502) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.747 (+/-0.502) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.747 (+/-0.502) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.797 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.797 (+/-0.492) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.797 (+/-0.492) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.797 (+/-0.492) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.797 (+/-0.492) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.797 (+/-0.492) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.797 (+/-0.492) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.747 (+/-0.502) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.747 (+/-0.502) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.747 (+/-0.502) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.797 (+/-0.492) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.797 (+/-0.492) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.798 (+/-0.492) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.797 (+/-0.492) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.798 (+/-0.492) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.797 (+/-0.492) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.797 (+/-0.492) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.797 (+/-0.492) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.747 (+/-0.502) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.747 (+/-0.502) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.797 (+/-0.492) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.797 (+/-0.492) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.798 (+/-0.492) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.797 (+/-0.492) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.798 (+/-0.492) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.797 (+/-0.492) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.797 (+/-0.492) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.797 (+/-0.492) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.747 (+/-0.502) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.747 (+/-0.502) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.797 (+/-0.492) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.797 (+/-0.492) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.798 (+/-0.492) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.797 (+/-0.492) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.798 (+/-0.492) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.797 (+/-0.492) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.797 (+/-0.492) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.797 (+/-0.492) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.747 (+/-0.502) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.747 (+/-0.502) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.797 (+/-0.492) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.797 (+/-0.492) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.798 (+/-0.492) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.797 (+/-0.492) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.798 (+/-0.492) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.797 (+/-0.492) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.797 (+/-0.492) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.797 (+/-0.492) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.747 (+/-0.502) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.747 (+/-0.502) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.797 (+/-0.492) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.797 (+/-0.492) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.798 (+/-0.492) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.797 (+/-0.492) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.798 (+/-0.492) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.797 (+/-0.492) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.797 (+/-0.492) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.797 (+/-0.492) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.747 (+/-0.502) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.797 (+/-0.492) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.797 (+/-0.492) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.797 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.798 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.797 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.798 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.797 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.797 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.797 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.747 (+/-0.502) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.797 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.797 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.722 (+/-0.417) for {'C': 10.0, 'kernel': 'linear'}
0.643 (+/-0.306) for {'C': 100.0, 'kernel': 'linear'}
0.626 (+/-0.318) for {'C': 1000.0, 'kernel': 'linear'}
0.626 (+/-0.318) for {'C': 10000.0, 'kernel': 'linear'}
0.626 (+/-0.318) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.642 (+/-0.236) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.642 (+/-0.236) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.617 (+/-0.238) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.642 (+/-0.236) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.642 (+/-0.236) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.642 (+/-0.236) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.617 (+/-0.238) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.617 (+/-0.238) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.617 (+/-0.238) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.617 (+/-0.238) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.642 (+/-0.236) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.642 (+/-0.236) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.617 (+/-0.238) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.642 (+/-0.236) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.642 (+/-0.236) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.642 (+/-0.236) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.617 (+/-0.238) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.617 (+/-0.238) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.617 (+/-0.238) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.642 (+/-0.236) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.642 (+/-0.236) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.642 (+/-0.236) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.617 (+/-0.238) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.642 (+/-0.236) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.642 (+/-0.236) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.642 (+/-0.236) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.617 (+/-0.238) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.617 (+/-0.238) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.617 (+/-0.238) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.642 (+/-0.236) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.642 (+/-0.236) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.642 (+/-0.236) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.617 (+/-0.238) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.642 (+/-0.236) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.642 (+/-0.236) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.642 (+/-0.236) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.617 (+/-0.238) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.617 (+/-0.238) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.617 (+/-0.238) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.642 (+/-0.236) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.642 (+/-0.236) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.642 (+/-0.236) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.642 (+/-0.236) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.642 (+/-0.236) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.642 (+/-0.236) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.642 (+/-0.236) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.617 (+/-0.238) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.617 (+/-0.238) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.617 (+/-0.238) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.642 (+/-0.236) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.642 (+/-0.236) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.667 (+/-0.316) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.642 (+/-0.236) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.667 (+/-0.316) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.642 (+/-0.236) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.642 (+/-0.236) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.642 (+/-0.236) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.617 (+/-0.238) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.617 (+/-0.238) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.642 (+/-0.236) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.642 (+/-0.236) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.667 (+/-0.316) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.642 (+/-0.236) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.667 (+/-0.316) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.642 (+/-0.236) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.642 (+/-0.236) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.642 (+/-0.236) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.617 (+/-0.238) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.617 (+/-0.238) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.642 (+/-0.236) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.642 (+/-0.236) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.667 (+/-0.316) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.642 (+/-0.236) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.667 (+/-0.316) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.642 (+/-0.236) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.642 (+/-0.236) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.642 (+/-0.236) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.617 (+/-0.238) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.617 (+/-0.238) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.642 (+/-0.236) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.642 (+/-0.236) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.667 (+/-0.316) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.642 (+/-0.236) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.667 (+/-0.316) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.642 (+/-0.236) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.642 (+/-0.236) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.642 (+/-0.236) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.617 (+/-0.238) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.617 (+/-0.238) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.642 (+/-0.236) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.642 (+/-0.236) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.667 (+/-0.316) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.642 (+/-0.236) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.667 (+/-0.316) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.642 (+/-0.236) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.642 (+/-0.236) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.642 (+/-0.236) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.617 (+/-0.238) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.642 (+/-0.236) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.642 (+/-0.236) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.642 (+/-0.236) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.667 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.642 (+/-0.236) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.667 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.642 (+/-0.236) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.642 (+/-0.236) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.642 (+/-0.236) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.617 (+/-0.238) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.642 (+/-0.236) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.642 (+/-0.236) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.641 (+/-0.236) for {'C': 10.0, 'kernel': 'linear'}
0.638 (+/-0.239) for {'C': 100.0, 'kernel': 'linear'}
0.612 (+/-0.242) for {'C': 1000.0, 'kernel': 'linear'}
0.613 (+/-0.241) for {'C': 10000.0, 'kernel': 'linear'}
0.613 (+/-0.241) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6666666666666665
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.798 (+/-0.492) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.798 (+/-0.492) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.798 (+/-0.492) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.798 (+/-0.492) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.797 (+/-0.492) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.798 (+/-0.492) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.797 (+/-0.492) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.797 (+/-0.492) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.797 (+/-0.492) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.797 (+/-0.492) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.798 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.798 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.798 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.798 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.797 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.798 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.797 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.797 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.797 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.797 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.798 (+/-0.492) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.798 (+/-0.492) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.798 (+/-0.492) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.798 (+/-0.492) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.797 (+/-0.492) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.848 (+/-0.460) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.798 (+/-0.492) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.797 (+/-0.492) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.797 (+/-0.492) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.797 (+/-0.492) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.798 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.798 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.798 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.798 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.797 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.848 (+/-0.460) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.798 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.797 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.798 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.798 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.798 (+/-0.492) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.798 (+/-0.492) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.798 (+/-0.492) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.798 (+/-0.492) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.797 (+/-0.492) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.848 (+/-0.460) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.798 (+/-0.492) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.797 (+/-0.492) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.798 (+/-0.492) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.798 (+/-0.492) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.798 (+/-0.492) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.798 (+/-0.492) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.798 (+/-0.492) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.798 (+/-0.492) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.797 (+/-0.492) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.848 (+/-0.460) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.798 (+/-0.492) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.797 (+/-0.492) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.798 (+/-0.492) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.798 (+/-0.492) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.798 (+/-0.492) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.798 (+/-0.492) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.798 (+/-0.492) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.798 (+/-0.492) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.773 (+/-0.474) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.848 (+/-0.460) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.798 (+/-0.492) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.798 (+/-0.492) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.798 (+/-0.492) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.798 (+/-0.492) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.798 (+/-0.492) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.798 (+/-0.492) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.798 (+/-0.492) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.798 (+/-0.492) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.773 (+/-0.474) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.823 (+/-0.451) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.798 (+/-0.492) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.798 (+/-0.492) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.798 (+/-0.492) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.798 (+/-0.492) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.798 (+/-0.492) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.798 (+/-0.492) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.798 (+/-0.492) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.848 (+/-0.460) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.773 (+/-0.474) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.823 (+/-0.451) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.848 (+/-0.459) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.798 (+/-0.492) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.798 (+/-0.492) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.798 (+/-0.492) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.748 (+/-0.449) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.798 (+/-0.492) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.798 (+/-0.492) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.798 (+/-0.492) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.798 (+/-0.492) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.773 (+/-0.474) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.823 (+/-0.451) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.848 (+/-0.459) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.798 (+/-0.492) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.798 (+/-0.492) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.798 (+/-0.492) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.748 (+/-0.449) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.798 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.848 (+/-0.460) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.798 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.798 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.773 (+/-0.474) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.823 (+/-0.451) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.848 (+/-0.460) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.798 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.798 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.798 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.714 (+/-0.418) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.655 (+/-0.392) for {'C': 10.0, 'kernel': 'linear'}
0.643 (+/-0.306) for {'C': 100.0, 'kernel': 'linear'}
0.626 (+/-0.318) for {'C': 1000.0, 'kernel': 'linear'}
0.626 (+/-0.318) for {'C': 10000.0, 'kernel': 'linear'}
0.626 (+/-0.318) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.667 (+/-0.316) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.667 (+/-0.316) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.667 (+/-0.316) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.667 (+/-0.316) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.642 (+/-0.236) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.667 (+/-0.316) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.642 (+/-0.236) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.642 (+/-0.236) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.642 (+/-0.236) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.642 (+/-0.236) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.667 (+/-0.316) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.667 (+/-0.316) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.667 (+/-0.316) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.667 (+/-0.316) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.642 (+/-0.236) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.667 (+/-0.316) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.642 (+/-0.236) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.642 (+/-0.236) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.642 (+/-0.236) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.642 (+/-0.236) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.667 (+/-0.316) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.667 (+/-0.316) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.667 (+/-0.316) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.667 (+/-0.316) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.642 (+/-0.236) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.683 (+/-0.296) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.667 (+/-0.316) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.642 (+/-0.236) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.642 (+/-0.236) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.642 (+/-0.236) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.667 (+/-0.316) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.667 (+/-0.316) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.667 (+/-0.316) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.667 (+/-0.316) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.642 (+/-0.236) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.683 (+/-0.296) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.667 (+/-0.316) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.642 (+/-0.236) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.667 (+/-0.316) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.667 (+/-0.316) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.667 (+/-0.316) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.667 (+/-0.316) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.667 (+/-0.316) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.667 (+/-0.316) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.642 (+/-0.236) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.683 (+/-0.296) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.667 (+/-0.316) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.642 (+/-0.236) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.667 (+/-0.316) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.667 (+/-0.316) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.667 (+/-0.316) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.667 (+/-0.316) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.667 (+/-0.316) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.667 (+/-0.316) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.642 (+/-0.236) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.683 (+/-0.296) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.667 (+/-0.316) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.642 (+/-0.236) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.667 (+/-0.316) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.667 (+/-0.316) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.667 (+/-0.316) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.667 (+/-0.316) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.667 (+/-0.316) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.667 (+/-0.316) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.667 (+/-0.316) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.683 (+/-0.296) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.667 (+/-0.316) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.667 (+/-0.316) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.667 (+/-0.316) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.667 (+/-0.316) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.667 (+/-0.316) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.667 (+/-0.316) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.667 (+/-0.316) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.667 (+/-0.316) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.667 (+/-0.316) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.683 (+/-0.296) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.667 (+/-0.316) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.667 (+/-0.316) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.667 (+/-0.316) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.667 (+/-0.316) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.667 (+/-0.316) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.667 (+/-0.316) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.667 (+/-0.316) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.683 (+/-0.296) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.667 (+/-0.316) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.683 (+/-0.296) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.658 (+/-0.217) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.667 (+/-0.316) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.667 (+/-0.316) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.667 (+/-0.316) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.666 (+/-0.316) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.667 (+/-0.316) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.667 (+/-0.316) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.667 (+/-0.316) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.667 (+/-0.316) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.667 (+/-0.316) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.683 (+/-0.296) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.658 (+/-0.217) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.667 (+/-0.316) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.667 (+/-0.316) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.667 (+/-0.316) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.666 (+/-0.316) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.667 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.683 (+/-0.296) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.667 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.667 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.667 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.683 (+/-0.296) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.683 (+/-0.296) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.667 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.667 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.667 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.666 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.635 (+/-0.325) for {'C': 10.0, 'kernel': 'linear'}
0.638 (+/-0.239) for {'C': 100.0, 'kernel': 'linear'}
0.612 (+/-0.242) for {'C': 1000.0, 'kernel': 'linear'}
0.613 (+/-0.241) for {'C': 10000.0, 'kernel': 'linear'}
0.613 (+/-0.241) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6833333333333332
第0轮loop中，tunemodel函数得到的最优参数是： ([{'C': array([436515.83224017, 444631.26746911, 452897.57990362, 461317.57456038,
       469894.10860521, 478630.09232264, 487528.49010339, 496592.32145034,
       505824.66200311, 515228.64458176, 524807.46024977]), 'kernel': ['rbf'], 'gamma': array([2.29086765e-07, 2.33345806e-07, 2.37684029e-07, 2.42102905e-07,
       2.46603934e-07, 2.51188643e-07, 2.55858589e-07, 2.60615355e-07,
       2.65460556e-07, 2.70395836e-07, 2.75422870e-07])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}], {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}, 0.6833333333333332)
这是第0次迭代微调C和gamma。
第0次迭代，得到delta： [1.52327252e+05 6.35274710e-22]
执行tunemodel()函数前，使用的grid search parameter是：
 [{'C': array([436515.83224017, 444631.26746911, 452897.57990362, 461317.57456038,
       469894.10860521, 478630.09232264, 487528.49010339, 496592.32145034,
       505824.66200311, 515228.64458176, 524807.46024977]), 'kernel': ['rbf'], 'gamma': array([2.29086765e-07, 2.33345806e-07, 2.37684029e-07, 2.42102905e-07,
       2.46603934e-07, 2.51188643e-07, 2.55858589e-07, 2.60615355e-07,
       2.65460556e-07, 2.70395836e-07, 2.75422870e-07])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.797 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.797 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.797 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.797 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.797 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.797 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.797 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.747 (+/-0.502) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.797 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.797 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.797 (+/-0.492) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.747 (+/-0.502) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.797 (+/-0.492) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.797 (+/-0.492) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.797 (+/-0.492) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.797 (+/-0.492) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.797 (+/-0.492) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.747 (+/-0.502) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.797 (+/-0.492) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.797 (+/-0.492) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.797 (+/-0.492) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.797 (+/-0.492) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.797 (+/-0.492) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.797 (+/-0.492) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.797 (+/-0.492) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.797 (+/-0.492) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.797 (+/-0.492) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.747 (+/-0.502) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.797 (+/-0.492) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.797 (+/-0.492) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.797 (+/-0.492) for {'C': 461317.57456037874, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.797 (+/-0.492) for {'C': 461317.57456037874, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.797 (+/-0.492) for {'C': 461317.57456037874, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.797 (+/-0.492) for {'C': 461317.57456037874, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.797 (+/-0.492) for {'C': 461317.57456037874, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.797 (+/-0.492) for {'C': 461317.57456037874, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 461317.57456037874, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.797 (+/-0.492) for {'C': 461317.57456037874, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.747 (+/-0.502) for {'C': 461317.57456037874, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.797 (+/-0.492) for {'C': 461317.57456037874, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.797 (+/-0.492) for {'C': 461317.57456037874, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.797 (+/-0.492) for {'C': 469894.10860521457, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.747 (+/-0.502) for {'C': 469894.10860521457, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.797 (+/-0.492) for {'C': 469894.10860521457, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.797 (+/-0.492) for {'C': 469894.10860521457, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.797 (+/-0.492) for {'C': 469894.10860521457, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.797 (+/-0.492) for {'C': 469894.10860521457, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 469894.10860521457, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.797 (+/-0.492) for {'C': 469894.10860521457, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.747 (+/-0.502) for {'C': 469894.10860521457, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.797 (+/-0.492) for {'C': 469894.10860521457, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.797 (+/-0.492) for {'C': 469894.10860521457, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.797 (+/-0.492) for {'C': 478630.09232263727, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.747 (+/-0.502) for {'C': 478630.09232263727, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.797 (+/-0.492) for {'C': 478630.09232263727, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.797 (+/-0.492) for {'C': 478630.09232263727, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.797 (+/-0.492) for {'C': 478630.09232263727, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.797 (+/-0.492) for {'C': 478630.09232263727, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 478630.09232263727, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.797 (+/-0.492) for {'C': 478630.09232263727, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.747 (+/-0.502) for {'C': 478630.09232263727, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.797 (+/-0.492) for {'C': 478630.09232263727, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.797 (+/-0.492) for {'C': 478630.09232263727, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.797 (+/-0.492) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.747 (+/-0.502) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.797 (+/-0.492) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.797 (+/-0.492) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.797 (+/-0.492) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.797 (+/-0.492) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.797 (+/-0.492) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.747 (+/-0.502) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.797 (+/-0.492) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.797 (+/-0.492) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.797 (+/-0.492) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.747 (+/-0.502) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.797 (+/-0.492) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.797 (+/-0.492) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.797 (+/-0.492) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.797 (+/-0.492) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.797 (+/-0.492) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.747 (+/-0.502) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.797 (+/-0.492) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.797 (+/-0.492) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.797 (+/-0.492) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.747 (+/-0.502) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.797 (+/-0.492) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.797 (+/-0.492) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.797 (+/-0.492) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.797 (+/-0.492) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.797 (+/-0.492) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.747 (+/-0.502) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.797 (+/-0.492) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.797 (+/-0.492) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.797 (+/-0.492) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.747 (+/-0.502) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.797 (+/-0.492) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.797 (+/-0.492) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.797 (+/-0.492) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.797 (+/-0.492) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.797 (+/-0.492) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.747 (+/-0.502) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.797 (+/-0.492) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.797 (+/-0.492) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.797 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.747 (+/-0.502) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.797 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.797 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.797 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.797 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.797 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.747 (+/-0.502) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.797 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.797 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.722 (+/-0.417) for {'C': 10.0, 'kernel': 'linear'}
0.643 (+/-0.306) for {'C': 100.0, 'kernel': 'linear'}
0.626 (+/-0.318) for {'C': 1000.0, 'kernel': 'linear'}
0.626 (+/-0.318) for {'C': 10000.0, 'kernel': 'linear'}
0.626 (+/-0.318) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.642 (+/-0.236) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.642 (+/-0.236) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.642 (+/-0.236) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.642 (+/-0.236) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.642 (+/-0.236) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.642 (+/-0.236) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.642 (+/-0.236) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.617 (+/-0.238) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.642 (+/-0.236) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.642 (+/-0.236) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.642 (+/-0.236) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.617 (+/-0.238) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.642 (+/-0.236) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.642 (+/-0.236) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.642 (+/-0.236) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.642 (+/-0.236) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.642 (+/-0.236) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.617 (+/-0.238) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.642 (+/-0.236) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.642 (+/-0.236) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.642 (+/-0.236) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.642 (+/-0.236) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.642 (+/-0.236) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.642 (+/-0.236) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.642 (+/-0.236) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.642 (+/-0.236) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.642 (+/-0.236) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.617 (+/-0.238) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.642 (+/-0.236) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.642 (+/-0.236) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.642 (+/-0.236) for {'C': 461317.57456037874, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.642 (+/-0.236) for {'C': 461317.57456037874, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.642 (+/-0.236) for {'C': 461317.57456037874, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.642 (+/-0.236) for {'C': 461317.57456037874, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.642 (+/-0.236) for {'C': 461317.57456037874, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.642 (+/-0.236) for {'C': 461317.57456037874, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 461317.57456037874, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.642 (+/-0.236) for {'C': 461317.57456037874, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.617 (+/-0.238) for {'C': 461317.57456037874, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.642 (+/-0.236) for {'C': 461317.57456037874, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.642 (+/-0.236) for {'C': 461317.57456037874, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.642 (+/-0.236) for {'C': 469894.10860521457, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.617 (+/-0.238) for {'C': 469894.10860521457, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.642 (+/-0.236) for {'C': 469894.10860521457, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.642 (+/-0.236) for {'C': 469894.10860521457, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.642 (+/-0.236) for {'C': 469894.10860521457, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.642 (+/-0.236) for {'C': 469894.10860521457, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 469894.10860521457, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.642 (+/-0.236) for {'C': 469894.10860521457, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.617 (+/-0.238) for {'C': 469894.10860521457, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.642 (+/-0.236) for {'C': 469894.10860521457, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.642 (+/-0.236) for {'C': 469894.10860521457, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.642 (+/-0.236) for {'C': 478630.09232263727, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.617 (+/-0.238) for {'C': 478630.09232263727, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.642 (+/-0.236) for {'C': 478630.09232263727, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.642 (+/-0.236) for {'C': 478630.09232263727, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.642 (+/-0.236) for {'C': 478630.09232263727, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.642 (+/-0.236) for {'C': 478630.09232263727, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 478630.09232263727, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.642 (+/-0.236) for {'C': 478630.09232263727, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.617 (+/-0.238) for {'C': 478630.09232263727, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.642 (+/-0.236) for {'C': 478630.09232263727, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.642 (+/-0.236) for {'C': 478630.09232263727, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.642 (+/-0.236) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.617 (+/-0.238) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.642 (+/-0.236) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.642 (+/-0.236) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.642 (+/-0.236) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.642 (+/-0.236) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.642 (+/-0.236) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.617 (+/-0.238) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.642 (+/-0.236) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.642 (+/-0.236) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.642 (+/-0.236) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.617 (+/-0.238) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.642 (+/-0.236) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.642 (+/-0.236) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.642 (+/-0.236) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.642 (+/-0.236) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.642 (+/-0.236) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.617 (+/-0.238) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.642 (+/-0.236) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.642 (+/-0.236) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.642 (+/-0.236) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.617 (+/-0.238) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.642 (+/-0.236) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.642 (+/-0.236) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.642 (+/-0.236) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.642 (+/-0.236) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.642 (+/-0.236) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.617 (+/-0.238) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.642 (+/-0.236) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.642 (+/-0.236) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.642 (+/-0.236) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.617 (+/-0.238) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.642 (+/-0.236) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.642 (+/-0.236) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.642 (+/-0.236) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.642 (+/-0.236) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.642 (+/-0.236) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.617 (+/-0.238) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.642 (+/-0.236) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.642 (+/-0.236) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.642 (+/-0.236) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.617 (+/-0.238) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.642 (+/-0.236) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.642 (+/-0.236) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.642 (+/-0.236) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.642 (+/-0.236) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.642 (+/-0.236) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.617 (+/-0.238) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.642 (+/-0.236) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.642 (+/-0.236) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.641 (+/-0.236) for {'C': 10.0, 'kernel': 'linear'}
0.638 (+/-0.239) for {'C': 100.0, 'kernel': 'linear'}
0.612 (+/-0.242) for {'C': 1000.0, 'kernel': 'linear'}
0.613 (+/-0.241) for {'C': 10000.0, 'kernel': 'linear'}
0.613 (+/-0.241) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6416666666666666
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.797 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.797 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.798 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.797 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.797 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.798 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.797 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.797 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.797 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.797 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.797 (+/-0.492) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.797 (+/-0.492) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.798 (+/-0.492) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.798 (+/-0.492) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.797 (+/-0.492) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.848 (+/-0.460) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.797 (+/-0.492) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.797 (+/-0.492) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.797 (+/-0.492) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.797 (+/-0.492) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.797 (+/-0.492) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.797 (+/-0.492) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.798 (+/-0.492) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.798 (+/-0.492) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.797 (+/-0.492) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.848 (+/-0.460) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.797 (+/-0.492) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.797 (+/-0.492) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.797 (+/-0.492) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.797 (+/-0.492) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.797 (+/-0.492) for {'C': 461317.57456037874, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.797 (+/-0.492) for {'C': 461317.57456037874, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.798 (+/-0.492) for {'C': 461317.57456037874, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.798 (+/-0.492) for {'C': 461317.57456037874, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.797 (+/-0.492) for {'C': 461317.57456037874, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.848 (+/-0.460) for {'C': 461317.57456037874, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.798 (+/-0.492) for {'C': 461317.57456037874, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.797 (+/-0.492) for {'C': 461317.57456037874, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.797 (+/-0.492) for {'C': 461317.57456037874, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.797 (+/-0.492) for {'C': 461317.57456037874, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.797 (+/-0.492) for {'C': 461317.57456037874, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.797 (+/-0.492) for {'C': 469894.10860521457, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.797 (+/-0.492) for {'C': 469894.10860521457, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.798 (+/-0.492) for {'C': 469894.10860521457, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.798 (+/-0.492) for {'C': 469894.10860521457, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.797 (+/-0.492) for {'C': 469894.10860521457, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.848 (+/-0.460) for {'C': 469894.10860521457, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.798 (+/-0.492) for {'C': 469894.10860521457, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.797 (+/-0.492) for {'C': 469894.10860521457, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.797 (+/-0.492) for {'C': 469894.10860521457, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.797 (+/-0.492) for {'C': 469894.10860521457, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.797 (+/-0.492) for {'C': 469894.10860521457, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.797 (+/-0.492) for {'C': 478630.09232263727, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.797 (+/-0.492) for {'C': 478630.09232263727, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.798 (+/-0.492) for {'C': 478630.09232263727, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.798 (+/-0.492) for {'C': 478630.09232263727, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.797 (+/-0.492) for {'C': 478630.09232263727, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.848 (+/-0.460) for {'C': 478630.09232263727, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.798 (+/-0.492) for {'C': 478630.09232263727, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.797 (+/-0.492) for {'C': 478630.09232263727, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.797 (+/-0.492) for {'C': 478630.09232263727, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.797 (+/-0.492) for {'C': 478630.09232263727, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.797 (+/-0.492) for {'C': 478630.09232263727, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.797 (+/-0.492) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.797 (+/-0.492) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.798 (+/-0.492) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.798 (+/-0.492) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.797 (+/-0.492) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.848 (+/-0.460) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.798 (+/-0.492) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.797 (+/-0.492) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.797 (+/-0.492) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.797 (+/-0.492) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.797 (+/-0.492) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.797 (+/-0.492) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.797 (+/-0.492) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.798 (+/-0.492) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.798 (+/-0.492) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.797 (+/-0.492) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.848 (+/-0.460) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.798 (+/-0.492) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.797 (+/-0.492) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.797 (+/-0.492) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.797 (+/-0.492) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.797 (+/-0.492) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.797 (+/-0.492) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.797 (+/-0.492) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.798 (+/-0.492) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.798 (+/-0.492) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.797 (+/-0.492) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.848 (+/-0.460) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.798 (+/-0.492) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.798 (+/-0.492) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.797 (+/-0.492) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.797 (+/-0.492) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.797 (+/-0.492) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.797 (+/-0.492) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.797 (+/-0.492) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.798 (+/-0.492) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.798 (+/-0.492) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.797 (+/-0.492) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.848 (+/-0.460) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.798 (+/-0.492) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.798 (+/-0.492) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.797 (+/-0.492) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.797 (+/-0.492) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.797 (+/-0.492) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.797 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.797 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.798 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.798 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.797 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.848 (+/-0.460) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.798 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.798 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.797 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.797 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.797 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.655 (+/-0.392) for {'C': 10.0, 'kernel': 'linear'}
0.643 (+/-0.306) for {'C': 100.0, 'kernel': 'linear'}
0.626 (+/-0.318) for {'C': 1000.0, 'kernel': 'linear'}
0.626 (+/-0.318) for {'C': 10000.0, 'kernel': 'linear'}
0.626 (+/-0.318) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.642 (+/-0.236) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.642 (+/-0.236) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.667 (+/-0.316) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.642 (+/-0.236) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.642 (+/-0.236) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.667 (+/-0.316) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.642 (+/-0.236) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.642 (+/-0.236) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.642 (+/-0.236) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.642 (+/-0.236) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.642 (+/-0.236) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.642 (+/-0.236) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.667 (+/-0.316) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.667 (+/-0.316) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.642 (+/-0.236) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.683 (+/-0.296) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.642 (+/-0.236) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.642 (+/-0.236) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.642 (+/-0.236) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.642 (+/-0.236) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.642 (+/-0.236) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.642 (+/-0.236) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.667 (+/-0.316) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.667 (+/-0.316) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.642 (+/-0.236) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.683 (+/-0.296) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.642 (+/-0.236) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.642 (+/-0.236) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.642 (+/-0.236) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.642 (+/-0.236) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.642 (+/-0.236) for {'C': 461317.57456037874, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.642 (+/-0.236) for {'C': 461317.57456037874, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.667 (+/-0.316) for {'C': 461317.57456037874, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.667 (+/-0.316) for {'C': 461317.57456037874, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.642 (+/-0.236) for {'C': 461317.57456037874, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.683 (+/-0.296) for {'C': 461317.57456037874, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.667 (+/-0.316) for {'C': 461317.57456037874, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.642 (+/-0.236) for {'C': 461317.57456037874, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.642 (+/-0.236) for {'C': 461317.57456037874, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.642 (+/-0.236) for {'C': 461317.57456037874, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.642 (+/-0.236) for {'C': 461317.57456037874, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.642 (+/-0.236) for {'C': 469894.10860521457, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.642 (+/-0.236) for {'C': 469894.10860521457, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.667 (+/-0.316) for {'C': 469894.10860521457, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.667 (+/-0.316) for {'C': 469894.10860521457, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.642 (+/-0.236) for {'C': 469894.10860521457, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.683 (+/-0.296) for {'C': 469894.10860521457, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.667 (+/-0.316) for {'C': 469894.10860521457, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.642 (+/-0.236) for {'C': 469894.10860521457, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.642 (+/-0.236) for {'C': 469894.10860521457, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.642 (+/-0.236) for {'C': 469894.10860521457, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.642 (+/-0.236) for {'C': 469894.10860521457, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.642 (+/-0.236) for {'C': 478630.09232263727, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.642 (+/-0.236) for {'C': 478630.09232263727, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.667 (+/-0.316) for {'C': 478630.09232263727, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.667 (+/-0.316) for {'C': 478630.09232263727, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.642 (+/-0.236) for {'C': 478630.09232263727, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.683 (+/-0.296) for {'C': 478630.09232263727, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.667 (+/-0.316) for {'C': 478630.09232263727, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.642 (+/-0.236) for {'C': 478630.09232263727, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.642 (+/-0.236) for {'C': 478630.09232263727, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.642 (+/-0.236) for {'C': 478630.09232263727, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.642 (+/-0.236) for {'C': 478630.09232263727, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.642 (+/-0.236) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.642 (+/-0.236) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.667 (+/-0.316) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.667 (+/-0.316) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.642 (+/-0.236) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.683 (+/-0.296) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.667 (+/-0.316) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.642 (+/-0.236) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.642 (+/-0.236) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.642 (+/-0.236) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.642 (+/-0.236) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.642 (+/-0.236) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.642 (+/-0.236) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.667 (+/-0.316) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.667 (+/-0.316) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.642 (+/-0.236) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.683 (+/-0.296) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.667 (+/-0.316) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.642 (+/-0.236) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.642 (+/-0.236) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.642 (+/-0.236) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.642 (+/-0.236) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.642 (+/-0.236) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.642 (+/-0.236) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.667 (+/-0.316) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.667 (+/-0.316) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.642 (+/-0.236) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.683 (+/-0.296) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.667 (+/-0.316) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.667 (+/-0.316) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.642 (+/-0.236) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.642 (+/-0.236) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.642 (+/-0.236) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.642 (+/-0.236) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.642 (+/-0.236) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.667 (+/-0.316) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.667 (+/-0.316) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.642 (+/-0.236) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.683 (+/-0.296) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.667 (+/-0.316) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.667 (+/-0.316) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.642 (+/-0.236) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.642 (+/-0.236) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.642 (+/-0.236) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.642 (+/-0.236) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.642 (+/-0.236) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.667 (+/-0.316) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.667 (+/-0.316) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.642 (+/-0.236) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.683 (+/-0.296) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.667 (+/-0.316) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.667 (+/-0.316) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.642 (+/-0.236) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.642 (+/-0.236) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.642 (+/-0.236) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.635 (+/-0.325) for {'C': 10.0, 'kernel': 'linear'}
0.638 (+/-0.239) for {'C': 100.0, 'kernel': 'linear'}
0.612 (+/-0.242) for {'C': 1000.0, 'kernel': 'linear'}
0.613 (+/-0.241) for {'C': 10000.0, 'kernel': 'linear'}
0.613 (+/-0.241) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6833333333333332
第1轮loop中，tunemodel函数得到的最优参数是： ([{'C': array([436515.83224017, 438126.98202249, 439744.07844737, 441367.14346344,
       442996.19910036, 444631.26746911, 446272.37076225, 447919.53125428,
       449572.77130191, 451232.11334434, 452897.57990362]), 'kernel': ['rbf'], 'gamma': array([2.46603934e-07, 2.47514132e-07, 2.48427689e-07, 2.49344619e-07,
       2.50264933e-07, 2.51188643e-07, 2.52115763e-07, 2.53046305e-07,
       2.53980281e-07, 2.54917705e-07, 2.55858589e-07])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}], {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}, 0.6833333333333332)
这是第1次迭代微调C和gamma。
第1次迭代，得到delta： [33998.82485353     0.        ]
执行tunemodel()函数前，使用的grid search parameter是：
 [{'C': array([436515.83224017, 438126.98202249, 439744.07844737, 441367.14346344,
       442996.19910036, 444631.26746911, 446272.37076225, 447919.53125428,
       449572.77130191, 451232.11334434, 452897.57990362]), 'kernel': ['rbf'], 'gamma': array([2.46603934e-07, 2.47514132e-07, 2.48427689e-07, 2.49344619e-07,
       2.50264933e-07, 2.51188643e-07, 2.52115763e-07, 2.53046305e-07,
       2.53980281e-07, 2.54917705e-07, 2.55858589e-07])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.797 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.797 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.475141318074313e-07}
0.797 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.4842768936967995e-07}
0.797 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.49344618809783e-07}
0.797 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.50264932573108e-07}
0.797 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.797 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.530463049461398e-07}
0.797 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.5398028137728204e-07}
0.797 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.797 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.797 (+/-0.492) for {'C': 438126.9820224933, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.797 (+/-0.492) for {'C': 438126.9820224933, 'kernel': 'rbf', 'gamma': 2.475141318074313e-07}
0.797 (+/-0.492) for {'C': 438126.9820224933, 'kernel': 'rbf', 'gamma': 2.4842768936967995e-07}
0.797 (+/-0.492) for {'C': 438126.9820224933, 'kernel': 'rbf', 'gamma': 2.49344618809783e-07}
0.797 (+/-0.492) for {'C': 438126.9820224933, 'kernel': 'rbf', 'gamma': 2.50264932573108e-07}
0.797 (+/-0.492) for {'C': 438126.9820224933, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 438126.9820224933, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.797 (+/-0.492) for {'C': 438126.9820224933, 'kernel': 'rbf', 'gamma': 2.530463049461398e-07}
0.797 (+/-0.492) for {'C': 438126.9820224933, 'kernel': 'rbf', 'gamma': 2.5398028137728204e-07}
0.797 (+/-0.492) for {'C': 438126.9820224933, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.797 (+/-0.492) for {'C': 438126.9820224933, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.797 (+/-0.492) for {'C': 439744.0784473682, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.797 (+/-0.492) for {'C': 439744.0784473682, 'kernel': 'rbf', 'gamma': 2.475141318074313e-07}
0.797 (+/-0.492) for {'C': 439744.0784473682, 'kernel': 'rbf', 'gamma': 2.4842768936967995e-07}
0.797 (+/-0.492) for {'C': 439744.0784473682, 'kernel': 'rbf', 'gamma': 2.49344618809783e-07}
0.797 (+/-0.492) for {'C': 439744.0784473682, 'kernel': 'rbf', 'gamma': 2.50264932573108e-07}
0.797 (+/-0.492) for {'C': 439744.0784473682, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 439744.0784473682, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.797 (+/-0.492) for {'C': 439744.0784473682, 'kernel': 'rbf', 'gamma': 2.530463049461398e-07}
0.797 (+/-0.492) for {'C': 439744.0784473682, 'kernel': 'rbf', 'gamma': 2.5398028137728204e-07}
0.797 (+/-0.492) for {'C': 439744.0784473682, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.797 (+/-0.492) for {'C': 439744.0784473682, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.797 (+/-0.492) for {'C': 441367.1434634396, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.797 (+/-0.492) for {'C': 441367.1434634396, 'kernel': 'rbf', 'gamma': 2.475141318074313e-07}
0.797 (+/-0.492) for {'C': 441367.1434634396, 'kernel': 'rbf', 'gamma': 2.4842768936967995e-07}
0.797 (+/-0.492) for {'C': 441367.1434634396, 'kernel': 'rbf', 'gamma': 2.49344618809783e-07}
0.797 (+/-0.492) for {'C': 441367.1434634396, 'kernel': 'rbf', 'gamma': 2.50264932573108e-07}
0.797 (+/-0.492) for {'C': 441367.1434634396, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 441367.1434634396, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.797 (+/-0.492) for {'C': 441367.1434634396, 'kernel': 'rbf', 'gamma': 2.530463049461398e-07}
0.797 (+/-0.492) for {'C': 441367.1434634396, 'kernel': 'rbf', 'gamma': 2.5398028137728204e-07}
0.797 (+/-0.492) for {'C': 441367.1434634396, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.797 (+/-0.492) for {'C': 441367.1434634396, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.797 (+/-0.492) for {'C': 442996.199100363, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.797 (+/-0.492) for {'C': 442996.199100363, 'kernel': 'rbf', 'gamma': 2.475141318074313e-07}
0.797 (+/-0.492) for {'C': 442996.199100363, 'kernel': 'rbf', 'gamma': 2.4842768936967995e-07}
0.797 (+/-0.492) for {'C': 442996.199100363, 'kernel': 'rbf', 'gamma': 2.49344618809783e-07}
0.797 (+/-0.492) for {'C': 442996.199100363, 'kernel': 'rbf', 'gamma': 2.50264932573108e-07}
0.797 (+/-0.492) for {'C': 442996.199100363, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 442996.199100363, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.797 (+/-0.492) for {'C': 442996.199100363, 'kernel': 'rbf', 'gamma': 2.530463049461398e-07}
0.797 (+/-0.492) for {'C': 442996.199100363, 'kernel': 'rbf', 'gamma': 2.5398028137728204e-07}
0.797 (+/-0.492) for {'C': 442996.199100363, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.797 (+/-0.492) for {'C': 442996.199100363, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.797 (+/-0.492) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.797 (+/-0.492) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.475141318074313e-07}
0.797 (+/-0.492) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.4842768936967995e-07}
0.797 (+/-0.492) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.49344618809783e-07}
0.797 (+/-0.492) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.50264932573108e-07}
0.797 (+/-0.492) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.797 (+/-0.492) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.530463049461398e-07}
0.797 (+/-0.492) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.5398028137728204e-07}
0.797 (+/-0.492) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.797 (+/-0.492) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.797 (+/-0.492) for {'C': 446272.3707622524, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.797 (+/-0.492) for {'C': 446272.3707622524, 'kernel': 'rbf', 'gamma': 2.475141318074313e-07}
0.797 (+/-0.492) for {'C': 446272.3707622524, 'kernel': 'rbf', 'gamma': 2.4842768936967995e-07}
0.797 (+/-0.492) for {'C': 446272.3707622524, 'kernel': 'rbf', 'gamma': 2.49344618809783e-07}
0.797 (+/-0.492) for {'C': 446272.3707622524, 'kernel': 'rbf', 'gamma': 2.50264932573108e-07}
0.797 (+/-0.492) for {'C': 446272.3707622524, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 446272.3707622524, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.797 (+/-0.492) for {'C': 446272.3707622524, 'kernel': 'rbf', 'gamma': 2.530463049461398e-07}
0.797 (+/-0.492) for {'C': 446272.3707622524, 'kernel': 'rbf', 'gamma': 2.5398028137728204e-07}
0.797 (+/-0.492) for {'C': 446272.3707622524, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.797 (+/-0.492) for {'C': 446272.3707622524, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.797 (+/-0.492) for {'C': 447919.53125428315, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.797 (+/-0.492) for {'C': 447919.53125428315, 'kernel': 'rbf', 'gamma': 2.475141318074313e-07}
0.797 (+/-0.492) for {'C': 447919.53125428315, 'kernel': 'rbf', 'gamma': 2.4842768936967995e-07}
0.797 (+/-0.492) for {'C': 447919.53125428315, 'kernel': 'rbf', 'gamma': 2.49344618809783e-07}
0.797 (+/-0.492) for {'C': 447919.53125428315, 'kernel': 'rbf', 'gamma': 2.50264932573108e-07}
0.797 (+/-0.492) for {'C': 447919.53125428315, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 447919.53125428315, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.797 (+/-0.492) for {'C': 447919.53125428315, 'kernel': 'rbf', 'gamma': 2.530463049461398e-07}
0.797 (+/-0.492) for {'C': 447919.53125428315, 'kernel': 'rbf', 'gamma': 2.5398028137728204e-07}
0.797 (+/-0.492) for {'C': 447919.53125428315, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.797 (+/-0.492) for {'C': 447919.53125428315, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.797 (+/-0.492) for {'C': 449572.77130190533, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.797 (+/-0.492) for {'C': 449572.77130190533, 'kernel': 'rbf', 'gamma': 2.475141318074313e-07}
0.797 (+/-0.492) for {'C': 449572.77130190533, 'kernel': 'rbf', 'gamma': 2.4842768936967995e-07}
0.797 (+/-0.492) for {'C': 449572.77130190533, 'kernel': 'rbf', 'gamma': 2.49344618809783e-07}
0.797 (+/-0.492) for {'C': 449572.77130190533, 'kernel': 'rbf', 'gamma': 2.50264932573108e-07}
0.797 (+/-0.492) for {'C': 449572.77130190533, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 449572.77130190533, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.797 (+/-0.492) for {'C': 449572.77130190533, 'kernel': 'rbf', 'gamma': 2.530463049461398e-07}
0.797 (+/-0.492) for {'C': 449572.77130190533, 'kernel': 'rbf', 'gamma': 2.5398028137728204e-07}
0.797 (+/-0.492) for {'C': 449572.77130190533, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.797 (+/-0.492) for {'C': 449572.77130190533, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.797 (+/-0.492) for {'C': 451232.1133443365, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.797 (+/-0.492) for {'C': 451232.1133443365, 'kernel': 'rbf', 'gamma': 2.475141318074313e-07}
0.797 (+/-0.492) for {'C': 451232.1133443365, 'kernel': 'rbf', 'gamma': 2.4842768936967995e-07}
0.797 (+/-0.492) for {'C': 451232.1133443365, 'kernel': 'rbf', 'gamma': 2.49344618809783e-07}
0.797 (+/-0.492) for {'C': 451232.1133443365, 'kernel': 'rbf', 'gamma': 2.50264932573108e-07}
0.797 (+/-0.492) for {'C': 451232.1133443365, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 451232.1133443365, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.797 (+/-0.492) for {'C': 451232.1133443365, 'kernel': 'rbf', 'gamma': 2.530463049461398e-07}
0.797 (+/-0.492) for {'C': 451232.1133443365, 'kernel': 'rbf', 'gamma': 2.5398028137728204e-07}
0.797 (+/-0.492) for {'C': 451232.1133443365, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.797 (+/-0.492) for {'C': 451232.1133443365, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.797 (+/-0.492) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.797 (+/-0.492) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.475141318074313e-07}
0.797 (+/-0.492) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.4842768936967995e-07}
0.797 (+/-0.492) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.49344618809783e-07}
0.797 (+/-0.492) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.50264932573108e-07}
0.797 (+/-0.492) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.797 (+/-0.492) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.530463049461398e-07}
0.797 (+/-0.492) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.5398028137728204e-07}
0.797 (+/-0.492) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.797 (+/-0.492) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.722 (+/-0.417) for {'C': 10.0, 'kernel': 'linear'}
0.643 (+/-0.306) for {'C': 100.0, 'kernel': 'linear'}
0.626 (+/-0.318) for {'C': 1000.0, 'kernel': 'linear'}
0.626 (+/-0.318) for {'C': 10000.0, 'kernel': 'linear'}
0.626 (+/-0.318) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.642 (+/-0.236) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.642 (+/-0.236) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.475141318074313e-07}
0.642 (+/-0.236) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.4842768936967995e-07}
0.642 (+/-0.236) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.49344618809783e-07}
0.642 (+/-0.236) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.50264932573108e-07}
0.642 (+/-0.236) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.642 (+/-0.236) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.530463049461398e-07}
0.642 (+/-0.236) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.5398028137728204e-07}
0.642 (+/-0.236) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.642 (+/-0.236) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.642 (+/-0.236) for {'C': 438126.9820224933, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.642 (+/-0.236) for {'C': 438126.9820224933, 'kernel': 'rbf', 'gamma': 2.475141318074313e-07}
0.642 (+/-0.236) for {'C': 438126.9820224933, 'kernel': 'rbf', 'gamma': 2.4842768936967995e-07}
0.642 (+/-0.236) for {'C': 438126.9820224933, 'kernel': 'rbf', 'gamma': 2.49344618809783e-07}
0.642 (+/-0.236) for {'C': 438126.9820224933, 'kernel': 'rbf', 'gamma': 2.50264932573108e-07}
0.642 (+/-0.236) for {'C': 438126.9820224933, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 438126.9820224933, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.642 (+/-0.236) for {'C': 438126.9820224933, 'kernel': 'rbf', 'gamma': 2.530463049461398e-07}
0.642 (+/-0.236) for {'C': 438126.9820224933, 'kernel': 'rbf', 'gamma': 2.5398028137728204e-07}
0.642 (+/-0.236) for {'C': 438126.9820224933, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.642 (+/-0.236) for {'C': 438126.9820224933, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.642 (+/-0.236) for {'C': 439744.0784473682, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.642 (+/-0.236) for {'C': 439744.0784473682, 'kernel': 'rbf', 'gamma': 2.475141318074313e-07}
0.642 (+/-0.236) for {'C': 439744.0784473682, 'kernel': 'rbf', 'gamma': 2.4842768936967995e-07}
0.642 (+/-0.236) for {'C': 439744.0784473682, 'kernel': 'rbf', 'gamma': 2.49344618809783e-07}
0.642 (+/-0.236) for {'C': 439744.0784473682, 'kernel': 'rbf', 'gamma': 2.50264932573108e-07}
0.642 (+/-0.236) for {'C': 439744.0784473682, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 439744.0784473682, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.642 (+/-0.236) for {'C': 439744.0784473682, 'kernel': 'rbf', 'gamma': 2.530463049461398e-07}
0.642 (+/-0.236) for {'C': 439744.0784473682, 'kernel': 'rbf', 'gamma': 2.5398028137728204e-07}
0.642 (+/-0.236) for {'C': 439744.0784473682, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.642 (+/-0.236) for {'C': 439744.0784473682, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.642 (+/-0.236) for {'C': 441367.1434634396, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.642 (+/-0.236) for {'C': 441367.1434634396, 'kernel': 'rbf', 'gamma': 2.475141318074313e-07}
0.642 (+/-0.236) for {'C': 441367.1434634396, 'kernel': 'rbf', 'gamma': 2.4842768936967995e-07}
0.642 (+/-0.236) for {'C': 441367.1434634396, 'kernel': 'rbf', 'gamma': 2.49344618809783e-07}
0.642 (+/-0.236) for {'C': 441367.1434634396, 'kernel': 'rbf', 'gamma': 2.50264932573108e-07}
0.642 (+/-0.236) for {'C': 441367.1434634396, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 441367.1434634396, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.642 (+/-0.236) for {'C': 441367.1434634396, 'kernel': 'rbf', 'gamma': 2.530463049461398e-07}
0.642 (+/-0.236) for {'C': 441367.1434634396, 'kernel': 'rbf', 'gamma': 2.5398028137728204e-07}
0.642 (+/-0.236) for {'C': 441367.1434634396, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.642 (+/-0.236) for {'C': 441367.1434634396, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.642 (+/-0.236) for {'C': 442996.199100363, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.642 (+/-0.236) for {'C': 442996.199100363, 'kernel': 'rbf', 'gamma': 2.475141318074313e-07}
0.642 (+/-0.236) for {'C': 442996.199100363, 'kernel': 'rbf', 'gamma': 2.4842768936967995e-07}
0.642 (+/-0.236) for {'C': 442996.199100363, 'kernel': 'rbf', 'gamma': 2.49344618809783e-07}
0.642 (+/-0.236) for {'C': 442996.199100363, 'kernel': 'rbf', 'gamma': 2.50264932573108e-07}
0.642 (+/-0.236) for {'C': 442996.199100363, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 442996.199100363, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.642 (+/-0.236) for {'C': 442996.199100363, 'kernel': 'rbf', 'gamma': 2.530463049461398e-07}
0.642 (+/-0.236) for {'C': 442996.199100363, 'kernel': 'rbf', 'gamma': 2.5398028137728204e-07}
0.642 (+/-0.236) for {'C': 442996.199100363, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.642 (+/-0.236) for {'C': 442996.199100363, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.642 (+/-0.236) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.642 (+/-0.236) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.475141318074313e-07}
0.642 (+/-0.236) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.4842768936967995e-07}
0.642 (+/-0.236) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.49344618809783e-07}
0.642 (+/-0.236) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.50264932573108e-07}
0.642 (+/-0.236) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.642 (+/-0.236) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.530463049461398e-07}
0.642 (+/-0.236) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.5398028137728204e-07}
0.642 (+/-0.236) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.642 (+/-0.236) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.642 (+/-0.236) for {'C': 446272.3707622524, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.642 (+/-0.236) for {'C': 446272.3707622524, 'kernel': 'rbf', 'gamma': 2.475141318074313e-07}
0.642 (+/-0.236) for {'C': 446272.3707622524, 'kernel': 'rbf', 'gamma': 2.4842768936967995e-07}
0.642 (+/-0.236) for {'C': 446272.3707622524, 'kernel': 'rbf', 'gamma': 2.49344618809783e-07}
0.642 (+/-0.236) for {'C': 446272.3707622524, 'kernel': 'rbf', 'gamma': 2.50264932573108e-07}
0.642 (+/-0.236) for {'C': 446272.3707622524, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 446272.3707622524, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.642 (+/-0.236) for {'C': 446272.3707622524, 'kernel': 'rbf', 'gamma': 2.530463049461398e-07}
0.642 (+/-0.236) for {'C': 446272.3707622524, 'kernel': 'rbf', 'gamma': 2.5398028137728204e-07}
0.642 (+/-0.236) for {'C': 446272.3707622524, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.642 (+/-0.236) for {'C': 446272.3707622524, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.642 (+/-0.236) for {'C': 447919.53125428315, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.642 (+/-0.236) for {'C': 447919.53125428315, 'kernel': 'rbf', 'gamma': 2.475141318074313e-07}
0.642 (+/-0.236) for {'C': 447919.53125428315, 'kernel': 'rbf', 'gamma': 2.4842768936967995e-07}
0.642 (+/-0.236) for {'C': 447919.53125428315, 'kernel': 'rbf', 'gamma': 2.49344618809783e-07}
0.642 (+/-0.236) for {'C': 447919.53125428315, 'kernel': 'rbf', 'gamma': 2.50264932573108e-07}
0.642 (+/-0.236) for {'C': 447919.53125428315, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 447919.53125428315, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.642 (+/-0.236) for {'C': 447919.53125428315, 'kernel': 'rbf', 'gamma': 2.530463049461398e-07}
0.642 (+/-0.236) for {'C': 447919.53125428315, 'kernel': 'rbf', 'gamma': 2.5398028137728204e-07}
0.642 (+/-0.236) for {'C': 447919.53125428315, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.642 (+/-0.236) for {'C': 447919.53125428315, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.642 (+/-0.236) for {'C': 449572.77130190533, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.642 (+/-0.236) for {'C': 449572.77130190533, 'kernel': 'rbf', 'gamma': 2.475141318074313e-07}
0.642 (+/-0.236) for {'C': 449572.77130190533, 'kernel': 'rbf', 'gamma': 2.4842768936967995e-07}
0.642 (+/-0.236) for {'C': 449572.77130190533, 'kernel': 'rbf', 'gamma': 2.49344618809783e-07}
0.642 (+/-0.236) for {'C': 449572.77130190533, 'kernel': 'rbf', 'gamma': 2.50264932573108e-07}
0.642 (+/-0.236) for {'C': 449572.77130190533, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 449572.77130190533, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.642 (+/-0.236) for {'C': 449572.77130190533, 'kernel': 'rbf', 'gamma': 2.530463049461398e-07}
0.642 (+/-0.236) for {'C': 449572.77130190533, 'kernel': 'rbf', 'gamma': 2.5398028137728204e-07}
0.642 (+/-0.236) for {'C': 449572.77130190533, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.642 (+/-0.236) for {'C': 449572.77130190533, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.642 (+/-0.236) for {'C': 451232.1133443365, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.642 (+/-0.236) for {'C': 451232.1133443365, 'kernel': 'rbf', 'gamma': 2.475141318074313e-07}
0.642 (+/-0.236) for {'C': 451232.1133443365, 'kernel': 'rbf', 'gamma': 2.4842768936967995e-07}
0.642 (+/-0.236) for {'C': 451232.1133443365, 'kernel': 'rbf', 'gamma': 2.49344618809783e-07}
0.642 (+/-0.236) for {'C': 451232.1133443365, 'kernel': 'rbf', 'gamma': 2.50264932573108e-07}
0.642 (+/-0.236) for {'C': 451232.1133443365, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 451232.1133443365, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.642 (+/-0.236) for {'C': 451232.1133443365, 'kernel': 'rbf', 'gamma': 2.530463049461398e-07}
0.642 (+/-0.236) for {'C': 451232.1133443365, 'kernel': 'rbf', 'gamma': 2.5398028137728204e-07}
0.642 (+/-0.236) for {'C': 451232.1133443365, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.642 (+/-0.236) for {'C': 451232.1133443365, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.642 (+/-0.236) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.642 (+/-0.236) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.475141318074313e-07}
0.642 (+/-0.236) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.4842768936967995e-07}
0.642 (+/-0.236) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.49344618809783e-07}
0.642 (+/-0.236) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.50264932573108e-07}
0.642 (+/-0.236) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.642 (+/-0.236) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.530463049461398e-07}
0.642 (+/-0.236) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.5398028137728204e-07}
0.642 (+/-0.236) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.642 (+/-0.236) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.641 (+/-0.236) for {'C': 10.0, 'kernel': 'linear'}
0.638 (+/-0.239) for {'C': 100.0, 'kernel': 'linear'}
0.612 (+/-0.242) for {'C': 1000.0, 'kernel': 'linear'}
0.613 (+/-0.241) for {'C': 10000.0, 'kernel': 'linear'}
0.613 (+/-0.241) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6416666666666666
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.797 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.797 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.475141318074313e-07}
0.797 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.4842768936967995e-07}
0.848 (+/-0.459) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.49344618809783e-07}
0.848 (+/-0.460) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.50264932573108e-07}
0.798 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.798 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.798 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.530463049461398e-07}
0.798 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.5398028137728204e-07}
0.798 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.797 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.797 (+/-0.492) for {'C': 438126.9820224933, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.797 (+/-0.492) for {'C': 438126.9820224933, 'kernel': 'rbf', 'gamma': 2.475141318074313e-07}
0.797 (+/-0.492) for {'C': 438126.9820224933, 'kernel': 'rbf', 'gamma': 2.4842768936967995e-07}
0.848 (+/-0.459) for {'C': 438126.9820224933, 'kernel': 'rbf', 'gamma': 2.49344618809783e-07}
0.848 (+/-0.460) for {'C': 438126.9820224933, 'kernel': 'rbf', 'gamma': 2.50264932573108e-07}
0.798 (+/-0.492) for {'C': 438126.9820224933, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.798 (+/-0.492) for {'C': 438126.9820224933, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.798 (+/-0.492) for {'C': 438126.9820224933, 'kernel': 'rbf', 'gamma': 2.530463049461398e-07}
0.798 (+/-0.492) for {'C': 438126.9820224933, 'kernel': 'rbf', 'gamma': 2.5398028137728204e-07}
0.798 (+/-0.492) for {'C': 438126.9820224933, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.797 (+/-0.492) for {'C': 438126.9820224933, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.797 (+/-0.492) for {'C': 439744.0784473682, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.797 (+/-0.492) for {'C': 439744.0784473682, 'kernel': 'rbf', 'gamma': 2.475141318074313e-07}
0.797 (+/-0.492) for {'C': 439744.0784473682, 'kernel': 'rbf', 'gamma': 2.4842768936967995e-07}
0.848 (+/-0.459) for {'C': 439744.0784473682, 'kernel': 'rbf', 'gamma': 2.49344618809783e-07}
0.848 (+/-0.460) for {'C': 439744.0784473682, 'kernel': 'rbf', 'gamma': 2.50264932573108e-07}
0.848 (+/-0.460) for {'C': 439744.0784473682, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.798 (+/-0.492) for {'C': 439744.0784473682, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.798 (+/-0.492) for {'C': 439744.0784473682, 'kernel': 'rbf', 'gamma': 2.530463049461398e-07}
0.798 (+/-0.492) for {'C': 439744.0784473682, 'kernel': 'rbf', 'gamma': 2.5398028137728204e-07}
0.798 (+/-0.492) for {'C': 439744.0784473682, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.797 (+/-0.492) for {'C': 439744.0784473682, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.797 (+/-0.492) for {'C': 441367.1434634396, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.797 (+/-0.492) for {'C': 441367.1434634396, 'kernel': 'rbf', 'gamma': 2.475141318074313e-07}
0.797 (+/-0.492) for {'C': 441367.1434634396, 'kernel': 'rbf', 'gamma': 2.4842768936967995e-07}
0.848 (+/-0.459) for {'C': 441367.1434634396, 'kernel': 'rbf', 'gamma': 2.49344618809783e-07}
0.848 (+/-0.460) for {'C': 441367.1434634396, 'kernel': 'rbf', 'gamma': 2.50264932573108e-07}
0.848 (+/-0.460) for {'C': 441367.1434634396, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.798 (+/-0.492) for {'C': 441367.1434634396, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.798 (+/-0.492) for {'C': 441367.1434634396, 'kernel': 'rbf', 'gamma': 2.530463049461398e-07}
0.798 (+/-0.492) for {'C': 441367.1434634396, 'kernel': 'rbf', 'gamma': 2.5398028137728204e-07}
0.798 (+/-0.492) for {'C': 441367.1434634396, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.797 (+/-0.492) for {'C': 441367.1434634396, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.797 (+/-0.492) for {'C': 442996.199100363, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.797 (+/-0.492) for {'C': 442996.199100363, 'kernel': 'rbf', 'gamma': 2.475141318074313e-07}
0.797 (+/-0.492) for {'C': 442996.199100363, 'kernel': 'rbf', 'gamma': 2.4842768936967995e-07}
0.848 (+/-0.459) for {'C': 442996.199100363, 'kernel': 'rbf', 'gamma': 2.49344618809783e-07}
0.848 (+/-0.460) for {'C': 442996.199100363, 'kernel': 'rbf', 'gamma': 2.50264932573108e-07}
0.848 (+/-0.460) for {'C': 442996.199100363, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.798 (+/-0.492) for {'C': 442996.199100363, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.798 (+/-0.492) for {'C': 442996.199100363, 'kernel': 'rbf', 'gamma': 2.530463049461398e-07}
0.798 (+/-0.492) for {'C': 442996.199100363, 'kernel': 'rbf', 'gamma': 2.5398028137728204e-07}
0.798 (+/-0.492) for {'C': 442996.199100363, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.797 (+/-0.492) for {'C': 442996.199100363, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.797 (+/-0.492) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.797 (+/-0.492) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.475141318074313e-07}
0.797 (+/-0.492) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.4842768936967995e-07}
0.848 (+/-0.459) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.49344618809783e-07}
0.848 (+/-0.460) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.50264932573108e-07}
0.848 (+/-0.460) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.798 (+/-0.492) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.798 (+/-0.492) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.530463049461398e-07}
0.798 (+/-0.492) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.5398028137728204e-07}
0.798 (+/-0.492) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.797 (+/-0.492) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.797 (+/-0.492) for {'C': 446272.3707622524, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.797 (+/-0.492) for {'C': 446272.3707622524, 'kernel': 'rbf', 'gamma': 2.475141318074313e-07}
0.797 (+/-0.492) for {'C': 446272.3707622524, 'kernel': 'rbf', 'gamma': 2.4842768936967995e-07}
0.848 (+/-0.459) for {'C': 446272.3707622524, 'kernel': 'rbf', 'gamma': 2.49344618809783e-07}
0.848 (+/-0.460) for {'C': 446272.3707622524, 'kernel': 'rbf', 'gamma': 2.50264932573108e-07}
0.798 (+/-0.492) for {'C': 446272.3707622524, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.798 (+/-0.492) for {'C': 446272.3707622524, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.798 (+/-0.492) for {'C': 446272.3707622524, 'kernel': 'rbf', 'gamma': 2.530463049461398e-07}
0.798 (+/-0.492) for {'C': 446272.3707622524, 'kernel': 'rbf', 'gamma': 2.5398028137728204e-07}
0.798 (+/-0.492) for {'C': 446272.3707622524, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.797 (+/-0.492) for {'C': 446272.3707622524, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.797 (+/-0.492) for {'C': 447919.53125428315, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.797 (+/-0.492) for {'C': 447919.53125428315, 'kernel': 'rbf', 'gamma': 2.475141318074313e-07}
0.797 (+/-0.492) for {'C': 447919.53125428315, 'kernel': 'rbf', 'gamma': 2.4842768936967995e-07}
0.848 (+/-0.459) for {'C': 447919.53125428315, 'kernel': 'rbf', 'gamma': 2.49344618809783e-07}
0.848 (+/-0.460) for {'C': 447919.53125428315, 'kernel': 'rbf', 'gamma': 2.50264932573108e-07}
0.848 (+/-0.460) for {'C': 447919.53125428315, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.798 (+/-0.492) for {'C': 447919.53125428315, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.798 (+/-0.492) for {'C': 447919.53125428315, 'kernel': 'rbf', 'gamma': 2.530463049461398e-07}
0.798 (+/-0.492) for {'C': 447919.53125428315, 'kernel': 'rbf', 'gamma': 2.5398028137728204e-07}
0.798 (+/-0.492) for {'C': 447919.53125428315, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.797 (+/-0.492) for {'C': 447919.53125428315, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.797 (+/-0.492) for {'C': 449572.77130190533, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.797 (+/-0.492) for {'C': 449572.77130190533, 'kernel': 'rbf', 'gamma': 2.475141318074313e-07}
0.797 (+/-0.492) for {'C': 449572.77130190533, 'kernel': 'rbf', 'gamma': 2.4842768936967995e-07}
0.848 (+/-0.459) for {'C': 449572.77130190533, 'kernel': 'rbf', 'gamma': 2.49344618809783e-07}
0.848 (+/-0.460) for {'C': 449572.77130190533, 'kernel': 'rbf', 'gamma': 2.50264932573108e-07}
0.848 (+/-0.460) for {'C': 449572.77130190533, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.798 (+/-0.492) for {'C': 449572.77130190533, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.798 (+/-0.492) for {'C': 449572.77130190533, 'kernel': 'rbf', 'gamma': 2.530463049461398e-07}
0.798 (+/-0.492) for {'C': 449572.77130190533, 'kernel': 'rbf', 'gamma': 2.5398028137728204e-07}
0.798 (+/-0.492) for {'C': 449572.77130190533, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.797 (+/-0.492) for {'C': 449572.77130190533, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.797 (+/-0.492) for {'C': 451232.1133443365, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.797 (+/-0.492) for {'C': 451232.1133443365, 'kernel': 'rbf', 'gamma': 2.475141318074313e-07}
0.797 (+/-0.492) for {'C': 451232.1133443365, 'kernel': 'rbf', 'gamma': 2.4842768936967995e-07}
0.848 (+/-0.459) for {'C': 451232.1133443365, 'kernel': 'rbf', 'gamma': 2.49344618809783e-07}
0.848 (+/-0.460) for {'C': 451232.1133443365, 'kernel': 'rbf', 'gamma': 2.50264932573108e-07}
0.848 (+/-0.460) for {'C': 451232.1133443365, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.798 (+/-0.492) for {'C': 451232.1133443365, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.798 (+/-0.492) for {'C': 451232.1133443365, 'kernel': 'rbf', 'gamma': 2.530463049461398e-07}
0.798 (+/-0.492) for {'C': 451232.1133443365, 'kernel': 'rbf', 'gamma': 2.5398028137728204e-07}
0.798 (+/-0.492) for {'C': 451232.1133443365, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.797 (+/-0.492) for {'C': 451232.1133443365, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.797 (+/-0.492) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.797 (+/-0.492) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.475141318074313e-07}
0.797 (+/-0.492) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.4842768936967995e-07}
0.848 (+/-0.459) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.49344618809783e-07}
0.848 (+/-0.460) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.50264932573108e-07}
0.848 (+/-0.460) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.798 (+/-0.492) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.798 (+/-0.492) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.530463049461398e-07}
0.798 (+/-0.492) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.5398028137728204e-07}
0.798 (+/-0.492) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.797 (+/-0.492) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.655 (+/-0.392) for {'C': 10.0, 'kernel': 'linear'}
0.643 (+/-0.306) for {'C': 100.0, 'kernel': 'linear'}
0.626 (+/-0.318) for {'C': 1000.0, 'kernel': 'linear'}
0.626 (+/-0.318) for {'C': 10000.0, 'kernel': 'linear'}
0.626 (+/-0.318) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.642 (+/-0.236) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.642 (+/-0.236) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.475141318074313e-07}
0.642 (+/-0.236) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.4842768936967995e-07}
0.658 (+/-0.217) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.49344618809783e-07}
0.683 (+/-0.296) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.50264932573108e-07}
0.667 (+/-0.316) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.667 (+/-0.316) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.667 (+/-0.316) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.530463049461398e-07}
0.667 (+/-0.316) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.5398028137728204e-07}
0.667 (+/-0.316) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.642 (+/-0.236) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.642 (+/-0.236) for {'C': 438126.9820224933, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.642 (+/-0.236) for {'C': 438126.9820224933, 'kernel': 'rbf', 'gamma': 2.475141318074313e-07}
0.642 (+/-0.236) for {'C': 438126.9820224933, 'kernel': 'rbf', 'gamma': 2.4842768936967995e-07}
0.658 (+/-0.217) for {'C': 438126.9820224933, 'kernel': 'rbf', 'gamma': 2.49344618809783e-07}
0.683 (+/-0.296) for {'C': 438126.9820224933, 'kernel': 'rbf', 'gamma': 2.50264932573108e-07}
0.667 (+/-0.316) for {'C': 438126.9820224933, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.667 (+/-0.316) for {'C': 438126.9820224933, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.667 (+/-0.316) for {'C': 438126.9820224933, 'kernel': 'rbf', 'gamma': 2.530463049461398e-07}
0.667 (+/-0.316) for {'C': 438126.9820224933, 'kernel': 'rbf', 'gamma': 2.5398028137728204e-07}
0.667 (+/-0.316) for {'C': 438126.9820224933, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.642 (+/-0.236) for {'C': 438126.9820224933, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.642 (+/-0.236) for {'C': 439744.0784473682, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.642 (+/-0.236) for {'C': 439744.0784473682, 'kernel': 'rbf', 'gamma': 2.475141318074313e-07}
0.642 (+/-0.236) for {'C': 439744.0784473682, 'kernel': 'rbf', 'gamma': 2.4842768936967995e-07}
0.658 (+/-0.217) for {'C': 439744.0784473682, 'kernel': 'rbf', 'gamma': 2.49344618809783e-07}
0.683 (+/-0.296) for {'C': 439744.0784473682, 'kernel': 'rbf', 'gamma': 2.50264932573108e-07}
0.683 (+/-0.296) for {'C': 439744.0784473682, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.667 (+/-0.316) for {'C': 439744.0784473682, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.667 (+/-0.316) for {'C': 439744.0784473682, 'kernel': 'rbf', 'gamma': 2.530463049461398e-07}
0.667 (+/-0.316) for {'C': 439744.0784473682, 'kernel': 'rbf', 'gamma': 2.5398028137728204e-07}
0.667 (+/-0.316) for {'C': 439744.0784473682, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.642 (+/-0.236) for {'C': 439744.0784473682, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.642 (+/-0.236) for {'C': 441367.1434634396, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.642 (+/-0.236) for {'C': 441367.1434634396, 'kernel': 'rbf', 'gamma': 2.475141318074313e-07}
0.642 (+/-0.236) for {'C': 441367.1434634396, 'kernel': 'rbf', 'gamma': 2.4842768936967995e-07}
0.658 (+/-0.217) for {'C': 441367.1434634396, 'kernel': 'rbf', 'gamma': 2.49344618809783e-07}
0.683 (+/-0.296) for {'C': 441367.1434634396, 'kernel': 'rbf', 'gamma': 2.50264932573108e-07}
0.683 (+/-0.296) for {'C': 441367.1434634396, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.667 (+/-0.316) for {'C': 441367.1434634396, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.667 (+/-0.316) for {'C': 441367.1434634396, 'kernel': 'rbf', 'gamma': 2.530463049461398e-07}
0.667 (+/-0.316) for {'C': 441367.1434634396, 'kernel': 'rbf', 'gamma': 2.5398028137728204e-07}
0.667 (+/-0.316) for {'C': 441367.1434634396, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.642 (+/-0.236) for {'C': 441367.1434634396, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.642 (+/-0.236) for {'C': 442996.199100363, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.642 (+/-0.236) for {'C': 442996.199100363, 'kernel': 'rbf', 'gamma': 2.475141318074313e-07}
0.642 (+/-0.236) for {'C': 442996.199100363, 'kernel': 'rbf', 'gamma': 2.4842768936967995e-07}
0.658 (+/-0.217) for {'C': 442996.199100363, 'kernel': 'rbf', 'gamma': 2.49344618809783e-07}
0.683 (+/-0.296) for {'C': 442996.199100363, 'kernel': 'rbf', 'gamma': 2.50264932573108e-07}
0.683 (+/-0.296) for {'C': 442996.199100363, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.667 (+/-0.316) for {'C': 442996.199100363, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.667 (+/-0.316) for {'C': 442996.199100363, 'kernel': 'rbf', 'gamma': 2.530463049461398e-07}
0.667 (+/-0.316) for {'C': 442996.199100363, 'kernel': 'rbf', 'gamma': 2.5398028137728204e-07}
0.667 (+/-0.316) for {'C': 442996.199100363, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.642 (+/-0.236) for {'C': 442996.199100363, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.642 (+/-0.236) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.642 (+/-0.236) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.475141318074313e-07}
0.642 (+/-0.236) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.4842768936967995e-07}
0.658 (+/-0.217) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.49344618809783e-07}
0.683 (+/-0.296) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.50264932573108e-07}
0.683 (+/-0.296) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.667 (+/-0.316) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.667 (+/-0.316) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.530463049461398e-07}
0.667 (+/-0.316) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.5398028137728204e-07}
0.667 (+/-0.316) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.642 (+/-0.236) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.642 (+/-0.236) for {'C': 446272.3707622524, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.642 (+/-0.236) for {'C': 446272.3707622524, 'kernel': 'rbf', 'gamma': 2.475141318074313e-07}
0.642 (+/-0.236) for {'C': 446272.3707622524, 'kernel': 'rbf', 'gamma': 2.4842768936967995e-07}
0.658 (+/-0.217) for {'C': 446272.3707622524, 'kernel': 'rbf', 'gamma': 2.49344618809783e-07}
0.683 (+/-0.296) for {'C': 446272.3707622524, 'kernel': 'rbf', 'gamma': 2.50264932573108e-07}
0.667 (+/-0.316) for {'C': 446272.3707622524, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.667 (+/-0.316) for {'C': 446272.3707622524, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.667 (+/-0.316) for {'C': 446272.3707622524, 'kernel': 'rbf', 'gamma': 2.530463049461398e-07}
0.667 (+/-0.316) for {'C': 446272.3707622524, 'kernel': 'rbf', 'gamma': 2.5398028137728204e-07}
0.667 (+/-0.316) for {'C': 446272.3707622524, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.642 (+/-0.236) for {'C': 446272.3707622524, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.642 (+/-0.236) for {'C': 447919.53125428315, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.642 (+/-0.236) for {'C': 447919.53125428315, 'kernel': 'rbf', 'gamma': 2.475141318074313e-07}
0.642 (+/-0.236) for {'C': 447919.53125428315, 'kernel': 'rbf', 'gamma': 2.4842768936967995e-07}
0.658 (+/-0.217) for {'C': 447919.53125428315, 'kernel': 'rbf', 'gamma': 2.49344618809783e-07}
0.683 (+/-0.296) for {'C': 447919.53125428315, 'kernel': 'rbf', 'gamma': 2.50264932573108e-07}
0.683 (+/-0.296) for {'C': 447919.53125428315, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.667 (+/-0.316) for {'C': 447919.53125428315, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.667 (+/-0.316) for {'C': 447919.53125428315, 'kernel': 'rbf', 'gamma': 2.530463049461398e-07}
0.667 (+/-0.316) for {'C': 447919.53125428315, 'kernel': 'rbf', 'gamma': 2.5398028137728204e-07}
0.667 (+/-0.316) for {'C': 447919.53125428315, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.642 (+/-0.236) for {'C': 447919.53125428315, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.642 (+/-0.236) for {'C': 449572.77130190533, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.642 (+/-0.236) for {'C': 449572.77130190533, 'kernel': 'rbf', 'gamma': 2.475141318074313e-07}
0.642 (+/-0.236) for {'C': 449572.77130190533, 'kernel': 'rbf', 'gamma': 2.4842768936967995e-07}
0.658 (+/-0.217) for {'C': 449572.77130190533, 'kernel': 'rbf', 'gamma': 2.49344618809783e-07}
0.683 (+/-0.296) for {'C': 449572.77130190533, 'kernel': 'rbf', 'gamma': 2.50264932573108e-07}
0.683 (+/-0.296) for {'C': 449572.77130190533, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.667 (+/-0.316) for {'C': 449572.77130190533, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.667 (+/-0.316) for {'C': 449572.77130190533, 'kernel': 'rbf', 'gamma': 2.530463049461398e-07}
0.667 (+/-0.316) for {'C': 449572.77130190533, 'kernel': 'rbf', 'gamma': 2.5398028137728204e-07}
0.667 (+/-0.316) for {'C': 449572.77130190533, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.642 (+/-0.236) for {'C': 449572.77130190533, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.642 (+/-0.236) for {'C': 451232.1133443365, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.642 (+/-0.236) for {'C': 451232.1133443365, 'kernel': 'rbf', 'gamma': 2.475141318074313e-07}
0.642 (+/-0.236) for {'C': 451232.1133443365, 'kernel': 'rbf', 'gamma': 2.4842768936967995e-07}
0.658 (+/-0.217) for {'C': 451232.1133443365, 'kernel': 'rbf', 'gamma': 2.49344618809783e-07}
0.683 (+/-0.296) for {'C': 451232.1133443365, 'kernel': 'rbf', 'gamma': 2.50264932573108e-07}
0.683 (+/-0.296) for {'C': 451232.1133443365, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.667 (+/-0.316) for {'C': 451232.1133443365, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.667 (+/-0.316) for {'C': 451232.1133443365, 'kernel': 'rbf', 'gamma': 2.530463049461398e-07}
0.667 (+/-0.316) for {'C': 451232.1133443365, 'kernel': 'rbf', 'gamma': 2.5398028137728204e-07}
0.667 (+/-0.316) for {'C': 451232.1133443365, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.642 (+/-0.236) for {'C': 451232.1133443365, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.642 (+/-0.236) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.642 (+/-0.236) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.475141318074313e-07}
0.642 (+/-0.236) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.4842768936967995e-07}
0.658 (+/-0.217) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.49344618809783e-07}
0.683 (+/-0.296) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.50264932573108e-07}
0.683 (+/-0.296) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.667 (+/-0.316) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.667 (+/-0.316) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.530463049461398e-07}
0.667 (+/-0.316) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.5398028137728204e-07}
0.667 (+/-0.316) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.642 (+/-0.236) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.635 (+/-0.325) for {'C': 10.0, 'kernel': 'linear'}
0.638 (+/-0.239) for {'C': 100.0, 'kernel': 'linear'}
0.612 (+/-0.242) for {'C': 1000.0, 'kernel': 'linear'}
0.613 (+/-0.241) for {'C': 10000.0, 'kernel': 'linear'}
0.613 (+/-0.241) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.50264932573108e-07}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6833333333333332
发现最优参数C为原先的最大/最小值，直接重新设置超参。
第2轮loop中，tunemodel函数得到的最优参数是： ([{'C': array([4.36515832e+00, 4.36515832e+01, 4.36515832e+02, 4.36515832e+03,
       4.36515832e+04, 4.36515832e+05, 4.36515832e+06, 4.36515832e+07,
       4.36515832e+08, 4.36515832e+09, 4.36515832e+10]), 'kernel': ['rbf'], 'gamma': array([2.49344619e-07, 2.49528410e-07, 2.49712337e-07, 2.49896400e-07,
       2.50080598e-07, 2.50264933e-07, 2.50449403e-07, 2.50634008e-07,
       2.50818751e-07, 2.51003629e-07, 2.51188643e-07])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}], {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.50264932573108e-07}, 0.6833333333333332)
这是第2次迭代微调C和gamma。
第2次迭代，得到delta： [8.11543523e+03 9.23710578e-10]
SVC模型clf_op_iter的参数是： {'kernel': 'rbf', 'decision_function_shape': 'ovr', 'class_weight': {-1: 15}, 'tol': 0.001, 'gamma': 2.50264932573108e-07, 'random_state': 0, 'cache_size': 200, 'verbose': False, 'degree': 3, 'C': 436515.83224016585, 'probability': False, 'shrinking': True, 'coef0': 0.0, 'max_iter': -1}

训练模型SVC对训练样本的预测准确率： 0.9362154500354358

测试集中，预测为舞弊样本的有： (array([ 370,  658, 1246, 1247, 1248, 1255, 1256], dtype=int64),) 
测试集中，实际为舞弊样本的有： (array([1246, 1247, 1248, 1249, 1250, 1251, 1252, 1253, 1254, 1255, 1256],
      dtype=int64),) 
预测的舞弊样本数目： 7

训练模型SVC对测试样本的预测准确率： 0.9347980155917789
以上是第26次特征筛选。
第26次特征筛选，AUC值是： 0.7264701590544287
X_train_iter_svc.shape is: (1257, 26)
X_test_iter_svc.shape is: (1257, 26)
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.635 (+/-0.326) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.748 (+/-0.449) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.20      0.17      0.18         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.99      0.99       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.614 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.666 (+/-0.316) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.20      0.17      0.18         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.99      0.99       629

本轮grid search结果，得到最好的参数选择是： {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6663461538461538
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.622 (+/-0.319) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.20      0.17      0.18         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.99      0.99       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.613 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.20      0.17      0.18         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.99      0.99       629

本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6663461538461538
RBF的粗grid search最佳超参： {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001} RBF的粗grid search最高分值： 0.6663461538461538
linear的粗grid search最佳超参： {'C': 1.0, 'kernel': 'linear'} linear的粗grid search最高分值： 0.6663461538461538
粗grid search得到的parameter是：
 [{'C': array([1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03, 1.e+04, 1.e+05,
       1.e+06, 1.e+07, 1.e+08]), 'kernel': ['rbf'], 'gamma': array([1.e-08, 1.e-07, 1.e-06, 1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01,
       1.e+00, 1.e+01, 1.e+02])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.449) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.664 (+/-0.388) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.631 (+/-0.327) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.748 (+/-0.449) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.375) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.635 (+/-0.326) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.748 (+/-0.449) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.706 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.656 (+/-0.312) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.635 (+/-0.326) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.747 (+/-0.502) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.747 (+/-0.449) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.706 (+/-0.383) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.656 (+/-0.312) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.635 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.747 (+/-0.502) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.798 (+/-0.437) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.685 (+/-0.376) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.656 (+/-0.312) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.614 (+/-0.327) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.635 (+/-0.326) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.649 (+/-0.778) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.797 (+/-0.492) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.676 (+/-0.294) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.631 (+/-0.310) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.637 (+/-0.307) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.614 (+/-0.327) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.635 (+/-0.326) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.699 (+/-0.797) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.797 (+/-0.492) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.648 (+/-0.306) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.657 (+/-0.303) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.624 (+/-0.319) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.614 (+/-0.327) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.635 (+/-0.326) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.706 (+/-0.383) for {'C': 10.0, 'kernel': 'linear'}
0.641 (+/-0.312) for {'C': 100.0, 'kernel': 'linear'}
0.622 (+/-0.319) for {'C': 1000.0, 'kernel': 'linear'}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'linear'}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.641 (+/-0.236) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.666 (+/-0.316) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.641 (+/-0.236) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.232) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.316) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.666 (+/-0.316) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.639 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.617 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.641 (+/-0.236) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.316) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.588 (+/-0.233) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.617 (+/-0.238) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.683 (+/-0.296) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.665 (+/-0.315) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.318) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.588 (+/-0.232) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.587 (+/-0.234) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.238) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.617 (+/-0.238) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.642 (+/-0.236) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.681 (+/-0.295) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.661 (+/-0.315) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.638 (+/-0.238) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.587 (+/-0.233) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.587 (+/-0.234) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.238) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.642 (+/-0.236) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.642 (+/-0.236) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.664 (+/-0.315) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.687 (+/-0.298) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.612 (+/-0.242) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.587 (+/-0.233) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.587 (+/-0.234) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.238) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.666 (+/-0.315) for {'C': 10.0, 'kernel': 'linear'}
0.663 (+/-0.316) for {'C': 100.0, 'kernel': 'linear'}
0.613 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.588 (+/-0.233) for {'C': 10000.0, 'kernel': 'linear'}
0.588 (+/-0.233) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.08      0.17      0.11         6
          1       0.99      0.98      0.99       623

avg / total       0.98      0.97      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6868579437711271
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.698 (+/-0.383) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.629 (+/-0.329) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.714 (+/-0.359) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.644 (+/-0.389) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.631 (+/-0.327) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.723 (+/-0.358) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.661 (+/-0.384) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.635 (+/-0.326) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.723 (+/-0.358) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.660 (+/-0.385) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.656 (+/-0.312) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.635 (+/-0.326) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.747 (+/-0.502) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.797 (+/-0.492) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.702 (+/-0.355) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.660 (+/-0.385) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.656 (+/-0.312) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.635 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.024) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.797 (+/-0.492) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.654 (+/-0.292) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.659 (+/-0.386) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.656 (+/-0.312) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.614 (+/-0.327) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.635 (+/-0.326) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.699 (+/-0.797) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.681 (+/-0.372) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.642 (+/-0.302) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.629 (+/-0.311) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.637 (+/-0.307) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.614 (+/-0.327) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.635 (+/-0.326) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.598 (+/-0.745) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.656 (+/-0.367) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.615 (+/-0.307) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.657 (+/-0.303) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.624 (+/-0.319) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.614 (+/-0.327) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.635 (+/-0.326) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.673 (+/-0.377) for {'C': 10.0, 'kernel': 'linear'}
0.637 (+/-0.310) for {'C': 100.0, 'kernel': 'linear'}
0.622 (+/-0.319) for {'C': 1000.0, 'kernel': 'linear'}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'linear'}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.666 (+/-0.315) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.682 (+/-0.295) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.611 (+/-0.242) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.682 (+/-0.295) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.661 (+/-0.314) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.232) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.682 (+/-0.295) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.660 (+/-0.314) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.639 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.617 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.642 (+/-0.236) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.682 (+/-0.294) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.660 (+/-0.314) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.588 (+/-0.233) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.523 (+/-0.138) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.642 (+/-0.236) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.680 (+/-0.290) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.659 (+/-0.314) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.318) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.588 (+/-0.232) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.587 (+/-0.234) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.238) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.642 (+/-0.236) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.641 (+/-0.235) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.676 (+/-0.293) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.661 (+/-0.314) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.638 (+/-0.238) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.587 (+/-0.233) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.587 (+/-0.234) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.238) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.561 (+/-0.310) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.640 (+/-0.233) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.657 (+/-0.311) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.687 (+/-0.298) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.612 (+/-0.242) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.587 (+/-0.233) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.587 (+/-0.234) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.238) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.663 (+/-0.316) for {'C': 10.0, 'kernel': 'linear'}
0.662 (+/-0.315) for {'C': 100.0, 'kernel': 'linear'}
0.613 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.588 (+/-0.233) for {'C': 10000.0, 'kernel': 'linear'}
0.588 (+/-0.233) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.07      0.17      0.10         6
          1       0.99      0.98      0.99       623

avg / total       0.98      0.97      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6868579437711271
发现最优参数C为原先的最大/最小值，直接重新设置超参。
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.772 (+/-0.473) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.748 (+/-0.449) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.747 (+/-0.502) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.747 (+/-0.502) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.747 (+/-0.502) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.747 (+/-0.502) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.772 (+/-0.473) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.747 (+/-0.449) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.723 (+/-0.423) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.723 (+/-0.423) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.698 (+/-0.383) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.706 (+/-0.383) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.706 (+/-0.383) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.798 (+/-0.437) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.722 (+/-0.417) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.689 (+/-0.382) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.698 (+/-0.383) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.689 (+/-0.375) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.685 (+/-0.376) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.683 (+/-0.378) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.644 (+/-0.319) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.638 (+/-0.310) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.644 (+/-0.309) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.656 (+/-0.312) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.676 (+/-0.294) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.697 (+/-0.438) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.660 (+/-0.327) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.629 (+/-0.313) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.630 (+/-0.312) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.631 (+/-0.310) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.644 (+/-0.298) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.664 (+/-0.298) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.624 (+/-0.318) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.639 (+/-0.309) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.637 (+/-0.307) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.648 (+/-0.306) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.673 (+/-0.377) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.649 (+/-0.323) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.626 (+/-0.314) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.622 (+/-0.319) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.657 (+/-0.303) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.634 (+/-0.310) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.639 (+/-0.327) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.649 (+/-0.314) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.638 (+/-0.309) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.624 (+/-0.319) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.648 (+/-0.306) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.665 (+/-0.376) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.649 (+/-0.323) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.626 (+/-0.314) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.623 (+/-0.319) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.659 (+/-0.301) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.632 (+/-0.310) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.647 (+/-0.341) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.649 (+/-0.314) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.646 (+/-0.317) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.624 (+/-0.319) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.648 (+/-0.306) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.665 (+/-0.376) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.649 (+/-0.323) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.626 (+/-0.314) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.623 (+/-0.319) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.659 (+/-0.301) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.632 (+/-0.310) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.647 (+/-0.341) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.649 (+/-0.314) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.646 (+/-0.317) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.624 (+/-0.319) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.648 (+/-0.306) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.665 (+/-0.376) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.649 (+/-0.323) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.626 (+/-0.314) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.623 (+/-0.319) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.659 (+/-0.301) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.632 (+/-0.310) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.647 (+/-0.341) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.649 (+/-0.314) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.646 (+/-0.317) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.624 (+/-0.319) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.648 (+/-0.306) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.665 (+/-0.376) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.649 (+/-0.323) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.626 (+/-0.314) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.623 (+/-0.319) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.659 (+/-0.301) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.632 (+/-0.310) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.647 (+/-0.341) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.649 (+/-0.314) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.646 (+/-0.317) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.624 (+/-0.319) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.648 (+/-0.306) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.665 (+/-0.376) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.649 (+/-0.323) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.626 (+/-0.314) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.623 (+/-0.319) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.659 (+/-0.301) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.632 (+/-0.310) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.647 (+/-0.341) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.649 (+/-0.314) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.646 (+/-0.317) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.624 (+/-0.319) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.706 (+/-0.383) for {'C': 10.0, 'kernel': 'linear'}
0.641 (+/-0.312) for {'C': 100.0, 'kernel': 'linear'}
0.622 (+/-0.319) for {'C': 1000.0, 'kernel': 'linear'}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'linear'}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.642 (+/-0.236) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.666 (+/-0.316) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.617 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.617 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.617 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.617 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.642 (+/-0.236) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.641 (+/-0.236) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.666 (+/-0.316) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.683 (+/-0.296) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.641 (+/-0.235) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.666 (+/-0.315) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.666 (+/-0.316) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.666 (+/-0.316) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.665 (+/-0.315) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.664 (+/-0.316) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.662 (+/-0.317) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.662 (+/-0.316) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.663 (+/-0.317) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.664 (+/-0.318) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.681 (+/-0.295) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.639 (+/-0.329) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.665 (+/-0.316) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.660 (+/-0.316) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.660 (+/-0.315) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.661 (+/-0.315) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.687 (+/-0.298) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.688 (+/-0.298) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.614 (+/-0.238) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.638 (+/-0.237) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.638 (+/-0.238) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.664 (+/-0.315) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.662 (+/-0.320) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.664 (+/-0.317) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.658 (+/-0.315) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.634 (+/-0.327) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.687 (+/-0.298) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.662 (+/-0.316) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.638 (+/-0.327) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.639 (+/-0.238) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.638 (+/-0.236) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.612 (+/-0.242) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.664 (+/-0.315) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.661 (+/-0.320) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.663 (+/-0.317) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.658 (+/-0.315) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.635 (+/-0.326) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.687 (+/-0.297) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.662 (+/-0.315) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.639 (+/-0.328) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.639 (+/-0.238) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.663 (+/-0.316) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.612 (+/-0.242) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.664 (+/-0.315) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.661 (+/-0.320) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.663 (+/-0.317) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.658 (+/-0.315) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.635 (+/-0.326) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.687 (+/-0.297) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.662 (+/-0.315) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.639 (+/-0.328) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.639 (+/-0.238) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.663 (+/-0.316) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.612 (+/-0.242) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.664 (+/-0.315) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.661 (+/-0.320) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.663 (+/-0.317) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.658 (+/-0.315) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.635 (+/-0.326) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.687 (+/-0.297) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.662 (+/-0.315) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.639 (+/-0.328) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.639 (+/-0.238) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.663 (+/-0.316) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.612 (+/-0.242) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.664 (+/-0.315) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.661 (+/-0.320) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.663 (+/-0.317) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.658 (+/-0.315) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.635 (+/-0.326) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.687 (+/-0.297) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.662 (+/-0.315) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.639 (+/-0.328) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.639 (+/-0.238) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.663 (+/-0.316) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.612 (+/-0.242) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.664 (+/-0.315) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.661 (+/-0.320) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.664 (+/-0.317) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.658 (+/-0.315) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.635 (+/-0.326) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.687 (+/-0.297) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.662 (+/-0.315) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.639 (+/-0.328) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.639 (+/-0.238) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.663 (+/-0.316) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.612 (+/-0.242) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.666 (+/-0.315) for {'C': 10.0, 'kernel': 'linear'}
0.663 (+/-0.316) for {'C': 100.0, 'kernel': 'linear'}
0.613 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.588 (+/-0.233) for {'C': 10000.0, 'kernel': 'linear'}
0.588 (+/-0.233) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.11      0.17      0.13         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6879802539368455
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.797 (+/-0.492) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.748 (+/-0.449) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.698 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.723 (+/-0.358) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.797 (+/-0.492) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.797 (+/-0.492) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.797 (+/-0.492) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.748 (+/-0.449) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.689 (+/-0.382) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.702 (+/-0.355) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.673 (+/-0.330) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.676 (+/-0.370) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.669 (+/-0.373) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.665 (+/-0.381) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.660 (+/-0.385) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.654 (+/-0.292) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.673 (+/-0.330) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.656 (+/-0.312) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.659 (+/-0.375) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.627 (+/-0.314) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.659 (+/-0.386) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.660 (+/-0.385) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.632 (+/-0.311) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.634 (+/-0.309) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.644 (+/-0.309) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.656 (+/-0.312) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.642 (+/-0.302) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.656 (+/-0.390) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.622 (+/-0.313) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.624 (+/-0.316) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.630 (+/-0.312) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.629 (+/-0.311) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.644 (+/-0.298) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.664 (+/-0.298) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.624 (+/-0.318) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.639 (+/-0.309) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.637 (+/-0.307) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.615 (+/-0.307) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.646 (+/-0.387) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.615 (+/-0.307) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.624 (+/-0.315) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.622 (+/-0.319) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.657 (+/-0.303) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.634 (+/-0.310) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.639 (+/-0.327) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.649 (+/-0.314) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.638 (+/-0.309) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.624 (+/-0.319) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.615 (+/-0.307) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.646 (+/-0.387) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.615 (+/-0.307) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.624 (+/-0.315) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.623 (+/-0.319) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.659 (+/-0.301) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.632 (+/-0.310) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.647 (+/-0.341) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.649 (+/-0.314) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.646 (+/-0.317) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.624 (+/-0.319) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.615 (+/-0.307) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.646 (+/-0.387) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.615 (+/-0.307) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.624 (+/-0.315) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.623 (+/-0.319) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.659 (+/-0.301) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.632 (+/-0.310) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.647 (+/-0.341) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.649 (+/-0.314) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.646 (+/-0.317) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.624 (+/-0.319) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.615 (+/-0.307) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.646 (+/-0.387) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.615 (+/-0.307) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.624 (+/-0.315) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.623 (+/-0.319) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.659 (+/-0.301) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.632 (+/-0.310) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.647 (+/-0.341) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.649 (+/-0.314) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.646 (+/-0.317) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.624 (+/-0.319) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.615 (+/-0.307) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.646 (+/-0.387) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.615 (+/-0.307) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.624 (+/-0.315) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.623 (+/-0.319) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.659 (+/-0.301) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.632 (+/-0.310) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.647 (+/-0.341) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.649 (+/-0.314) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.646 (+/-0.317) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.624 (+/-0.319) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.615 (+/-0.307) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.646 (+/-0.387) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.615 (+/-0.307) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.624 (+/-0.315) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.623 (+/-0.319) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.659 (+/-0.301) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.632 (+/-0.310) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.647 (+/-0.341) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.649 (+/-0.314) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.646 (+/-0.317) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.624 (+/-0.319) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.673 (+/-0.377) for {'C': 10.0, 'kernel': 'linear'}
0.637 (+/-0.310) for {'C': 100.0, 'kernel': 'linear'}
0.622 (+/-0.319) for {'C': 1000.0, 'kernel': 'linear'}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'linear'}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.642 (+/-0.236) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.666 (+/-0.316) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.666 (+/-0.315) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.682 (+/-0.295) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.642 (+/-0.236) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.642 (+/-0.236) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.642 (+/-0.236) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.666 (+/-0.316) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.682 (+/-0.294) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.665 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.665 (+/-0.314) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.664 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.662 (+/-0.316) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.660 (+/-0.314) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.680 (+/-0.290) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.665 (+/-0.315) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.665 (+/-0.315) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.662 (+/-0.315) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.659 (+/-0.314) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.659 (+/-0.314) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.660 (+/-0.315) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.661 (+/-0.315) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.662 (+/-0.315) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.663 (+/-0.317) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.664 (+/-0.318) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.676 (+/-0.293) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.654 (+/-0.316) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.657 (+/-0.316) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.656 (+/-0.315) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.660 (+/-0.316) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.661 (+/-0.314) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.687 (+/-0.298) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.688 (+/-0.298) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.614 (+/-0.238) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.638 (+/-0.237) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.638 (+/-0.238) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.657 (+/-0.311) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.652 (+/-0.314) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.655 (+/-0.317) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.657 (+/-0.315) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.634 (+/-0.327) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.687 (+/-0.298) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.662 (+/-0.316) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.638 (+/-0.327) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.639 (+/-0.238) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.638 (+/-0.236) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.612 (+/-0.242) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.657 (+/-0.312) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.652 (+/-0.313) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.654 (+/-0.318) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.657 (+/-0.314) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.635 (+/-0.326) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.687 (+/-0.297) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.662 (+/-0.315) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.639 (+/-0.328) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.639 (+/-0.238) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.663 (+/-0.316) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.612 (+/-0.242) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.657 (+/-0.312) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.652 (+/-0.314) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.654 (+/-0.318) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.657 (+/-0.314) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.635 (+/-0.326) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.687 (+/-0.297) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.662 (+/-0.315) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.639 (+/-0.328) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.639 (+/-0.238) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.663 (+/-0.316) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.612 (+/-0.242) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.657 (+/-0.312) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.652 (+/-0.314) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.654 (+/-0.318) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.657 (+/-0.314) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.635 (+/-0.326) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.687 (+/-0.297) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.662 (+/-0.315) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.639 (+/-0.328) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.639 (+/-0.238) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.663 (+/-0.316) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.612 (+/-0.242) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.657 (+/-0.312) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.652 (+/-0.314) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.654 (+/-0.318) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.657 (+/-0.314) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.635 (+/-0.326) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.687 (+/-0.297) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.662 (+/-0.315) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.639 (+/-0.328) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.639 (+/-0.238) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.663 (+/-0.316) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.612 (+/-0.242) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.657 (+/-0.312) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.652 (+/-0.314) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.654 (+/-0.318) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.657 (+/-0.314) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.635 (+/-0.326) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.687 (+/-0.297) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.662 (+/-0.315) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.639 (+/-0.328) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.639 (+/-0.238) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.663 (+/-0.316) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.612 (+/-0.242) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.663 (+/-0.316) for {'C': 10.0, 'kernel': 'linear'}
0.662 (+/-0.315) for {'C': 100.0, 'kernel': 'linear'}
0.613 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.588 (+/-0.233) for {'C': 10000.0, 'kernel': 'linear'}
0.588 (+/-0.233) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.11      0.17      0.13         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6879802539368455
循环迭代之前，delta is: [9.00000000e+07 1.51188643e-05]
执行tunemodel()函数前，使用的grid search parameter是：
 [{'C': array([ 1000000.        ,  1584893.19246111,  2511886.43150958,
        3981071.70553497,  6309573.44480193,  9999999.99999999,
       15848931.92461113, 25118864.31509581, 39810717.05534975,
       63095734.44801924, 99999999.99999991]), 'kernel': ['rbf'], 'gamma': array([1.58489319e-05, 1.73780083e-05, 1.90546072e-05, 2.08929613e-05,
       2.29086765e-05, 2.51188643e-05, 2.75422870e-05, 3.01995172e-05,
       3.31131121e-05, 3.63078055e-05, 3.98107171e-05])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.683 (+/-0.378) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.691 (+/-0.387) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.7378008287493767e-05}
0.658 (+/-0.329) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.905460717963247e-05}
0.660 (+/-0.327) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.0892961308540406e-05}
0.658 (+/-0.329) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.644 (+/-0.319) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.654 (+/-0.332) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.670 (+/-0.381) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.0199517204020175e-05}
0.646 (+/-0.317) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.3113112148259144e-05}
0.646 (+/-0.317) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.630780547701015e-05}
0.638 (+/-0.310) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.656 (+/-0.330) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.654 (+/-0.332) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.7378008287493767e-05}
0.643 (+/-0.320) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.905460717963247e-05}
0.646 (+/-0.317) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.0892961308540406e-05}
0.649 (+/-0.314) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.634 (+/-0.311) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.639 (+/-0.314) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.636 (+/-0.309) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.0199517204020175e-05}
0.638 (+/-0.310) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.3113112148259144e-05}
0.643 (+/-0.306) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.630780547701015e-05}
0.639 (+/-0.307) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.639 (+/-0.309) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.644 (+/-0.309) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.7378008287493767e-05}
0.635 (+/-0.312) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.905460717963247e-05}
0.633 (+/-0.309) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.0892961308540406e-05}
0.641 (+/-0.307) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.634 (+/-0.311) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.637 (+/-0.310) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.649 (+/-0.314) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.0199517204020175e-05}
0.636 (+/-0.309) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.3113112148259144e-05}
0.649 (+/-0.314) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.630780547701015e-05}
0.641 (+/-0.307) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.637 (+/-0.308) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.641 (+/-0.311) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.7378008287493767e-05}
0.634 (+/-0.308) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.905460717963247e-05}
0.631 (+/-0.310) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.0892961308540406e-05}
0.635 (+/-0.308) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.634 (+/-0.311) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.646 (+/-0.317) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.674 (+/-0.301) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.0199517204020175e-05}
0.639 (+/-0.309) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.3113112148259144e-05}
0.648 (+/-0.316) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.630780547701015e-05}
0.641 (+/-0.307) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.641 (+/-0.298) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.643 (+/-0.310) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.7378008287493767e-05}
0.633 (+/-0.309) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.905460717963247e-05}
0.634 (+/-0.308) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.0892961308540406e-05}
0.637 (+/-0.307) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.641 (+/-0.307) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.646 (+/-0.316) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.674 (+/-0.301) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.0199517204020175e-05}
0.662 (+/-0.301) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.3113112148259144e-05}
0.641 (+/-0.307) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.630780547701015e-05}
0.639 (+/-0.309) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.644 (+/-0.298) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.648 (+/-0.316) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.7378008287493767e-05}
0.633 (+/-0.309) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.905460717963247e-05}
0.625 (+/-0.318) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.0892961308540406e-05}
0.626 (+/-0.318) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.664 (+/-0.298) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.639 (+/-0.309) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.652 (+/-0.313) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.0199517204020175e-05}
0.663 (+/-0.300) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.3113112148259144e-05}
0.649 (+/-0.314) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.630780547701015e-05}
0.624 (+/-0.318) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.635 (+/-0.308) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.649 (+/-0.314) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.7378008287493767e-05}
0.637 (+/-0.307) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.905460717963247e-05}
0.628 (+/-0.318) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.0892961308540406e-05}
0.624 (+/-0.319) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.669 (+/-0.298) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.664 (+/-0.299) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.652 (+/-0.313) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 3.0199517204020175e-05}
0.645 (+/-0.317) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 3.3113112148259144e-05}
0.639 (+/-0.309) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 3.630780547701015e-05}
0.624 (+/-0.318) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.635 (+/-0.308) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.660 (+/-0.327) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.7378008287493767e-05}
0.627 (+/-0.318) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.905460717963247e-05}
0.643 (+/-0.306) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.0892961308540406e-05}
0.631 (+/-0.319) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.669 (+/-0.298) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.660 (+/-0.327) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.656 (+/-0.312) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 3.0199517204020175e-05}
0.618 (+/-0.322) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 3.3113112148259144e-05}
0.647 (+/-0.315) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 3.630780547701015e-05}
0.641 (+/-0.307) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.634 (+/-0.310) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.681 (+/-0.373) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.7378008287493767e-05}
0.622 (+/-0.319) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.905460717963247e-05}
0.656 (+/-0.312) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 2.0892961308540406e-05}
0.624 (+/-0.318) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.634 (+/-0.321) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.643 (+/-0.306) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.647 (+/-0.308) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 3.0199517204020175e-05}
0.618 (+/-0.322) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 3.3113112148259144e-05}
0.639 (+/-0.309) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 3.630780547701015e-05}
0.641 (+/-0.307) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.635 (+/-0.310) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.681 (+/-0.373) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.7378008287493767e-05}
0.622 (+/-0.319) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.905460717963247e-05}
0.631 (+/-0.319) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 2.0892961308540406e-05}
0.622 (+/-0.319) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.634 (+/-0.321) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.643 (+/-0.306) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.641 (+/-0.307) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 3.0199517204020175e-05}
0.618 (+/-0.322) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 3.3113112148259144e-05}
0.639 (+/-0.309) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 3.630780547701015e-05}
0.641 (+/-0.307) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.634 (+/-0.310) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.681 (+/-0.373) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.7378008287493767e-05}
0.624 (+/-0.319) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.905460717963247e-05}
0.626 (+/-0.318) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 2.0892961308540406e-05}
0.624 (+/-0.318) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.639 (+/-0.327) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.643 (+/-0.306) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.641 (+/-0.307) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.0199517204020175e-05}
0.618 (+/-0.322) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.3113112148259144e-05}
0.639 (+/-0.309) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.630780547701015e-05}
0.649 (+/-0.314) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.706 (+/-0.383) for {'C': 10.0, 'kernel': 'linear'}
0.641 (+/-0.312) for {'C': 100.0, 'kernel': 'linear'}
0.622 (+/-0.319) for {'C': 1000.0, 'kernel': 'linear'}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'linear'}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.664 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.664 (+/-0.317) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.7378008287493767e-05}
0.664 (+/-0.317) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.905460717963247e-05}
0.664 (+/-0.317) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.0892961308540406e-05}
0.663 (+/-0.318) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.662 (+/-0.317) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.663 (+/-0.318) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.663 (+/-0.319) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.0199517204020175e-05}
0.663 (+/-0.317) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.3113112148259144e-05}
0.663 (+/-0.318) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.630780547701015e-05}
0.662 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.663 (+/-0.318) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.663 (+/-0.318) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.7378008287493767e-05}
0.662 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.905460717963247e-05}
0.663 (+/-0.317) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.0892961308540406e-05}
0.663 (+/-0.318) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.662 (+/-0.315) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.662 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.662 (+/-0.315) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.0199517204020175e-05}
0.663 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.3113112148259144e-05}
0.663 (+/-0.317) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.630780547701015e-05}
0.663 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.662 (+/-0.317) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.663 (+/-0.316) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.7378008287493767e-05}
0.662 (+/-0.316) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.905460717963247e-05}
0.662 (+/-0.314) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.0892961308540406e-05}
0.638 (+/-0.238) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.662 (+/-0.315) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.662 (+/-0.316) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.663 (+/-0.317) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.0199517204020175e-05}
0.663 (+/-0.315) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.3113112148259144e-05}
0.663 (+/-0.318) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.630780547701015e-05}
0.663 (+/-0.315) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.662 (+/-0.316) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.663 (+/-0.316) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.7378008287493767e-05}
0.662 (+/-0.315) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.905460717963247e-05}
0.662 (+/-0.314) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.0892961308540406e-05}
0.638 (+/-0.237) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.662 (+/-0.315) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.663 (+/-0.316) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.688 (+/-0.300) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.0199517204020175e-05}
0.663 (+/-0.315) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.3113112148259144e-05}
0.663 (+/-0.317) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.630780547701015e-05}
0.663 (+/-0.315) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.686 (+/-0.296) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.663 (+/-0.316) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.7378008287493767e-05}
0.662 (+/-0.315) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.905460717963247e-05}
0.662 (+/-0.314) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.0892961308540406e-05}
0.638 (+/-0.237) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.663 (+/-0.316) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.638 (+/-0.237) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.688 (+/-0.300) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.0199517204020175e-05}
0.663 (+/-0.224) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.3113112148259144e-05}
0.638 (+/-0.237) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.630780547701015e-05}
0.639 (+/-0.236) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.687 (+/-0.298) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.663 (+/-0.317) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.7378008287493767e-05}
0.662 (+/-0.314) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.905460717963247e-05}
0.637 (+/-0.324) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.0892961308540406e-05}
0.613 (+/-0.240) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.688 (+/-0.298) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.638 (+/-0.237) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.663 (+/-0.317) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.0199517204020175e-05}
0.664 (+/-0.223) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.3113112148259144e-05}
0.639 (+/-0.237) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.630780547701015e-05}
0.614 (+/-0.238) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.662 (+/-0.316) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.663 (+/-0.317) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.7378008287493767e-05}
0.662 (+/-0.316) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.905460717963247e-05}
0.637 (+/-0.326) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.0892961308540406e-05}
0.613 (+/-0.241) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.689 (+/-0.298) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.664 (+/-0.224) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.664 (+/-0.317) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 3.0199517204020175e-05}
0.639 (+/-0.236) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 3.3113112148259144e-05}
0.639 (+/-0.236) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 3.630780547701015e-05}
0.614 (+/-0.239) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.661 (+/-0.317) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.664 (+/-0.318) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.7378008287493767e-05}
0.637 (+/-0.326) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.905460717963247e-05}
0.638 (+/-0.240) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.0892961308540406e-05}
0.613 (+/-0.241) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.688 (+/-0.300) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.664 (+/-0.317) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.664 (+/-0.317) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 3.0199517204020175e-05}
0.613 (+/-0.236) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 3.3113112148259144e-05}
0.639 (+/-0.236) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 3.630780547701015e-05}
0.638 (+/-0.237) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.661 (+/-0.316) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.664 (+/-0.319) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.7378008287493767e-05}
0.612 (+/-0.241) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.905460717963247e-05}
0.637 (+/-0.242) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 2.0892961308540406e-05}
0.613 (+/-0.240) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.638 (+/-0.326) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.639 (+/-0.237) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.664 (+/-0.316) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 3.0199517204020175e-05}
0.612 (+/-0.236) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 3.3113112148259144e-05}
0.639 (+/-0.236) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 3.630780547701015e-05}
0.638 (+/-0.237) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.661 (+/-0.317) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.664 (+/-0.319) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.7378008287493767e-05}
0.612 (+/-0.241) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.905460717963247e-05}
0.612 (+/-0.243) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 2.0892961308540406e-05}
0.613 (+/-0.239) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.638 (+/-0.326) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.639 (+/-0.236) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.663 (+/-0.315) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 3.0199517204020175e-05}
0.612 (+/-0.236) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 3.3113112148259144e-05}
0.638 (+/-0.237) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 3.630780547701015e-05}
0.638 (+/-0.237) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.662 (+/-0.316) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.664 (+/-0.319) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.7378008287493767e-05}
0.612 (+/-0.241) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.905460717963247e-05}
0.612 (+/-0.242) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 2.0892961308540406e-05}
0.613 (+/-0.240) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.638 (+/-0.327) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.639 (+/-0.236) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.663 (+/-0.315) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.0199517204020175e-05}
0.612 (+/-0.236) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.3113112148259144e-05}
0.638 (+/-0.237) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.630780547701015e-05}
0.639 (+/-0.238) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.666 (+/-0.315) for {'C': 10.0, 'kernel': 'linear'}
0.663 (+/-0.316) for {'C': 100.0, 'kernel': 'linear'}
0.613 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.588 (+/-0.233) for {'C': 10000.0, 'kernel': 'linear'}
0.588 (+/-0.233) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.11      0.17      0.13         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6886212795778712
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.660 (+/-0.385) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.665 (+/-0.382) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.7378008287493767e-05}
0.626 (+/-0.314) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.905460717963247e-05}
0.627 (+/-0.312) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.0892961308540406e-05}
0.633 (+/-0.309) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.632 (+/-0.311) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.631 (+/-0.311) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.632 (+/-0.310) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.0199517204020175e-05}
0.641 (+/-0.311) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.3113112148259144e-05}
0.638 (+/-0.310) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.630780547701015e-05}
0.634 (+/-0.309) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.634 (+/-0.308) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.635 (+/-0.310) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.7378008287493767e-05}
0.626 (+/-0.314) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.905460717963247e-05}
0.631 (+/-0.310) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.0892961308540406e-05}
0.639 (+/-0.307) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.632 (+/-0.311) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.630 (+/-0.312) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.636 (+/-0.309) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.0199517204020175e-05}
0.638 (+/-0.310) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.3113112148259144e-05}
0.643 (+/-0.306) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.630780547701015e-05}
0.639 (+/-0.307) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.637 (+/-0.308) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.641 (+/-0.307) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.7378008287493767e-05}
0.629 (+/-0.312) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.905460717963247e-05}
0.633 (+/-0.309) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.0892961308540406e-05}
0.637 (+/-0.307) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.634 (+/-0.311) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.637 (+/-0.310) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.649 (+/-0.314) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.0199517204020175e-05}
0.636 (+/-0.309) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.3113112148259144e-05}
0.649 (+/-0.314) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.630780547701015e-05}
0.641 (+/-0.307) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.637 (+/-0.308) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.641 (+/-0.311) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.7378008287493767e-05}
0.634 (+/-0.308) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.905460717963247e-05}
0.631 (+/-0.310) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.0892961308540406e-05}
0.635 (+/-0.308) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.634 (+/-0.311) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.646 (+/-0.317) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.674 (+/-0.301) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.0199517204020175e-05}
0.639 (+/-0.309) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.3113112148259144e-05}
0.648 (+/-0.316) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.630780547701015e-05}
0.641 (+/-0.307) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.641 (+/-0.298) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.643 (+/-0.310) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.7378008287493767e-05}
0.633 (+/-0.309) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.905460717963247e-05}
0.634 (+/-0.308) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.0892961308540406e-05}
0.637 (+/-0.307) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.641 (+/-0.307) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.646 (+/-0.316) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.674 (+/-0.301) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.0199517204020175e-05}
0.662 (+/-0.301) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.3113112148259144e-05}
0.641 (+/-0.307) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.630780547701015e-05}
0.639 (+/-0.309) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.644 (+/-0.298) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.648 (+/-0.316) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.7378008287493767e-05}
0.633 (+/-0.309) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.905460717963247e-05}
0.625 (+/-0.318) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.0892961308540406e-05}
0.626 (+/-0.318) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.664 (+/-0.298) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.639 (+/-0.309) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.652 (+/-0.313) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.0199517204020175e-05}
0.663 (+/-0.300) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.3113112148259144e-05}
0.649 (+/-0.314) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.630780547701015e-05}
0.624 (+/-0.318) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.635 (+/-0.308) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.649 (+/-0.314) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.7378008287493767e-05}
0.637 (+/-0.307) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.905460717963247e-05}
0.628 (+/-0.318) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.0892961308540406e-05}
0.624 (+/-0.319) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.669 (+/-0.298) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.664 (+/-0.299) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.652 (+/-0.313) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 3.0199517204020175e-05}
0.645 (+/-0.317) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 3.3113112148259144e-05}
0.639 (+/-0.309) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 3.630780547701015e-05}
0.624 (+/-0.318) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.635 (+/-0.308) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.660 (+/-0.327) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.7378008287493767e-05}
0.627 (+/-0.318) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.905460717963247e-05}
0.643 (+/-0.306) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.0892961308540406e-05}
0.631 (+/-0.319) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.669 (+/-0.298) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.660 (+/-0.327) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.656 (+/-0.312) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 3.0199517204020175e-05}
0.618 (+/-0.322) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 3.3113112148259144e-05}
0.647 (+/-0.315) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 3.630780547701015e-05}
0.641 (+/-0.307) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.634 (+/-0.310) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.681 (+/-0.373) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.7378008287493767e-05}
0.622 (+/-0.319) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.905460717963247e-05}
0.656 (+/-0.312) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 2.0892961308540406e-05}
0.624 (+/-0.318) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.634 (+/-0.321) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.643 (+/-0.306) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.647 (+/-0.308) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 3.0199517204020175e-05}
0.618 (+/-0.322) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 3.3113112148259144e-05}
0.639 (+/-0.309) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 3.630780547701015e-05}
0.641 (+/-0.307) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.635 (+/-0.310) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.681 (+/-0.373) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.7378008287493767e-05}
0.622 (+/-0.319) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.905460717963247e-05}
0.631 (+/-0.319) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 2.0892961308540406e-05}
0.622 (+/-0.319) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.634 (+/-0.321) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.643 (+/-0.306) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.641 (+/-0.307) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 3.0199517204020175e-05}
0.618 (+/-0.322) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 3.3113112148259144e-05}
0.639 (+/-0.309) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 3.630780547701015e-05}
0.641 (+/-0.307) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.634 (+/-0.310) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.681 (+/-0.373) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.7378008287493767e-05}
0.624 (+/-0.319) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.905460717963247e-05}
0.626 (+/-0.318) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 2.0892961308540406e-05}
0.624 (+/-0.318) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.639 (+/-0.327) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.643 (+/-0.306) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.641 (+/-0.307) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.0199517204020175e-05}
0.618 (+/-0.322) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.3113112148259144e-05}
0.639 (+/-0.309) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.630780547701015e-05}
0.649 (+/-0.314) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.673 (+/-0.377) for {'C': 10.0, 'kernel': 'linear'}
0.637 (+/-0.310) for {'C': 100.0, 'kernel': 'linear'}
0.622 (+/-0.319) for {'C': 1000.0, 'kernel': 'linear'}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'linear'}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.20      0.17      0.18         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.99      0.99       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.660 (+/-0.315) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.661 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.7378008287493767e-05}
0.659 (+/-0.312) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.905460717963247e-05}
0.660 (+/-0.313) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.0892961308540406e-05}
0.661 (+/-0.315) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.661 (+/-0.315) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.661 (+/-0.315) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.662 (+/-0.315) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.0199517204020175e-05}
0.663 (+/-0.317) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.3113112148259144e-05}
0.662 (+/-0.317) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.630780547701015e-05}
0.662 (+/-0.315) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.661 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.662 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.7378008287493767e-05}
0.660 (+/-0.311) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.905460717963247e-05}
0.661 (+/-0.314) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.0892961308540406e-05}
0.662 (+/-0.317) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.662 (+/-0.314) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.661 (+/-0.314) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.662 (+/-0.315) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.0199517204020175e-05}
0.663 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.3113112148259144e-05}
0.663 (+/-0.317) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.630780547701015e-05}
0.663 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.662 (+/-0.317) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.663 (+/-0.316) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.7378008287493767e-05}
0.661 (+/-0.314) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.905460717963247e-05}
0.662 (+/-0.315) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.0892961308540406e-05}
0.638 (+/-0.238) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.662 (+/-0.315) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.662 (+/-0.316) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.663 (+/-0.317) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.0199517204020175e-05}
0.663 (+/-0.315) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.3113112148259144e-05}
0.663 (+/-0.318) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.630780547701015e-05}
0.663 (+/-0.315) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.662 (+/-0.316) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.663 (+/-0.316) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.7378008287493767e-05}
0.662 (+/-0.315) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.905460717963247e-05}
0.662 (+/-0.314) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.0892961308540406e-05}
0.638 (+/-0.237) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.662 (+/-0.315) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.663 (+/-0.316) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.688 (+/-0.300) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.0199517204020175e-05}
0.663 (+/-0.315) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.3113112148259144e-05}
0.663 (+/-0.317) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.630780547701015e-05}
0.663 (+/-0.315) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.686 (+/-0.296) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.663 (+/-0.316) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.7378008287493767e-05}
0.662 (+/-0.315) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.905460717963247e-05}
0.662 (+/-0.314) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.0892961308540406e-05}
0.638 (+/-0.237) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.663 (+/-0.316) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.638 (+/-0.237) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.688 (+/-0.300) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.0199517204020175e-05}
0.663 (+/-0.224) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.3113112148259144e-05}
0.638 (+/-0.237) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.630780547701015e-05}
0.639 (+/-0.236) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.687 (+/-0.298) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.663 (+/-0.317) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.7378008287493767e-05}
0.662 (+/-0.314) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.905460717963247e-05}
0.637 (+/-0.324) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.0892961308540406e-05}
0.613 (+/-0.240) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.688 (+/-0.298) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.638 (+/-0.237) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.663 (+/-0.317) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.0199517204020175e-05}
0.664 (+/-0.223) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.3113112148259144e-05}
0.639 (+/-0.237) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.630780547701015e-05}
0.614 (+/-0.238) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.662 (+/-0.316) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.663 (+/-0.317) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.7378008287493767e-05}
0.662 (+/-0.316) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.905460717963247e-05}
0.637 (+/-0.326) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.0892961308540406e-05}
0.613 (+/-0.241) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.689 (+/-0.298) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.664 (+/-0.224) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.664 (+/-0.317) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 3.0199517204020175e-05}
0.639 (+/-0.236) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 3.3113112148259144e-05}
0.639 (+/-0.236) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 3.630780547701015e-05}
0.614 (+/-0.239) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.661 (+/-0.317) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.664 (+/-0.318) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.7378008287493767e-05}
0.637 (+/-0.326) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.905460717963247e-05}
0.638 (+/-0.240) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.0892961308540406e-05}
0.613 (+/-0.241) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.688 (+/-0.300) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.664 (+/-0.317) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.664 (+/-0.317) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 3.0199517204020175e-05}
0.613 (+/-0.236) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 3.3113112148259144e-05}
0.639 (+/-0.236) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 3.630780547701015e-05}
0.638 (+/-0.237) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.661 (+/-0.316) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.664 (+/-0.319) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.7378008287493767e-05}
0.612 (+/-0.241) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.905460717963247e-05}
0.637 (+/-0.242) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 2.0892961308540406e-05}
0.613 (+/-0.240) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.638 (+/-0.326) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.639 (+/-0.237) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.664 (+/-0.316) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 3.0199517204020175e-05}
0.612 (+/-0.236) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 3.3113112148259144e-05}
0.639 (+/-0.236) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 3.630780547701015e-05}
0.638 (+/-0.237) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.661 (+/-0.317) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.664 (+/-0.319) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.7378008287493767e-05}
0.612 (+/-0.241) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.905460717963247e-05}
0.612 (+/-0.243) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 2.0892961308540406e-05}
0.613 (+/-0.239) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.638 (+/-0.326) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.639 (+/-0.236) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.663 (+/-0.315) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 3.0199517204020175e-05}
0.612 (+/-0.236) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 3.3113112148259144e-05}
0.638 (+/-0.237) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 3.630780547701015e-05}
0.638 (+/-0.237) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.662 (+/-0.316) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.664 (+/-0.319) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.7378008287493767e-05}
0.612 (+/-0.241) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.905460717963247e-05}
0.612 (+/-0.242) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 2.0892961308540406e-05}
0.613 (+/-0.240) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.638 (+/-0.327) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.639 (+/-0.236) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.663 (+/-0.315) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.0199517204020175e-05}
0.612 (+/-0.236) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.3113112148259144e-05}
0.638 (+/-0.237) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.630780547701015e-05}
0.639 (+/-0.238) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.663 (+/-0.316) for {'C': 10.0, 'kernel': 'linear'}
0.662 (+/-0.315) for {'C': 100.0, 'kernel': 'linear'}
0.613 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.588 (+/-0.233) for {'C': 10000.0, 'kernel': 'linear'}
0.588 (+/-0.233) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.11      0.17      0.13         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6886212795778712
第0轮loop中，tunemodel函数得到的最优参数是： ([{'C': array([ 9999999.99999999, 10964781.96143185, 12022644.34617414,
       13182567.38556406, 14454397.70745927, 15848931.92461113,
       17378008.28749376, 19054607.17963249, 20892961.30854038,
       22908676.52767773, 25118864.31509581]), 'kernel': ['rbf'], 'gamma': array([2.29086765e-05, 2.33345806e-05, 2.37684029e-05, 2.42102905e-05,
       2.46603934e-05, 2.51188643e-05, 2.55858589e-05, 2.60615355e-05,
       2.65460556e-05, 2.70395836e-05, 2.75422870e-05])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}], {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}, 0.6886212795778712)
这是第0次迭代微调C和gamma。
第0次迭代，得到delta： [5848931.92461113       0.        ]
执行tunemodel()函数前，使用的grid search parameter是：
 [{'C': array([ 9999999.99999999, 10964781.96143185, 12022644.34617414,
       13182567.38556406, 14454397.70745927, 15848931.92461113,
       17378008.28749376, 19054607.17963249, 20892961.30854038,
       22908676.52767773, 25118864.31509581]), 'kernel': ['rbf'], 'gamma': array([2.29086765e-05, 2.33345806e-05, 2.37684029e-05, 2.42102905e-05,
       2.46603934e-05, 2.51188643e-05, 2.55858589e-05, 2.60615355e-05,
       2.65460556e-05, 2.70395836e-05, 2.75422870e-05])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.626 (+/-0.318) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.664 (+/-0.326) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.3334580622810015e-05}
0.625 (+/-0.318) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.3768402866248774e-05}
0.640 (+/-0.313) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.4210290467361783e-05}
0.641 (+/-0.307) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.466039337234341e-05}
0.664 (+/-0.298) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.634 (+/-0.309) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5585858869056458e-05}
0.656 (+/-0.294) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.606153549998898e-05}
0.634 (+/-0.321) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.654605561975541e-05}
0.664 (+/-0.297) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.7039583641088475e-05}
0.639 (+/-0.309) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.626 (+/-0.318) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.656 (+/-0.312) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 2.3334580622810015e-05}
0.625 (+/-0.318) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 2.3768402866248774e-05}
0.645 (+/-0.318) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 2.4210290467361783e-05}
0.649 (+/-0.314) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 2.466039337234341e-05}
0.655 (+/-0.292) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.639 (+/-0.309) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 2.5585858869056458e-05}
0.658 (+/-0.292) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 2.606153549998898e-05}
0.634 (+/-0.321) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 2.654605561975541e-05}
0.668 (+/-0.295) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 2.7039583641088475e-05}
0.639 (+/-0.309) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.626 (+/-0.318) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.656 (+/-0.312) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 2.3334580622810015e-05}
0.625 (+/-0.318) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 2.3768402866248774e-05}
0.645 (+/-0.318) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 2.4210290467361783e-05}
0.649 (+/-0.314) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 2.466039337234341e-05}
0.658 (+/-0.292) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.635 (+/-0.308) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 2.5585858869056458e-05}
0.658 (+/-0.292) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 2.606153549998898e-05}
0.643 (+/-0.310) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 2.654605561975541e-05}
0.673 (+/-0.294) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 2.7039583641088475e-05}
0.639 (+/-0.309) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.626 (+/-0.318) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.656 (+/-0.312) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 2.3334580622810015e-05}
0.625 (+/-0.318) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 2.3768402866248774e-05}
0.640 (+/-0.313) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 2.4210290467361783e-05}
0.649 (+/-0.314) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 2.466039337234341e-05}
0.666 (+/-0.297) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.634 (+/-0.309) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 2.5585858869056458e-05}
0.641 (+/-0.307) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 2.606153549998898e-05}
0.632 (+/-0.309) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 2.654605561975541e-05}
0.673 (+/-0.294) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 2.7039583641088475e-05}
0.639 (+/-0.309) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.626 (+/-0.318) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.656 (+/-0.312) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 2.3334580622810015e-05}
0.625 (+/-0.318) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 2.3768402866248774e-05}
0.640 (+/-0.313) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 2.4210290467361783e-05}
0.643 (+/-0.306) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 2.466039337234341e-05}
0.669 (+/-0.298) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.634 (+/-0.309) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 2.5585858869056458e-05}
0.647 (+/-0.308) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 2.606153549998898e-05}
0.639 (+/-0.306) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 2.654605561975541e-05}
0.656 (+/-0.312) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 2.7039583641088475e-05}
0.647 (+/-0.315) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.624 (+/-0.319) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.656 (+/-0.312) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.3334580622810015e-05}
0.650 (+/-0.313) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.3768402866248774e-05}
0.666 (+/-0.302) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.4210290467361783e-05}
0.643 (+/-0.306) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.466039337234341e-05}
0.669 (+/-0.298) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.631 (+/-0.310) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.5585858869056458e-05}
0.647 (+/-0.308) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.606153549998898e-05}
0.639 (+/-0.306) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.654605561975541e-05}
0.673 (+/-0.294) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.7039583641088475e-05}
0.664 (+/-0.299) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.624 (+/-0.319) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.656 (+/-0.312) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 2.3334580622810015e-05}
0.650 (+/-0.313) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 2.3768402866248774e-05}
0.667 (+/-0.301) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 2.4210290467361783e-05}
0.641 (+/-0.307) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 2.466039337234341e-05}
0.668 (+/-0.300) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.630 (+/-0.311) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 2.5585858869056458e-05}
0.647 (+/-0.308) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 2.606153549998898e-05}
0.639 (+/-0.306) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 2.654605561975541e-05}
0.673 (+/-0.294) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 2.7039583641088475e-05}
0.664 (+/-0.299) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.631 (+/-0.319) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.656 (+/-0.312) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 2.3334580622810015e-05}
0.650 (+/-0.313) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 2.3768402866248774e-05}
0.668 (+/-0.300) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 2.4210290467361783e-05}
0.637 (+/-0.307) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 2.466039337234341e-05}
0.668 (+/-0.300) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.629 (+/-0.311) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 2.5585858869056458e-05}
0.647 (+/-0.308) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 2.606153549998898e-05}
0.639 (+/-0.306) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 2.654605561975541e-05}
0.698 (+/-0.352) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 2.7039583641088475e-05}
0.664 (+/-0.299) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.631 (+/-0.319) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.652 (+/-0.313) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.3334580622810015e-05}
0.650 (+/-0.313) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.3768402866248774e-05}
0.643 (+/-0.310) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.4210290467361783e-05}
0.637 (+/-0.307) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.466039337234341e-05}
0.668 (+/-0.300) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.629 (+/-0.311) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.5585858869056458e-05}
0.643 (+/-0.306) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.606153549998898e-05}
0.637 (+/-0.307) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.654605561975541e-05}
0.706 (+/-0.353) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.7039583641088475e-05}
0.664 (+/-0.299) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.631 (+/-0.319) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.652 (+/-0.313) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.3334580622810015e-05}
0.652 (+/-0.313) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.3768402866248774e-05}
0.639 (+/-0.309) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.4210290467361783e-05}
0.637 (+/-0.307) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.466039337234341e-05}
0.669 (+/-0.298) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.629 (+/-0.311) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.5585858869056458e-05}
0.643 (+/-0.306) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.606153549998898e-05}
0.637 (+/-0.307) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.654605561975541e-05}
0.698 (+/-0.352) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.7039583641088475e-05}
0.683 (+/-0.314) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.631 (+/-0.319) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.652 (+/-0.313) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.3334580622810015e-05}
0.627 (+/-0.318) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.3768402866248774e-05}
0.638 (+/-0.310) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.4210290467361783e-05}
0.637 (+/-0.307) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.466039337234341e-05}
0.669 (+/-0.298) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.629 (+/-0.311) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.5585858869056458e-05}
0.643 (+/-0.306) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.606153549998898e-05}
0.637 (+/-0.307) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.654605561975541e-05}
0.681 (+/-0.372) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.7039583641088475e-05}
0.660 (+/-0.327) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.706 (+/-0.383) for {'C': 10.0, 'kernel': 'linear'}
0.641 (+/-0.312) for {'C': 100.0, 'kernel': 'linear'}
0.622 (+/-0.319) for {'C': 1000.0, 'kernel': 'linear'}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'linear'}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.613 (+/-0.240) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.664 (+/-0.318) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.3334580622810015e-05}
0.638 (+/-0.324) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.3768402866248774e-05}
0.662 (+/-0.316) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.4210290467361783e-05}
0.638 (+/-0.238) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.466039337234341e-05}
0.688 (+/-0.298) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.638 (+/-0.237) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5585858869056458e-05}
0.688 (+/-0.298) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.606153549998898e-05}
0.638 (+/-0.327) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.654605561975541e-05}
0.664 (+/-0.224) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.7039583641088475e-05}
0.638 (+/-0.237) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.613 (+/-0.240) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.639 (+/-0.238) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 2.3334580622810015e-05}
0.638 (+/-0.324) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 2.3768402866248774e-05}
0.663 (+/-0.317) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 2.4210290467361783e-05}
0.638 (+/-0.239) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 2.466039337234341e-05}
0.688 (+/-0.297) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.638 (+/-0.237) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 2.5585858869056458e-05}
0.688 (+/-0.298) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 2.606153549998898e-05}
0.638 (+/-0.327) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 2.654605561975541e-05}
0.664 (+/-0.225) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 2.7039583641088475e-05}
0.638 (+/-0.237) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.613 (+/-0.241) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.639 (+/-0.238) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 2.3334580622810015e-05}
0.638 (+/-0.323) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 2.3768402866248774e-05}
0.663 (+/-0.316) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 2.4210290467361783e-05}
0.638 (+/-0.239) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 2.466039337234341e-05}
0.688 (+/-0.298) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.638 (+/-0.237) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 2.5585858869056458e-05}
0.688 (+/-0.298) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 2.606153549998898e-05}
0.663 (+/-0.317) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 2.654605561975541e-05}
0.664 (+/-0.225) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 2.7039583641088475e-05}
0.638 (+/-0.237) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.613 (+/-0.241) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.639 (+/-0.239) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 2.3334580622810015e-05}
0.637 (+/-0.324) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 2.3768402866248774e-05}
0.663 (+/-0.316) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 2.4210290467361783e-05}
0.638 (+/-0.239) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 2.466039337234341e-05}
0.688 (+/-0.298) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.638 (+/-0.237) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 2.5585858869056458e-05}
0.663 (+/-0.315) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 2.606153549998898e-05}
0.637 (+/-0.238) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 2.654605561975541e-05}
0.664 (+/-0.225) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 2.7039583641088475e-05}
0.639 (+/-0.236) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.613 (+/-0.241) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.639 (+/-0.239) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 2.3334580622810015e-05}
0.637 (+/-0.324) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 2.3768402866248774e-05}
0.663 (+/-0.316) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 2.4210290467361783e-05}
0.638 (+/-0.239) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 2.466039337234341e-05}
0.689 (+/-0.298) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.638 (+/-0.237) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 2.5585858869056458e-05}
0.664 (+/-0.316) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 2.606153549998898e-05}
0.638 (+/-0.238) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 2.654605561975541e-05}
0.639 (+/-0.238) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 2.7039583641088475e-05}
0.639 (+/-0.236) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.613 (+/-0.241) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.639 (+/-0.239) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.3334580622810015e-05}
0.663 (+/-0.315) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.3768402866248774e-05}
0.688 (+/-0.299) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.4210290467361783e-05}
0.638 (+/-0.239) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.466039337234341e-05}
0.689 (+/-0.298) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.637 (+/-0.237) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.5585858869056458e-05}
0.664 (+/-0.316) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.606153549998898e-05}
0.638 (+/-0.238) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.654605561975541e-05}
0.664 (+/-0.225) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.7039583641088475e-05}
0.664 (+/-0.224) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.613 (+/-0.240) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.639 (+/-0.239) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 2.3334580622810015e-05}
0.663 (+/-0.315) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 2.3768402866248774e-05}
0.688 (+/-0.299) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 2.4210290467361783e-05}
0.638 (+/-0.238) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 2.466039337234341e-05}
0.688 (+/-0.299) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.637 (+/-0.236) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 2.5585858869056458e-05}
0.664 (+/-0.316) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 2.606153549998898e-05}
0.638 (+/-0.238) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 2.654605561975541e-05}
0.664 (+/-0.225) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 2.7039583641088475e-05}
0.664 (+/-0.224) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.613 (+/-0.241) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.639 (+/-0.239) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 2.3334580622810015e-05}
0.662 (+/-0.315) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 2.3768402866248774e-05}
0.688 (+/-0.299) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 2.4210290467361783e-05}
0.638 (+/-0.238) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 2.466039337234341e-05}
0.688 (+/-0.299) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.637 (+/-0.236) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 2.5585858869056458e-05}
0.664 (+/-0.316) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 2.606153549998898e-05}
0.638 (+/-0.238) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 2.654605561975541e-05}
0.664 (+/-0.225) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 2.7039583641088475e-05}
0.664 (+/-0.224) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.613 (+/-0.241) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.639 (+/-0.238) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.3334580622810015e-05}
0.663 (+/-0.315) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.3768402866248774e-05}
0.663 (+/-0.316) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.4210290467361783e-05}
0.638 (+/-0.238) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.466039337234341e-05}
0.688 (+/-0.299) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.637 (+/-0.236) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.5585858869056458e-05}
0.664 (+/-0.315) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.606153549998898e-05}
0.638 (+/-0.238) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.654605561975541e-05}
0.665 (+/-0.225) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.7039583641088475e-05}
0.664 (+/-0.224) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.613 (+/-0.241) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.638 (+/-0.239) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.3334580622810015e-05}
0.663 (+/-0.315) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.3768402866248774e-05}
0.663 (+/-0.316) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.4210290467361783e-05}
0.638 (+/-0.237) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.466039337234341e-05}
0.688 (+/-0.300) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.638 (+/-0.235) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.5585858869056458e-05}
0.663 (+/-0.315) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.606153549998898e-05}
0.637 (+/-0.239) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.654605561975541e-05}
0.664 (+/-0.225) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.7039583641088475e-05}
0.689 (+/-0.300) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.613 (+/-0.241) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.638 (+/-0.239) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.3334580622810015e-05}
0.638 (+/-0.324) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.3768402866248774e-05}
0.662 (+/-0.316) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.4210290467361783e-05}
0.638 (+/-0.237) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.466039337234341e-05}
0.688 (+/-0.300) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.637 (+/-0.236) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.5585858869056458e-05}
0.663 (+/-0.315) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.606153549998898e-05}
0.637 (+/-0.239) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.654605561975541e-05}
0.639 (+/-0.238) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.7039583641088475e-05}
0.664 (+/-0.317) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.666 (+/-0.315) for {'C': 10.0, 'kernel': 'linear'}
0.663 (+/-0.316) for {'C': 100.0, 'kernel': 'linear'}
0.613 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.588 (+/-0.233) for {'C': 10000.0, 'kernel': 'linear'}
0.588 (+/-0.233) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.10      0.17      0.12         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.688941792398384
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.626 (+/-0.318) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.664 (+/-0.326) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.3334580622810015e-05}
0.625 (+/-0.318) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.3768402866248774e-05}
0.640 (+/-0.313) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.4210290467361783e-05}
0.641 (+/-0.307) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.466039337234341e-05}
0.664 (+/-0.298) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.634 (+/-0.309) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5585858869056458e-05}
0.656 (+/-0.294) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.606153549998898e-05}
0.634 (+/-0.321) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.654605561975541e-05}
0.664 (+/-0.297) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.7039583641088475e-05}
0.639 (+/-0.309) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.626 (+/-0.318) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.656 (+/-0.312) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 2.3334580622810015e-05}
0.625 (+/-0.318) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 2.3768402866248774e-05}
0.645 (+/-0.318) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 2.4210290467361783e-05}
0.649 (+/-0.314) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 2.466039337234341e-05}
0.655 (+/-0.292) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.639 (+/-0.309) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 2.5585858869056458e-05}
0.658 (+/-0.292) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 2.606153549998898e-05}
0.634 (+/-0.321) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 2.654605561975541e-05}
0.668 (+/-0.295) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 2.7039583641088475e-05}
0.639 (+/-0.309) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.626 (+/-0.318) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.656 (+/-0.312) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 2.3334580622810015e-05}
0.625 (+/-0.318) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 2.3768402866248774e-05}
0.645 (+/-0.318) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 2.4210290467361783e-05}
0.649 (+/-0.314) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 2.466039337234341e-05}
0.658 (+/-0.292) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.635 (+/-0.308) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 2.5585858869056458e-05}
0.658 (+/-0.292) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 2.606153549998898e-05}
0.643 (+/-0.310) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 2.654605561975541e-05}
0.673 (+/-0.294) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 2.7039583641088475e-05}
0.639 (+/-0.309) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.626 (+/-0.318) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.656 (+/-0.312) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 2.3334580622810015e-05}
0.625 (+/-0.318) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 2.3768402866248774e-05}
0.640 (+/-0.313) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 2.4210290467361783e-05}
0.649 (+/-0.314) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 2.466039337234341e-05}
0.666 (+/-0.297) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.634 (+/-0.309) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 2.5585858869056458e-05}
0.641 (+/-0.307) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 2.606153549998898e-05}
0.632 (+/-0.309) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 2.654605561975541e-05}
0.673 (+/-0.294) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 2.7039583641088475e-05}
0.639 (+/-0.309) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.626 (+/-0.318) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.656 (+/-0.312) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 2.3334580622810015e-05}
0.625 (+/-0.318) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 2.3768402866248774e-05}
0.640 (+/-0.313) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 2.4210290467361783e-05}
0.643 (+/-0.306) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 2.466039337234341e-05}
0.669 (+/-0.298) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.634 (+/-0.309) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 2.5585858869056458e-05}
0.647 (+/-0.308) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 2.606153549998898e-05}
0.639 (+/-0.306) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 2.654605561975541e-05}
0.656 (+/-0.312) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 2.7039583641088475e-05}
0.647 (+/-0.315) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.624 (+/-0.319) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.656 (+/-0.312) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.3334580622810015e-05}
0.650 (+/-0.313) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.3768402866248774e-05}
0.666 (+/-0.302) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.4210290467361783e-05}
0.643 (+/-0.306) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.466039337234341e-05}
0.669 (+/-0.298) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.631 (+/-0.310) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.5585858869056458e-05}
0.647 (+/-0.308) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.606153549998898e-05}
0.639 (+/-0.306) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.654605561975541e-05}
0.673 (+/-0.294) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.7039583641088475e-05}
0.664 (+/-0.299) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.624 (+/-0.319) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.656 (+/-0.312) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 2.3334580622810015e-05}
0.650 (+/-0.313) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 2.3768402866248774e-05}
0.667 (+/-0.301) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 2.4210290467361783e-05}
0.641 (+/-0.307) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 2.466039337234341e-05}
0.668 (+/-0.300) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.630 (+/-0.311) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 2.5585858869056458e-05}
0.647 (+/-0.308) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 2.606153549998898e-05}
0.639 (+/-0.306) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 2.654605561975541e-05}
0.673 (+/-0.294) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 2.7039583641088475e-05}
0.664 (+/-0.299) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.631 (+/-0.319) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.656 (+/-0.312) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 2.3334580622810015e-05}
0.650 (+/-0.313) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 2.3768402866248774e-05}
0.668 (+/-0.300) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 2.4210290467361783e-05}
0.637 (+/-0.307) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 2.466039337234341e-05}
0.668 (+/-0.300) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.629 (+/-0.311) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 2.5585858869056458e-05}
0.647 (+/-0.308) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 2.606153549998898e-05}
0.639 (+/-0.306) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 2.654605561975541e-05}
0.698 (+/-0.352) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 2.7039583641088475e-05}
0.664 (+/-0.299) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.631 (+/-0.319) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.652 (+/-0.313) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.3334580622810015e-05}
0.650 (+/-0.313) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.3768402866248774e-05}
0.643 (+/-0.310) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.4210290467361783e-05}
0.637 (+/-0.307) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.466039337234341e-05}
0.668 (+/-0.300) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.629 (+/-0.311) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.5585858869056458e-05}
0.643 (+/-0.306) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.606153549998898e-05}
0.637 (+/-0.307) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.654605561975541e-05}
0.706 (+/-0.353) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.7039583641088475e-05}
0.664 (+/-0.299) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.631 (+/-0.319) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.652 (+/-0.313) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.3334580622810015e-05}
0.652 (+/-0.313) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.3768402866248774e-05}
0.639 (+/-0.309) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.4210290467361783e-05}
0.637 (+/-0.307) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.466039337234341e-05}
0.669 (+/-0.298) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.629 (+/-0.311) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.5585858869056458e-05}
0.643 (+/-0.306) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.606153549998898e-05}
0.637 (+/-0.307) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.654605561975541e-05}
0.698 (+/-0.352) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.7039583641088475e-05}
0.683 (+/-0.314) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.631 (+/-0.319) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.652 (+/-0.313) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.3334580622810015e-05}
0.627 (+/-0.318) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.3768402866248774e-05}
0.638 (+/-0.310) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.4210290467361783e-05}
0.637 (+/-0.307) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.466039337234341e-05}
0.669 (+/-0.298) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.629 (+/-0.311) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.5585858869056458e-05}
0.643 (+/-0.306) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.606153549998898e-05}
0.637 (+/-0.307) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.654605561975541e-05}
0.681 (+/-0.372) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.7039583641088475e-05}
0.660 (+/-0.327) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.673 (+/-0.377) for {'C': 10.0, 'kernel': 'linear'}
0.637 (+/-0.310) for {'C': 100.0, 'kernel': 'linear'}
0.622 (+/-0.319) for {'C': 1000.0, 'kernel': 'linear'}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'linear'}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.20      0.17      0.18         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.99      0.99       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.613 (+/-0.240) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.664 (+/-0.318) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.3334580622810015e-05}
0.638 (+/-0.324) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.3768402866248774e-05}
0.662 (+/-0.316) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.4210290467361783e-05}
0.638 (+/-0.238) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.466039337234341e-05}
0.688 (+/-0.298) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.638 (+/-0.237) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5585858869056458e-05}
0.688 (+/-0.298) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.606153549998898e-05}
0.638 (+/-0.327) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.654605561975541e-05}
0.664 (+/-0.224) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.7039583641088475e-05}
0.638 (+/-0.237) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.613 (+/-0.240) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.639 (+/-0.238) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 2.3334580622810015e-05}
0.638 (+/-0.324) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 2.3768402866248774e-05}
0.663 (+/-0.317) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 2.4210290467361783e-05}
0.638 (+/-0.239) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 2.466039337234341e-05}
0.688 (+/-0.297) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.638 (+/-0.237) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 2.5585858869056458e-05}
0.688 (+/-0.298) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 2.606153549998898e-05}
0.638 (+/-0.327) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 2.654605561975541e-05}
0.664 (+/-0.225) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 2.7039583641088475e-05}
0.638 (+/-0.237) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.613 (+/-0.241) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.639 (+/-0.238) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 2.3334580622810015e-05}
0.638 (+/-0.323) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 2.3768402866248774e-05}
0.663 (+/-0.316) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 2.4210290467361783e-05}
0.638 (+/-0.239) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 2.466039337234341e-05}
0.688 (+/-0.298) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.638 (+/-0.237) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 2.5585858869056458e-05}
0.688 (+/-0.298) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 2.606153549998898e-05}
0.663 (+/-0.317) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 2.654605561975541e-05}
0.664 (+/-0.225) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 2.7039583641088475e-05}
0.638 (+/-0.237) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.613 (+/-0.241) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.639 (+/-0.239) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 2.3334580622810015e-05}
0.637 (+/-0.324) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 2.3768402866248774e-05}
0.663 (+/-0.316) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 2.4210290467361783e-05}
0.638 (+/-0.239) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 2.466039337234341e-05}
0.688 (+/-0.298) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.638 (+/-0.237) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 2.5585858869056458e-05}
0.663 (+/-0.315) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 2.606153549998898e-05}
0.637 (+/-0.238) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 2.654605561975541e-05}
0.664 (+/-0.225) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 2.7039583641088475e-05}
0.639 (+/-0.236) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.613 (+/-0.241) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.639 (+/-0.239) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 2.3334580622810015e-05}
0.637 (+/-0.324) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 2.3768402866248774e-05}
0.663 (+/-0.316) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 2.4210290467361783e-05}
0.638 (+/-0.239) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 2.466039337234341e-05}
0.689 (+/-0.298) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.638 (+/-0.237) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 2.5585858869056458e-05}
0.664 (+/-0.316) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 2.606153549998898e-05}
0.638 (+/-0.238) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 2.654605561975541e-05}
0.639 (+/-0.238) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 2.7039583641088475e-05}
0.639 (+/-0.236) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.613 (+/-0.241) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.639 (+/-0.239) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.3334580622810015e-05}
0.663 (+/-0.315) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.3768402866248774e-05}
0.688 (+/-0.299) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.4210290467361783e-05}
0.638 (+/-0.239) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.466039337234341e-05}
0.689 (+/-0.298) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.637 (+/-0.237) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.5585858869056458e-05}
0.664 (+/-0.316) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.606153549998898e-05}
0.638 (+/-0.238) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.654605561975541e-05}
0.664 (+/-0.225) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.7039583641088475e-05}
0.664 (+/-0.224) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.613 (+/-0.240) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.639 (+/-0.239) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 2.3334580622810015e-05}
0.663 (+/-0.315) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 2.3768402866248774e-05}
0.688 (+/-0.299) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 2.4210290467361783e-05}
0.638 (+/-0.238) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 2.466039337234341e-05}
0.688 (+/-0.299) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.637 (+/-0.236) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 2.5585858869056458e-05}
0.664 (+/-0.316) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 2.606153549998898e-05}
0.638 (+/-0.238) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 2.654605561975541e-05}
0.664 (+/-0.225) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 2.7039583641088475e-05}
0.664 (+/-0.224) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.613 (+/-0.241) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.639 (+/-0.239) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 2.3334580622810015e-05}
0.662 (+/-0.315) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 2.3768402866248774e-05}
0.688 (+/-0.299) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 2.4210290467361783e-05}
0.638 (+/-0.238) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 2.466039337234341e-05}
0.688 (+/-0.299) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.637 (+/-0.236) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 2.5585858869056458e-05}
0.664 (+/-0.316) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 2.606153549998898e-05}
0.638 (+/-0.238) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 2.654605561975541e-05}
0.664 (+/-0.225) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 2.7039583641088475e-05}
0.664 (+/-0.224) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.613 (+/-0.241) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.639 (+/-0.238) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.3334580622810015e-05}
0.663 (+/-0.315) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.3768402866248774e-05}
0.663 (+/-0.316) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.4210290467361783e-05}
0.638 (+/-0.238) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.466039337234341e-05}
0.688 (+/-0.299) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.637 (+/-0.236) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.5585858869056458e-05}
0.664 (+/-0.315) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.606153549998898e-05}
0.638 (+/-0.238) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.654605561975541e-05}
0.665 (+/-0.225) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.7039583641088475e-05}
0.664 (+/-0.224) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.613 (+/-0.241) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.638 (+/-0.239) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.3334580622810015e-05}
0.663 (+/-0.315) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.3768402866248774e-05}
0.663 (+/-0.316) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.4210290467361783e-05}
0.638 (+/-0.237) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.466039337234341e-05}
0.688 (+/-0.300) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.638 (+/-0.235) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.5585858869056458e-05}
0.663 (+/-0.315) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.606153549998898e-05}
0.637 (+/-0.239) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.654605561975541e-05}
0.664 (+/-0.225) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.7039583641088475e-05}
0.689 (+/-0.300) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.613 (+/-0.241) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.638 (+/-0.239) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.3334580622810015e-05}
0.638 (+/-0.324) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.3768402866248774e-05}
0.662 (+/-0.316) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.4210290467361783e-05}
0.638 (+/-0.237) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.466039337234341e-05}
0.688 (+/-0.300) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.637 (+/-0.236) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.5585858869056458e-05}
0.663 (+/-0.315) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.606153549998898e-05}
0.637 (+/-0.239) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.654605561975541e-05}
0.639 (+/-0.238) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.7039583641088475e-05}
0.664 (+/-0.317) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.663 (+/-0.316) for {'C': 10.0, 'kernel': 'linear'}
0.662 (+/-0.315) for {'C': 100.0, 'kernel': 'linear'}
0.613 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.588 (+/-0.233) for {'C': 10000.0, 'kernel': 'linear'}
0.588 (+/-0.233) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.10      0.17      0.12         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.688941792398384
发现最优参数gamma为原先的最大/最小值，直接重新设置超参。
第1轮loop中，tunemodel函数得到的最优参数是： ([{'C': array([20892961.30854038, 21281390.4598271 , 21677041.04819691,
       22080047.33018901, 22490546.05835781, 22908676.52767773,
       23334580.62281001, 23768402.86624874, 24210290.46736181,
       24660393.37234341, 25118864.31509581]), 'kernel': ['rbf'], 'gamma': array([2.7542287e-10, 2.7542287e-09, 2.7542287e-08, 2.7542287e-07,
       2.7542287e-06, 2.7542287e-05, 2.7542287e-04, 2.7542287e-03,
       2.7542287e-02, 2.7542287e-01, 2.7542287e+00])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}], {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}, 0.688941792398384)
这是第1次迭代微调C和gamma。
第1次迭代，得到delta： [7.05974460e+06 2.42342272e-06]
执行tunemodel()函数前，使用的grid search parameter是：
 [{'C': array([20892961.30854038, 21281390.4598271 , 21677041.04819691,
       22080047.33018901, 22490546.05835781, 22908676.52767773,
       23334580.62281001, 23768402.86624874, 24210290.46736181,
       24660393.37234341, 25118864.31509581]), 'kernel': ['rbf'], 'gamma': array([2.7542287e-10, 2.7542287e-09, 2.7542287e-08, 2.7542287e-07,
       2.7542287e-06, 2.7542287e-05, 2.7542287e-04, 2.7542287e-03,
       2.7542287e-02, 2.7542287e-01, 2.7542287e+00])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-10}
0.747 (+/-0.502) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-09}
0.547 (+/-0.300) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-08}
0.678 (+/-0.431) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.754228703338175e-07}
0.635 (+/-0.307) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.7542287033381744e-06}
0.664 (+/-0.299) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-05}
0.634 (+/-0.308) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 0.00027542287033381743}
0.614 (+/-0.327) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 0.0027542287033381747}
0.614 (+/-0.327) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 0.027542287033381744}
0.614 (+/-0.308) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 0.27542287033381746}
0.672 (+/-0.451) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.754228703338169}
0.496 (+/-0.001) for {'C': 21281390.459827095, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-10}
0.747 (+/-0.502) for {'C': 21281390.459827095, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-09}
0.547 (+/-0.300) for {'C': 21281390.459827095, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-08}
0.678 (+/-0.431) for {'C': 21281390.459827095, 'kernel': 'rbf', 'gamma': 2.754228703338175e-07}
0.635 (+/-0.307) for {'C': 21281390.459827095, 'kernel': 'rbf', 'gamma': 2.7542287033381744e-06}
0.666 (+/-0.297) for {'C': 21281390.459827095, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-05}
0.634 (+/-0.308) for {'C': 21281390.459827095, 'kernel': 'rbf', 'gamma': 0.00027542287033381743}
0.614 (+/-0.327) for {'C': 21281390.459827095, 'kernel': 'rbf', 'gamma': 0.0027542287033381747}
0.614 (+/-0.327) for {'C': 21281390.459827095, 'kernel': 'rbf', 'gamma': 0.027542287033381744}
0.614 (+/-0.308) for {'C': 21281390.459827095, 'kernel': 'rbf', 'gamma': 0.27542287033381746}
0.672 (+/-0.451) for {'C': 21281390.459827095, 'kernel': 'rbf', 'gamma': 2.754228703338169}
0.496 (+/-0.001) for {'C': 21677041.04819691, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-10}
0.747 (+/-0.502) for {'C': 21677041.04819691, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-09}
0.547 (+/-0.300) for {'C': 21677041.04819691, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-08}
0.678 (+/-0.431) for {'C': 21677041.04819691, 'kernel': 'rbf', 'gamma': 2.754228703338175e-07}
0.635 (+/-0.307) for {'C': 21677041.04819691, 'kernel': 'rbf', 'gamma': 2.7542287033381744e-06}
0.666 (+/-0.297) for {'C': 21677041.04819691, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-05}
0.634 (+/-0.308) for {'C': 21677041.04819691, 'kernel': 'rbf', 'gamma': 0.00027542287033381743}
0.614 (+/-0.327) for {'C': 21677041.04819691, 'kernel': 'rbf', 'gamma': 0.0027542287033381747}
0.614 (+/-0.327) for {'C': 21677041.04819691, 'kernel': 'rbf', 'gamma': 0.027542287033381744}
0.614 (+/-0.308) for {'C': 21677041.04819691, 'kernel': 'rbf', 'gamma': 0.27542287033381746}
0.672 (+/-0.451) for {'C': 21677041.04819691, 'kernel': 'rbf', 'gamma': 2.754228703338169}
0.496 (+/-0.001) for {'C': 22080047.33018901, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-10}
0.747 (+/-0.502) for {'C': 22080047.33018901, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-09}
0.547 (+/-0.300) for {'C': 22080047.33018901, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-08}
0.678 (+/-0.431) for {'C': 22080047.33018901, 'kernel': 'rbf', 'gamma': 2.754228703338175e-07}
0.635 (+/-0.307) for {'C': 22080047.33018901, 'kernel': 'rbf', 'gamma': 2.7542287033381744e-06}
0.674 (+/-0.311) for {'C': 22080047.33018901, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-05}
0.634 (+/-0.308) for {'C': 22080047.33018901, 'kernel': 'rbf', 'gamma': 0.00027542287033381743}
0.614 (+/-0.327) for {'C': 22080047.33018901, 'kernel': 'rbf', 'gamma': 0.0027542287033381747}
0.614 (+/-0.327) for {'C': 22080047.33018901, 'kernel': 'rbf', 'gamma': 0.027542287033381744}
0.614 (+/-0.308) for {'C': 22080047.33018901, 'kernel': 'rbf', 'gamma': 0.27542287033381746}
0.672 (+/-0.451) for {'C': 22080047.33018901, 'kernel': 'rbf', 'gamma': 2.754228703338169}
0.496 (+/-0.001) for {'C': 22490546.058357812, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-10}
0.747 (+/-0.502) for {'C': 22490546.058357812, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-09}
0.548 (+/-0.299) for {'C': 22490546.058357812, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-08}
0.678 (+/-0.431) for {'C': 22490546.058357812, 'kernel': 'rbf', 'gamma': 2.754228703338175e-07}
0.635 (+/-0.307) for {'C': 22490546.058357812, 'kernel': 'rbf', 'gamma': 2.7542287033381744e-06}
0.683 (+/-0.314) for {'C': 22490546.058357812, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-05}
0.634 (+/-0.308) for {'C': 22490546.058357812, 'kernel': 'rbf', 'gamma': 0.00027542287033381743}
0.614 (+/-0.327) for {'C': 22490546.058357812, 'kernel': 'rbf', 'gamma': 0.0027542287033381747}
0.614 (+/-0.327) for {'C': 22490546.058357812, 'kernel': 'rbf', 'gamma': 0.027542287033381744}
0.614 (+/-0.308) for {'C': 22490546.058357812, 'kernel': 'rbf', 'gamma': 0.27542287033381746}
0.672 (+/-0.451) for {'C': 22490546.058357812, 'kernel': 'rbf', 'gamma': 2.754228703338169}
0.496 (+/-0.001) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-10}
0.747 (+/-0.502) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-09}
0.548 (+/-0.299) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-08}
0.672 (+/-0.439) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.754228703338175e-07}
0.635 (+/-0.307) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.7542287033381744e-06}
0.683 (+/-0.314) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-05}
0.634 (+/-0.308) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 0.00027542287033381743}
0.614 (+/-0.327) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 0.0027542287033381747}
0.614 (+/-0.327) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 0.027542287033381744}
0.614 (+/-0.308) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 0.27542287033381746}
0.672 (+/-0.451) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.754228703338169}
0.496 (+/-0.001) for {'C': 23334580.622810014, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-10}
0.747 (+/-0.502) for {'C': 23334580.622810014, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-09}
0.548 (+/-0.299) for {'C': 23334580.622810014, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-08}
0.678 (+/-0.431) for {'C': 23334580.622810014, 'kernel': 'rbf', 'gamma': 2.754228703338175e-07}
0.635 (+/-0.307) for {'C': 23334580.622810014, 'kernel': 'rbf', 'gamma': 2.7542287033381744e-06}
0.683 (+/-0.314) for {'C': 23334580.622810014, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-05}
0.634 (+/-0.308) for {'C': 23334580.622810014, 'kernel': 'rbf', 'gamma': 0.00027542287033381743}
0.614 (+/-0.327) for {'C': 23334580.622810014, 'kernel': 'rbf', 'gamma': 0.0027542287033381747}
0.614 (+/-0.327) for {'C': 23334580.622810014, 'kernel': 'rbf', 'gamma': 0.027542287033381744}
0.614 (+/-0.308) for {'C': 23334580.622810014, 'kernel': 'rbf', 'gamma': 0.27542287033381746}
0.672 (+/-0.451) for {'C': 23334580.622810014, 'kernel': 'rbf', 'gamma': 2.754228703338169}
0.496 (+/-0.001) for {'C': 23768402.86624874, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-10}
0.747 (+/-0.502) for {'C': 23768402.86624874, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-09}
0.548 (+/-0.299) for {'C': 23768402.86624874, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-08}
0.678 (+/-0.431) for {'C': 23768402.86624874, 'kernel': 'rbf', 'gamma': 2.754228703338175e-07}
0.635 (+/-0.307) for {'C': 23768402.86624874, 'kernel': 'rbf', 'gamma': 2.7542287033381744e-06}
0.658 (+/-0.329) for {'C': 23768402.86624874, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-05}
0.634 (+/-0.308) for {'C': 23768402.86624874, 'kernel': 'rbf', 'gamma': 0.00027542287033381743}
0.614 (+/-0.327) for {'C': 23768402.86624874, 'kernel': 'rbf', 'gamma': 0.0027542287033381747}
0.614 (+/-0.327) for {'C': 23768402.86624874, 'kernel': 'rbf', 'gamma': 0.027542287033381744}
0.614 (+/-0.308) for {'C': 23768402.86624874, 'kernel': 'rbf', 'gamma': 0.27542287033381746}
0.672 (+/-0.451) for {'C': 23768402.86624874, 'kernel': 'rbf', 'gamma': 2.754228703338169}
0.496 (+/-0.001) for {'C': 24210290.467361808, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-10}
0.747 (+/-0.502) for {'C': 24210290.467361808, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-09}
0.548 (+/-0.299) for {'C': 24210290.467361808, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-08}
0.677 (+/-0.432) for {'C': 24210290.467361808, 'kernel': 'rbf', 'gamma': 2.754228703338175e-07}
0.635 (+/-0.307) for {'C': 24210290.467361808, 'kernel': 'rbf', 'gamma': 2.7542287033381744e-06}
0.660 (+/-0.327) for {'C': 24210290.467361808, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-05}
0.634 (+/-0.308) for {'C': 24210290.467361808, 'kernel': 'rbf', 'gamma': 0.00027542287033381743}
0.614 (+/-0.327) for {'C': 24210290.467361808, 'kernel': 'rbf', 'gamma': 0.0027542287033381747}
0.614 (+/-0.327) for {'C': 24210290.467361808, 'kernel': 'rbf', 'gamma': 0.027542287033381744}
0.614 (+/-0.308) for {'C': 24210290.467361808, 'kernel': 'rbf', 'gamma': 0.27542287033381746}
0.672 (+/-0.451) for {'C': 24210290.467361808, 'kernel': 'rbf', 'gamma': 2.754228703338169}
0.496 (+/-0.001) for {'C': 24660393.37234341, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-10}
0.747 (+/-0.502) for {'C': 24660393.37234341, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-09}
0.548 (+/-0.299) for {'C': 24660393.37234341, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-08}
0.677 (+/-0.432) for {'C': 24660393.37234341, 'kernel': 'rbf', 'gamma': 2.754228703338175e-07}
0.635 (+/-0.307) for {'C': 24660393.37234341, 'kernel': 'rbf', 'gamma': 2.7542287033381744e-06}
0.660 (+/-0.327) for {'C': 24660393.37234341, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-05}
0.634 (+/-0.308) for {'C': 24660393.37234341, 'kernel': 'rbf', 'gamma': 0.00027542287033381743}
0.614 (+/-0.327) for {'C': 24660393.37234341, 'kernel': 'rbf', 'gamma': 0.0027542287033381747}
0.614 (+/-0.327) for {'C': 24660393.37234341, 'kernel': 'rbf', 'gamma': 0.027542287033381744}
0.614 (+/-0.308) for {'C': 24660393.37234341, 'kernel': 'rbf', 'gamma': 0.27542287033381746}
0.672 (+/-0.451) for {'C': 24660393.37234341, 'kernel': 'rbf', 'gamma': 2.754228703338169}
0.496 (+/-0.001) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-10}
0.747 (+/-0.502) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-09}
0.548 (+/-0.299) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-08}
0.677 (+/-0.432) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.754228703338175e-07}
0.635 (+/-0.307) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.7542287033381744e-06}
0.660 (+/-0.327) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-05}
0.634 (+/-0.308) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.00027542287033381743}
0.614 (+/-0.327) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.0027542287033381747}
0.614 (+/-0.327) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.027542287033381744}
0.614 (+/-0.308) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.27542287033381746}
0.672 (+/-0.451) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.754228703338169}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.706 (+/-0.383) for {'C': 10.0, 'kernel': 'linear'}
0.641 (+/-0.312) for {'C': 100.0, 'kernel': 'linear'}
0.622 (+/-0.319) for {'C': 1000.0, 'kernel': 'linear'}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'linear'}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      1.00       623

avg / total       0.98      0.99      0.99       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-10}
0.617 (+/-0.238) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-09}
0.510 (+/-0.251) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-08}
0.656 (+/-0.309) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.754228703338175e-07}
0.662 (+/-0.314) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.7542287033381744e-06}
0.664 (+/-0.224) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-05}
0.638 (+/-0.237) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 0.00027542287033381743}
0.588 (+/-0.233) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 0.0027542287033381747}
0.588 (+/-0.232) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 0.027542287033381744}
0.614 (+/-0.237) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 0.27542287033381746}
0.590 (+/-0.231) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.754228703338169}
0.500 (+/-0.000) for {'C': 21281390.459827095, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-10}
0.617 (+/-0.238) for {'C': 21281390.459827095, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-09}
0.510 (+/-0.252) for {'C': 21281390.459827095, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-08}
0.656 (+/-0.309) for {'C': 21281390.459827095, 'kernel': 'rbf', 'gamma': 2.754228703338175e-07}
0.662 (+/-0.314) for {'C': 21281390.459827095, 'kernel': 'rbf', 'gamma': 2.7542287033381744e-06}
0.664 (+/-0.224) for {'C': 21281390.459827095, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-05}
0.638 (+/-0.237) for {'C': 21281390.459827095, 'kernel': 'rbf', 'gamma': 0.00027542287033381743}
0.588 (+/-0.233) for {'C': 21281390.459827095, 'kernel': 'rbf', 'gamma': 0.0027542287033381747}
0.588 (+/-0.232) for {'C': 21281390.459827095, 'kernel': 'rbf', 'gamma': 0.027542287033381744}
0.614 (+/-0.237) for {'C': 21281390.459827095, 'kernel': 'rbf', 'gamma': 0.27542287033381746}
0.590 (+/-0.231) for {'C': 21281390.459827095, 'kernel': 'rbf', 'gamma': 2.754228703338169}
0.500 (+/-0.000) for {'C': 21677041.04819691, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-10}
0.617 (+/-0.238) for {'C': 21677041.04819691, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-09}
0.509 (+/-0.253) for {'C': 21677041.04819691, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-08}
0.656 (+/-0.309) for {'C': 21677041.04819691, 'kernel': 'rbf', 'gamma': 2.754228703338175e-07}
0.662 (+/-0.314) for {'C': 21677041.04819691, 'kernel': 'rbf', 'gamma': 2.7542287033381744e-06}
0.664 (+/-0.224) for {'C': 21677041.04819691, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-05}
0.638 (+/-0.237) for {'C': 21677041.04819691, 'kernel': 'rbf', 'gamma': 0.00027542287033381743}
0.588 (+/-0.233) for {'C': 21677041.04819691, 'kernel': 'rbf', 'gamma': 0.0027542287033381747}
0.588 (+/-0.232) for {'C': 21677041.04819691, 'kernel': 'rbf', 'gamma': 0.027542287033381744}
0.614 (+/-0.237) for {'C': 21677041.04819691, 'kernel': 'rbf', 'gamma': 0.27542287033381746}
0.590 (+/-0.231) for {'C': 21677041.04819691, 'kernel': 'rbf', 'gamma': 2.754228703338169}
0.500 (+/-0.000) for {'C': 22080047.33018901, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-10}
0.617 (+/-0.238) for {'C': 22080047.33018901, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-09}
0.509 (+/-0.253) for {'C': 22080047.33018901, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-08}
0.656 (+/-0.309) for {'C': 22080047.33018901, 'kernel': 'rbf', 'gamma': 2.754228703338175e-07}
0.662 (+/-0.314) for {'C': 22080047.33018901, 'kernel': 'rbf', 'gamma': 2.7542287033381744e-06}
0.689 (+/-0.300) for {'C': 22080047.33018901, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-05}
0.638 (+/-0.237) for {'C': 22080047.33018901, 'kernel': 'rbf', 'gamma': 0.00027542287033381743}
0.588 (+/-0.233) for {'C': 22080047.33018901, 'kernel': 'rbf', 'gamma': 0.0027542287033381747}
0.588 (+/-0.232) for {'C': 22080047.33018901, 'kernel': 'rbf', 'gamma': 0.027542287033381744}
0.614 (+/-0.237) for {'C': 22080047.33018901, 'kernel': 'rbf', 'gamma': 0.27542287033381746}
0.590 (+/-0.231) for {'C': 22080047.33018901, 'kernel': 'rbf', 'gamma': 2.754228703338169}
0.500 (+/-0.000) for {'C': 22490546.058357812, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-10}
0.617 (+/-0.238) for {'C': 22490546.058357812, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-09}
0.533 (+/-0.208) for {'C': 22490546.058357812, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-08}
0.656 (+/-0.309) for {'C': 22490546.058357812, 'kernel': 'rbf', 'gamma': 2.754228703338175e-07}
0.662 (+/-0.314) for {'C': 22490546.058357812, 'kernel': 'rbf', 'gamma': 2.7542287033381744e-06}
0.689 (+/-0.300) for {'C': 22490546.058357812, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-05}
0.638 (+/-0.237) for {'C': 22490546.058357812, 'kernel': 'rbf', 'gamma': 0.00027542287033381743}
0.588 (+/-0.233) for {'C': 22490546.058357812, 'kernel': 'rbf', 'gamma': 0.0027542287033381747}
0.588 (+/-0.232) for {'C': 22490546.058357812, 'kernel': 'rbf', 'gamma': 0.027542287033381744}
0.614 (+/-0.237) for {'C': 22490546.058357812, 'kernel': 'rbf', 'gamma': 0.27542287033381746}
0.590 (+/-0.231) for {'C': 22490546.058357812, 'kernel': 'rbf', 'gamma': 2.754228703338169}
0.500 (+/-0.000) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-10}
0.617 (+/-0.238) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-09}
0.533 (+/-0.208) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-08}
0.631 (+/-0.319) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.754228703338175e-07}
0.662 (+/-0.315) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.7542287033381744e-06}
0.689 (+/-0.300) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-05}
0.638 (+/-0.237) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 0.00027542287033381743}
0.588 (+/-0.233) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 0.0027542287033381747}
0.588 (+/-0.232) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 0.027542287033381744}
0.614 (+/-0.237) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 0.27542287033381746}
0.590 (+/-0.231) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.754228703338169}
0.500 (+/-0.000) for {'C': 23334580.622810014, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-10}
0.617 (+/-0.238) for {'C': 23334580.622810014, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-09}
0.533 (+/-0.208) for {'C': 23334580.622810014, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-08}
0.656 (+/-0.309) for {'C': 23334580.622810014, 'kernel': 'rbf', 'gamma': 2.754228703338175e-07}
0.662 (+/-0.315) for {'C': 23334580.622810014, 'kernel': 'rbf', 'gamma': 2.7542287033381744e-06}
0.689 (+/-0.300) for {'C': 23334580.622810014, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-05}
0.638 (+/-0.237) for {'C': 23334580.622810014, 'kernel': 'rbf', 'gamma': 0.00027542287033381743}
0.588 (+/-0.233) for {'C': 23334580.622810014, 'kernel': 'rbf', 'gamma': 0.0027542287033381747}
0.588 (+/-0.232) for {'C': 23334580.622810014, 'kernel': 'rbf', 'gamma': 0.027542287033381744}
0.614 (+/-0.237) for {'C': 23334580.622810014, 'kernel': 'rbf', 'gamma': 0.27542287033381746}
0.590 (+/-0.231) for {'C': 23334580.622810014, 'kernel': 'rbf', 'gamma': 2.754228703338169}
0.500 (+/-0.000) for {'C': 23768402.86624874, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-10}
0.617 (+/-0.238) for {'C': 23768402.86624874, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-09}
0.532 (+/-0.208) for {'C': 23768402.86624874, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-08}
0.656 (+/-0.309) for {'C': 23768402.86624874, 'kernel': 'rbf', 'gamma': 2.754228703338175e-07}
0.662 (+/-0.315) for {'C': 23768402.86624874, 'kernel': 'rbf', 'gamma': 2.7542287033381744e-06}
0.664 (+/-0.317) for {'C': 23768402.86624874, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-05}
0.638 (+/-0.237) for {'C': 23768402.86624874, 'kernel': 'rbf', 'gamma': 0.00027542287033381743}
0.588 (+/-0.233) for {'C': 23768402.86624874, 'kernel': 'rbf', 'gamma': 0.0027542287033381747}
0.588 (+/-0.232) for {'C': 23768402.86624874, 'kernel': 'rbf', 'gamma': 0.027542287033381744}
0.614 (+/-0.237) for {'C': 23768402.86624874, 'kernel': 'rbf', 'gamma': 0.27542287033381746}
0.590 (+/-0.231) for {'C': 23768402.86624874, 'kernel': 'rbf', 'gamma': 2.754228703338169}
0.500 (+/-0.000) for {'C': 24210290.467361808, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-10}
0.617 (+/-0.238) for {'C': 24210290.467361808, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-09}
0.532 (+/-0.208) for {'C': 24210290.467361808, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-08}
0.656 (+/-0.309) for {'C': 24210290.467361808, 'kernel': 'rbf', 'gamma': 2.754228703338175e-07}
0.662 (+/-0.315) for {'C': 24210290.467361808, 'kernel': 'rbf', 'gamma': 2.7542287033381744e-06}
0.664 (+/-0.317) for {'C': 24210290.467361808, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-05}
0.638 (+/-0.237) for {'C': 24210290.467361808, 'kernel': 'rbf', 'gamma': 0.00027542287033381743}
0.588 (+/-0.233) for {'C': 24210290.467361808, 'kernel': 'rbf', 'gamma': 0.0027542287033381747}
0.588 (+/-0.232) for {'C': 24210290.467361808, 'kernel': 'rbf', 'gamma': 0.027542287033381744}
0.614 (+/-0.237) for {'C': 24210290.467361808, 'kernel': 'rbf', 'gamma': 0.27542287033381746}
0.590 (+/-0.231) for {'C': 24210290.467361808, 'kernel': 'rbf', 'gamma': 2.754228703338169}
0.500 (+/-0.000) for {'C': 24660393.37234341, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-10}
0.617 (+/-0.238) for {'C': 24660393.37234341, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-09}
0.531 (+/-0.207) for {'C': 24660393.37234341, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-08}
0.656 (+/-0.309) for {'C': 24660393.37234341, 'kernel': 'rbf', 'gamma': 2.754228703338175e-07}
0.662 (+/-0.315) for {'C': 24660393.37234341, 'kernel': 'rbf', 'gamma': 2.7542287033381744e-06}
0.664 (+/-0.317) for {'C': 24660393.37234341, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-05}
0.638 (+/-0.237) for {'C': 24660393.37234341, 'kernel': 'rbf', 'gamma': 0.00027542287033381743}
0.588 (+/-0.233) for {'C': 24660393.37234341, 'kernel': 'rbf', 'gamma': 0.0027542287033381747}
0.588 (+/-0.232) for {'C': 24660393.37234341, 'kernel': 'rbf', 'gamma': 0.027542287033381744}
0.614 (+/-0.237) for {'C': 24660393.37234341, 'kernel': 'rbf', 'gamma': 0.27542287033381746}
0.590 (+/-0.231) for {'C': 24660393.37234341, 'kernel': 'rbf', 'gamma': 2.754228703338169}
0.500 (+/-0.000) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-10}
0.617 (+/-0.238) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-09}
0.531 (+/-0.208) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-08}
0.656 (+/-0.309) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.754228703338175e-07}
0.662 (+/-0.315) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.7542287033381744e-06}
0.664 (+/-0.317) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-05}
0.638 (+/-0.237) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.00027542287033381743}
0.588 (+/-0.233) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.0027542287033381747}
0.588 (+/-0.232) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.027542287033381744}
0.614 (+/-0.237) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.27542287033381746}
0.590 (+/-0.231) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.754228703338169}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.666 (+/-0.315) for {'C': 10.0, 'kernel': 'linear'}
0.663 (+/-0.316) for {'C': 100.0, 'kernel': 'linear'}
0.613 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.588 (+/-0.233) for {'C': 10000.0, 'kernel': 'linear'}
0.588 (+/-0.233) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.10      0.17      0.12         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 22490546.058357812, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-05}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.688941792398384
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-10}
0.747 (+/-0.502) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-09}
0.548 (+/-0.300) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-08}
0.599 (+/-0.302) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.754228703338175e-07}
0.621 (+/-0.318) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.7542287033381744e-06}
0.664 (+/-0.299) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-05}
0.634 (+/-0.308) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 0.00027542287033381743}
0.614 (+/-0.327) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 0.0027542287033381747}
0.614 (+/-0.327) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 0.027542287033381744}
0.614 (+/-0.308) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 0.27542287033381746}
0.672 (+/-0.451) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.754228703338169}
0.496 (+/-0.001) for {'C': 21281390.459827095, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-10}
0.747 (+/-0.502) for {'C': 21281390.459827095, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-09}
0.547 (+/-0.300) for {'C': 21281390.459827095, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-08}
0.623 (+/-0.379) for {'C': 21281390.459827095, 'kernel': 'rbf', 'gamma': 2.754228703338175e-07}
0.621 (+/-0.318) for {'C': 21281390.459827095, 'kernel': 'rbf', 'gamma': 2.7542287033381744e-06}
0.666 (+/-0.297) for {'C': 21281390.459827095, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-05}
0.634 (+/-0.308) for {'C': 21281390.459827095, 'kernel': 'rbf', 'gamma': 0.00027542287033381743}
0.614 (+/-0.327) for {'C': 21281390.459827095, 'kernel': 'rbf', 'gamma': 0.0027542287033381747}
0.614 (+/-0.327) for {'C': 21281390.459827095, 'kernel': 'rbf', 'gamma': 0.027542287033381744}
0.614 (+/-0.308) for {'C': 21281390.459827095, 'kernel': 'rbf', 'gamma': 0.27542287033381746}
0.672 (+/-0.451) for {'C': 21281390.459827095, 'kernel': 'rbf', 'gamma': 2.754228703338169}
0.496 (+/-0.001) for {'C': 21677041.04819691, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-10}
0.747 (+/-0.502) for {'C': 21677041.04819691, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-09}
0.547 (+/-0.300) for {'C': 21677041.04819691, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-08}
0.599 (+/-0.302) for {'C': 21677041.04819691, 'kernel': 'rbf', 'gamma': 2.754228703338175e-07}
0.621 (+/-0.318) for {'C': 21677041.04819691, 'kernel': 'rbf', 'gamma': 2.7542287033381744e-06}
0.666 (+/-0.297) for {'C': 21677041.04819691, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-05}
0.634 (+/-0.308) for {'C': 21677041.04819691, 'kernel': 'rbf', 'gamma': 0.00027542287033381743}
0.614 (+/-0.327) for {'C': 21677041.04819691, 'kernel': 'rbf', 'gamma': 0.0027542287033381747}
0.614 (+/-0.327) for {'C': 21677041.04819691, 'kernel': 'rbf', 'gamma': 0.027542287033381744}
0.614 (+/-0.308) for {'C': 21677041.04819691, 'kernel': 'rbf', 'gamma': 0.27542287033381746}
0.672 (+/-0.451) for {'C': 21677041.04819691, 'kernel': 'rbf', 'gamma': 2.754228703338169}
0.496 (+/-0.001) for {'C': 22080047.33018901, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-10}
0.747 (+/-0.502) for {'C': 22080047.33018901, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-09}
0.547 (+/-0.300) for {'C': 22080047.33018901, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-08}
0.624 (+/-0.379) for {'C': 22080047.33018901, 'kernel': 'rbf', 'gamma': 2.754228703338175e-07}
0.621 (+/-0.318) for {'C': 22080047.33018901, 'kernel': 'rbf', 'gamma': 2.7542287033381744e-06}
0.674 (+/-0.311) for {'C': 22080047.33018901, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-05}
0.634 (+/-0.308) for {'C': 22080047.33018901, 'kernel': 'rbf', 'gamma': 0.00027542287033381743}
0.614 (+/-0.327) for {'C': 22080047.33018901, 'kernel': 'rbf', 'gamma': 0.0027542287033381747}
0.614 (+/-0.327) for {'C': 22080047.33018901, 'kernel': 'rbf', 'gamma': 0.027542287033381744}
0.614 (+/-0.308) for {'C': 22080047.33018901, 'kernel': 'rbf', 'gamma': 0.27542287033381746}
0.672 (+/-0.451) for {'C': 22080047.33018901, 'kernel': 'rbf', 'gamma': 2.754228703338169}
0.496 (+/-0.001) for {'C': 22490546.058357812, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-10}
0.747 (+/-0.502) for {'C': 22490546.058357812, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-09}
0.547 (+/-0.300) for {'C': 22490546.058357812, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-08}
0.598 (+/-0.302) for {'C': 22490546.058357812, 'kernel': 'rbf', 'gamma': 2.754228703338175e-07}
0.621 (+/-0.318) for {'C': 22490546.058357812, 'kernel': 'rbf', 'gamma': 2.7542287033381744e-06}
0.683 (+/-0.314) for {'C': 22490546.058357812, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-05}
0.634 (+/-0.308) for {'C': 22490546.058357812, 'kernel': 'rbf', 'gamma': 0.00027542287033381743}
0.614 (+/-0.327) for {'C': 22490546.058357812, 'kernel': 'rbf', 'gamma': 0.0027542287033381747}
0.614 (+/-0.327) for {'C': 22490546.058357812, 'kernel': 'rbf', 'gamma': 0.027542287033381744}
0.614 (+/-0.308) for {'C': 22490546.058357812, 'kernel': 'rbf', 'gamma': 0.27542287033381746}
0.672 (+/-0.451) for {'C': 22490546.058357812, 'kernel': 'rbf', 'gamma': 2.754228703338169}
0.496 (+/-0.001) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-10}
0.747 (+/-0.502) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-09}
0.548 (+/-0.299) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-08}
0.623 (+/-0.379) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.754228703338175e-07}
0.621 (+/-0.318) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.7542287033381744e-06}
0.683 (+/-0.314) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-05}
0.634 (+/-0.308) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 0.00027542287033381743}
0.614 (+/-0.327) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 0.0027542287033381747}
0.614 (+/-0.327) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 0.027542287033381744}
0.614 (+/-0.308) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 0.27542287033381746}
0.672 (+/-0.451) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.754228703338169}
0.496 (+/-0.001) for {'C': 23334580.622810014, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-10}
0.747 (+/-0.502) for {'C': 23334580.622810014, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-09}
0.547 (+/-0.300) for {'C': 23334580.622810014, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-08}
0.600 (+/-0.302) for {'C': 23334580.622810014, 'kernel': 'rbf', 'gamma': 2.754228703338175e-07}
0.621 (+/-0.318) for {'C': 23334580.622810014, 'kernel': 'rbf', 'gamma': 2.7542287033381744e-06}
0.683 (+/-0.314) for {'C': 23334580.622810014, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-05}
0.634 (+/-0.308) for {'C': 23334580.622810014, 'kernel': 'rbf', 'gamma': 0.00027542287033381743}
0.614 (+/-0.327) for {'C': 23334580.622810014, 'kernel': 'rbf', 'gamma': 0.0027542287033381747}
0.614 (+/-0.327) for {'C': 23334580.622810014, 'kernel': 'rbf', 'gamma': 0.027542287033381744}
0.614 (+/-0.308) for {'C': 23334580.622810014, 'kernel': 'rbf', 'gamma': 0.27542287033381746}
0.672 (+/-0.451) for {'C': 23334580.622810014, 'kernel': 'rbf', 'gamma': 2.754228703338169}
0.496 (+/-0.001) for {'C': 23768402.86624874, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-10}
0.747 (+/-0.502) for {'C': 23768402.86624874, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-09}
0.547 (+/-0.300) for {'C': 23768402.86624874, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-08}
0.598 (+/-0.302) for {'C': 23768402.86624874, 'kernel': 'rbf', 'gamma': 2.754228703338175e-07}
0.621 (+/-0.318) for {'C': 23768402.86624874, 'kernel': 'rbf', 'gamma': 2.7542287033381744e-06}
0.658 (+/-0.329) for {'C': 23768402.86624874, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-05}
0.634 (+/-0.308) for {'C': 23768402.86624874, 'kernel': 'rbf', 'gamma': 0.00027542287033381743}
0.614 (+/-0.327) for {'C': 23768402.86624874, 'kernel': 'rbf', 'gamma': 0.0027542287033381747}
0.614 (+/-0.327) for {'C': 23768402.86624874, 'kernel': 'rbf', 'gamma': 0.027542287033381744}
0.614 (+/-0.308) for {'C': 23768402.86624874, 'kernel': 'rbf', 'gamma': 0.27542287033381746}
0.672 (+/-0.451) for {'C': 23768402.86624874, 'kernel': 'rbf', 'gamma': 2.754228703338169}
0.496 (+/-0.001) for {'C': 24210290.467361808, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-10}
0.747 (+/-0.502) for {'C': 24210290.467361808, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-09}
0.548 (+/-0.299) for {'C': 24210290.467361808, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-08}
0.600 (+/-0.302) for {'C': 24210290.467361808, 'kernel': 'rbf', 'gamma': 2.754228703338175e-07}
0.621 (+/-0.318) for {'C': 24210290.467361808, 'kernel': 'rbf', 'gamma': 2.7542287033381744e-06}
0.660 (+/-0.327) for {'C': 24210290.467361808, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-05}
0.634 (+/-0.308) for {'C': 24210290.467361808, 'kernel': 'rbf', 'gamma': 0.00027542287033381743}
0.614 (+/-0.327) for {'C': 24210290.467361808, 'kernel': 'rbf', 'gamma': 0.0027542287033381747}
0.614 (+/-0.327) for {'C': 24210290.467361808, 'kernel': 'rbf', 'gamma': 0.027542287033381744}
0.614 (+/-0.308) for {'C': 24210290.467361808, 'kernel': 'rbf', 'gamma': 0.27542287033381746}
0.672 (+/-0.451) for {'C': 24210290.467361808, 'kernel': 'rbf', 'gamma': 2.754228703338169}
0.496 (+/-0.001) for {'C': 24660393.37234341, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-10}
0.747 (+/-0.502) for {'C': 24660393.37234341, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-09}
0.547 (+/-0.300) for {'C': 24660393.37234341, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-08}
0.598 (+/-0.302) for {'C': 24660393.37234341, 'kernel': 'rbf', 'gamma': 2.754228703338175e-07}
0.621 (+/-0.318) for {'C': 24660393.37234341, 'kernel': 'rbf', 'gamma': 2.7542287033381744e-06}
0.660 (+/-0.327) for {'C': 24660393.37234341, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-05}
0.634 (+/-0.308) for {'C': 24660393.37234341, 'kernel': 'rbf', 'gamma': 0.00027542287033381743}
0.614 (+/-0.327) for {'C': 24660393.37234341, 'kernel': 'rbf', 'gamma': 0.0027542287033381747}
0.614 (+/-0.327) for {'C': 24660393.37234341, 'kernel': 'rbf', 'gamma': 0.027542287033381744}
0.614 (+/-0.308) for {'C': 24660393.37234341, 'kernel': 'rbf', 'gamma': 0.27542287033381746}
0.672 (+/-0.451) for {'C': 24660393.37234341, 'kernel': 'rbf', 'gamma': 2.754228703338169}
0.496 (+/-0.001) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-10}
0.747 (+/-0.502) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-09}
0.547 (+/-0.300) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-08}
0.598 (+/-0.302) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.754228703338175e-07}
0.621 (+/-0.318) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.7542287033381744e-06}
0.660 (+/-0.327) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-05}
0.634 (+/-0.308) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.00027542287033381743}
0.614 (+/-0.327) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.0027542287033381747}
0.614 (+/-0.327) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.027542287033381744}
0.614 (+/-0.308) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.27542287033381746}
0.672 (+/-0.451) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.754228703338169}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.673 (+/-0.377) for {'C': 10.0, 'kernel': 'linear'}
0.637 (+/-0.310) for {'C': 100.0, 'kernel': 'linear'}
0.622 (+/-0.319) for {'C': 1000.0, 'kernel': 'linear'}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'linear'}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.20      0.17      0.18         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.99      0.99       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-10}
0.617 (+/-0.238) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-09}
0.470 (+/-0.282) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-08}
0.652 (+/-0.302) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.754228703338175e-07}
0.654 (+/-0.311) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.7542287033381744e-06}
0.664 (+/-0.224) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-05}
0.638 (+/-0.237) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 0.00027542287033381743}
0.588 (+/-0.233) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 0.0027542287033381747}
0.588 (+/-0.232) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 0.027542287033381744}
0.614 (+/-0.237) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 0.27542287033381746}
0.590 (+/-0.231) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.754228703338169}
0.500 (+/-0.000) for {'C': 21281390.459827095, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-10}
0.617 (+/-0.238) for {'C': 21281390.459827095, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-09}
0.465 (+/-0.288) for {'C': 21281390.459827095, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-08}
0.652 (+/-0.302) for {'C': 21281390.459827095, 'kernel': 'rbf', 'gamma': 2.754228703338175e-07}
0.654 (+/-0.311) for {'C': 21281390.459827095, 'kernel': 'rbf', 'gamma': 2.7542287033381744e-06}
0.664 (+/-0.224) for {'C': 21281390.459827095, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-05}
0.638 (+/-0.237) for {'C': 21281390.459827095, 'kernel': 'rbf', 'gamma': 0.00027542287033381743}
0.588 (+/-0.233) for {'C': 21281390.459827095, 'kernel': 'rbf', 'gamma': 0.0027542287033381747}
0.588 (+/-0.232) for {'C': 21281390.459827095, 'kernel': 'rbf', 'gamma': 0.027542287033381744}
0.614 (+/-0.237) for {'C': 21281390.459827095, 'kernel': 'rbf', 'gamma': 0.27542287033381746}
0.590 (+/-0.231) for {'C': 21281390.459827095, 'kernel': 'rbf', 'gamma': 2.754228703338169}
0.500 (+/-0.000) for {'C': 21677041.04819691, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-10}
0.617 (+/-0.238) for {'C': 21677041.04819691, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-09}
0.468 (+/-0.283) for {'C': 21677041.04819691, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-08}
0.652 (+/-0.302) for {'C': 21677041.04819691, 'kernel': 'rbf', 'gamma': 2.754228703338175e-07}
0.654 (+/-0.311) for {'C': 21677041.04819691, 'kernel': 'rbf', 'gamma': 2.7542287033381744e-06}
0.664 (+/-0.224) for {'C': 21677041.04819691, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-05}
0.638 (+/-0.237) for {'C': 21677041.04819691, 'kernel': 'rbf', 'gamma': 0.00027542287033381743}
0.588 (+/-0.233) for {'C': 21677041.04819691, 'kernel': 'rbf', 'gamma': 0.0027542287033381747}
0.588 (+/-0.232) for {'C': 21677041.04819691, 'kernel': 'rbf', 'gamma': 0.027542287033381744}
0.614 (+/-0.237) for {'C': 21677041.04819691, 'kernel': 'rbf', 'gamma': 0.27542287033381746}
0.590 (+/-0.231) for {'C': 21677041.04819691, 'kernel': 'rbf', 'gamma': 2.754228703338169}
0.500 (+/-0.000) for {'C': 22080047.33018901, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-10}
0.617 (+/-0.238) for {'C': 22080047.33018901, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-09}
0.463 (+/-0.290) for {'C': 22080047.33018901, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-08}
0.653 (+/-0.303) for {'C': 22080047.33018901, 'kernel': 'rbf', 'gamma': 2.754228703338175e-07}
0.654 (+/-0.311) for {'C': 22080047.33018901, 'kernel': 'rbf', 'gamma': 2.7542287033381744e-06}
0.689 (+/-0.300) for {'C': 22080047.33018901, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-05}
0.638 (+/-0.237) for {'C': 22080047.33018901, 'kernel': 'rbf', 'gamma': 0.00027542287033381743}
0.588 (+/-0.233) for {'C': 22080047.33018901, 'kernel': 'rbf', 'gamma': 0.0027542287033381747}
0.588 (+/-0.232) for {'C': 22080047.33018901, 'kernel': 'rbf', 'gamma': 0.027542287033381744}
0.614 (+/-0.237) for {'C': 22080047.33018901, 'kernel': 'rbf', 'gamma': 0.27542287033381746}
0.590 (+/-0.231) for {'C': 22080047.33018901, 'kernel': 'rbf', 'gamma': 2.754228703338169}
0.500 (+/-0.000) for {'C': 22490546.058357812, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-10}
0.617 (+/-0.238) for {'C': 22490546.058357812, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-09}
0.464 (+/-0.286) for {'C': 22490546.058357812, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-08}
0.652 (+/-0.302) for {'C': 22490546.058357812, 'kernel': 'rbf', 'gamma': 2.754228703338175e-07}
0.654 (+/-0.311) for {'C': 22490546.058357812, 'kernel': 'rbf', 'gamma': 2.7542287033381744e-06}
0.689 (+/-0.300) for {'C': 22490546.058357812, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-05}
0.638 (+/-0.237) for {'C': 22490546.058357812, 'kernel': 'rbf', 'gamma': 0.00027542287033381743}
0.588 (+/-0.233) for {'C': 22490546.058357812, 'kernel': 'rbf', 'gamma': 0.0027542287033381747}
0.588 (+/-0.232) for {'C': 22490546.058357812, 'kernel': 'rbf', 'gamma': 0.027542287033381744}
0.614 (+/-0.237) for {'C': 22490546.058357812, 'kernel': 'rbf', 'gamma': 0.27542287033381746}
0.590 (+/-0.231) for {'C': 22490546.058357812, 'kernel': 'rbf', 'gamma': 2.754228703338169}
0.500 (+/-0.000) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-10}
0.617 (+/-0.238) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-09}
0.487 (+/-0.291) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-08}
0.652 (+/-0.302) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.754228703338175e-07}
0.654 (+/-0.311) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.7542287033381744e-06}
0.689 (+/-0.300) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-05}
0.638 (+/-0.237) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 0.00027542287033381743}
0.588 (+/-0.233) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 0.0027542287033381747}
0.588 (+/-0.232) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 0.027542287033381744}
0.614 (+/-0.237) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 0.27542287033381746}
0.590 (+/-0.231) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.754228703338169}
0.500 (+/-0.000) for {'C': 23334580.622810014, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-10}
0.617 (+/-0.238) for {'C': 23334580.622810014, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-09}
0.463 (+/-0.289) for {'C': 23334580.622810014, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-08}
0.653 (+/-0.303) for {'C': 23334580.622810014, 'kernel': 'rbf', 'gamma': 2.754228703338175e-07}
0.654 (+/-0.311) for {'C': 23334580.622810014, 'kernel': 'rbf', 'gamma': 2.7542287033381744e-06}
0.689 (+/-0.300) for {'C': 23334580.622810014, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-05}
0.638 (+/-0.237) for {'C': 23334580.622810014, 'kernel': 'rbf', 'gamma': 0.00027542287033381743}
0.588 (+/-0.233) for {'C': 23334580.622810014, 'kernel': 'rbf', 'gamma': 0.0027542287033381747}
0.588 (+/-0.232) for {'C': 23334580.622810014, 'kernel': 'rbf', 'gamma': 0.027542287033381744}
0.614 (+/-0.237) for {'C': 23334580.622810014, 'kernel': 'rbf', 'gamma': 0.27542287033381746}
0.590 (+/-0.231) for {'C': 23334580.622810014, 'kernel': 'rbf', 'gamma': 2.754228703338169}
0.500 (+/-0.000) for {'C': 23768402.86624874, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-10}
0.617 (+/-0.238) for {'C': 23768402.86624874, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-09}
0.461 (+/-0.290) for {'C': 23768402.86624874, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-08}
0.652 (+/-0.302) for {'C': 23768402.86624874, 'kernel': 'rbf', 'gamma': 2.754228703338175e-07}
0.654 (+/-0.311) for {'C': 23768402.86624874, 'kernel': 'rbf', 'gamma': 2.7542287033381744e-06}
0.664 (+/-0.317) for {'C': 23768402.86624874, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-05}
0.638 (+/-0.237) for {'C': 23768402.86624874, 'kernel': 'rbf', 'gamma': 0.00027542287033381743}
0.588 (+/-0.233) for {'C': 23768402.86624874, 'kernel': 'rbf', 'gamma': 0.0027542287033381747}
0.588 (+/-0.232) for {'C': 23768402.86624874, 'kernel': 'rbf', 'gamma': 0.027542287033381744}
0.614 (+/-0.237) for {'C': 23768402.86624874, 'kernel': 'rbf', 'gamma': 0.27542287033381746}
0.590 (+/-0.231) for {'C': 23768402.86624874, 'kernel': 'rbf', 'gamma': 2.754228703338169}
0.500 (+/-0.000) for {'C': 24210290.467361808, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-10}
0.617 (+/-0.238) for {'C': 24210290.467361808, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-09}
0.483 (+/-0.294) for {'C': 24210290.467361808, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-08}
0.653 (+/-0.303) for {'C': 24210290.467361808, 'kernel': 'rbf', 'gamma': 2.754228703338175e-07}
0.654 (+/-0.311) for {'C': 24210290.467361808, 'kernel': 'rbf', 'gamma': 2.7542287033381744e-06}
0.664 (+/-0.317) for {'C': 24210290.467361808, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-05}
0.638 (+/-0.237) for {'C': 24210290.467361808, 'kernel': 'rbf', 'gamma': 0.00027542287033381743}
0.588 (+/-0.233) for {'C': 24210290.467361808, 'kernel': 'rbf', 'gamma': 0.0027542287033381747}
0.588 (+/-0.232) for {'C': 24210290.467361808, 'kernel': 'rbf', 'gamma': 0.027542287033381744}
0.614 (+/-0.237) for {'C': 24210290.467361808, 'kernel': 'rbf', 'gamma': 0.27542287033381746}
0.590 (+/-0.231) for {'C': 24210290.467361808, 'kernel': 'rbf', 'gamma': 2.754228703338169}
0.500 (+/-0.000) for {'C': 24660393.37234341, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-10}
0.617 (+/-0.238) for {'C': 24660393.37234341, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-09}
0.459 (+/-0.292) for {'C': 24660393.37234341, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-08}
0.652 (+/-0.302) for {'C': 24660393.37234341, 'kernel': 'rbf', 'gamma': 2.754228703338175e-07}
0.654 (+/-0.311) for {'C': 24660393.37234341, 'kernel': 'rbf', 'gamma': 2.7542287033381744e-06}
0.664 (+/-0.317) for {'C': 24660393.37234341, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-05}
0.638 (+/-0.237) for {'C': 24660393.37234341, 'kernel': 'rbf', 'gamma': 0.00027542287033381743}
0.588 (+/-0.233) for {'C': 24660393.37234341, 'kernel': 'rbf', 'gamma': 0.0027542287033381747}
0.588 (+/-0.232) for {'C': 24660393.37234341, 'kernel': 'rbf', 'gamma': 0.027542287033381744}
0.614 (+/-0.237) for {'C': 24660393.37234341, 'kernel': 'rbf', 'gamma': 0.27542287033381746}
0.590 (+/-0.231) for {'C': 24660393.37234341, 'kernel': 'rbf', 'gamma': 2.754228703338169}
0.500 (+/-0.000) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-10}
0.617 (+/-0.238) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-09}
0.458 (+/-0.293) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-08}
0.652 (+/-0.302) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.754228703338175e-07}
0.654 (+/-0.311) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.7542287033381744e-06}
0.664 (+/-0.317) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-05}
0.638 (+/-0.237) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.00027542287033381743}
0.588 (+/-0.233) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.0027542287033381747}
0.588 (+/-0.232) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.027542287033381744}
0.614 (+/-0.237) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.27542287033381746}
0.590 (+/-0.231) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.754228703338169}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.663 (+/-0.316) for {'C': 10.0, 'kernel': 'linear'}
0.662 (+/-0.315) for {'C': 100.0, 'kernel': 'linear'}
0.613 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.588 (+/-0.233) for {'C': 10000.0, 'kernel': 'linear'}
0.588 (+/-0.233) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.10      0.17      0.12         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 22490546.058357812, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-05}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.688941792398384
第2轮loop中，tunemodel函数得到的最优参数是： ([{'C': array([22080047.33018901, 22161543.25959723, 22243339.98485111,
       22325438.61616745, 22407840.26786055, 22490546.05835781,
       22573557.11021449, 22656874.55012912, 22740499.50895897,
       22824433.12173501, 22908676.52767773]), 'kernel': ['rbf'], 'gamma': array([2.75422870e-06, 4.36515832e-06, 6.91830971e-06, 1.09647820e-05,
       1.73780083e-05, 2.75422870e-05, 4.36515832e-05, 6.91830971e-05,
       1.09647820e-04, 1.73780083e-04, 2.75422870e-04])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}], {'C': 22490546.058357812, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-05}, 0.688941792398384)
这是第2次迭代微调C和gamma。
第2次迭代，得到delta： [4.18130469e+05 5.08219768e-20]
SVC模型clf_op_iter的参数是： {'kernel': 'rbf', 'decision_function_shape': 'ovr', 'class_weight': {-1: 15}, 'tol': 0.001, 'gamma': 2.7542287033381745e-05, 'random_state': 0, 'cache_size': 200, 'verbose': False, 'degree': 3, 'C': 22490546.058357812, 'probability': False, 'shrinking': True, 'coef0': 0.0, 'max_iter': -1}

训练模型SVC对训练样本的预测准确率： 0.9581856839121191

测试集中，预测为舞弊样本的有： (array([1252, 1255], dtype=int64),) 
测试集中，实际为舞弊样本的有： (array([1246, 1247, 1248, 1249, 1250, 1251, 1252, 1253, 1254, 1255, 1256],
      dtype=int64),) 
预测的舞弊样本数目： 2

训练模型SVC对测试样本的预测准确率： 0.9043231750531537
以上是第27次特征筛选。
第27次特征筛选，AUC值是： 0.5909090909090908
X_train_iter_svc.shape is: (1257, 25)
X_test_iter_svc.shape is: (1257, 25)
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.639 (+/-0.326) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.747 (+/-0.449) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.17      0.17      0.17         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.614 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.641 (+/-0.236) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.17      0.17      0.17         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6413461538461539
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.624 (+/-0.318) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.20      0.17      0.18         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.99      0.99       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.614 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.20      0.17      0.18         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.99      0.99       629

本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6663461538461538
RBF的粗grid search最佳超参： {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001} RBF的粗grid search最高分值： 0.6413461538461539
linear的粗grid search最佳超参： {'C': 1.0, 'kernel': 'linear'} linear的粗grid search最高分值： 0.6663461538461538
粗grid search得到的parameter是：
 [{'C': array([1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03, 1.e+04, 1.e+05,
       1.e+06, 1.e+07, 1.e+08]), 'kernel': ['rbf'], 'gamma': array([1.e-08, 1.e-07, 1.e-06, 1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01,
       1.e+00, 1.e+01, 1.e+02])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.449) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.639 (+/-0.326) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.631 (+/-0.327) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.449) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.672 (+/-0.392) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.610 (+/-0.326) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.748 (+/-0.449) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.375) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.656 (+/-0.312) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.626 (+/-0.318) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.747 (+/-0.502) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.748 (+/-0.449) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.706 (+/-0.383) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.656 (+/-0.312) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.626 (+/-0.318) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.797 (+/-0.492) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.798 (+/-0.437) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.714 (+/-0.418) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.656 (+/-0.312) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.614 (+/-0.327) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.626 (+/-0.318) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.649 (+/-0.778) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.797 (+/-0.492) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.676 (+/-0.294) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.632 (+/-0.311) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.643 (+/-0.306) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.614 (+/-0.327) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.626 (+/-0.318) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.699 (+/-0.797) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.797 (+/-0.492) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.659 (+/-0.313) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.628 (+/-0.312) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.626 (+/-0.318) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.614 (+/-0.327) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.626 (+/-0.318) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.706 (+/-0.383) for {'C': 10.0, 'kernel': 'linear'}
0.641 (+/-0.311) for {'C': 100.0, 'kernel': 'linear'}
0.624 (+/-0.318) for {'C': 1000.0, 'kernel': 'linear'}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'linear'}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.006) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.641 (+/-0.236) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.641 (+/-0.236) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.589 (+/-0.230) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.316) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.641 (+/-0.236) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.639 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.617 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.666 (+/-0.316) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.316) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.588 (+/-0.232) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.642 (+/-0.236) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.683 (+/-0.296) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.666 (+/-0.317) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.317) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.588 (+/-0.232) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.588 (+/-0.233) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.617 (+/-0.238) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.642 (+/-0.236) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.681 (+/-0.295) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.660 (+/-0.318) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.639 (+/-0.237) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.588 (+/-0.233) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.588 (+/-0.233) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.642 (+/-0.236) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.642 (+/-0.236) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.664 (+/-0.316) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.660 (+/-0.315) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.613 (+/-0.241) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.588 (+/-0.233) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.588 (+/-0.233) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.666 (+/-0.315) for {'C': 10.0, 'kernel': 'linear'}
0.663 (+/-0.316) for {'C': 100.0, 'kernel': 'linear'}
0.614 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.588 (+/-0.233) for {'C': 10000.0, 'kernel': 'linear'}
0.588 (+/-0.233) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6830128205128205
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.698 (+/-0.383) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.629 (+/-0.329) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.714 (+/-0.359) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.618 (+/-0.322) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.631 (+/-0.327) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.723 (+/-0.358) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.661 (+/-0.384) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.610 (+/-0.326) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.714 (+/-0.351) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.660 (+/-0.385) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.656 (+/-0.312) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.626 (+/-0.318) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.747 (+/-0.502) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.797 (+/-0.492) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.710 (+/-0.363) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.662 (+/-0.384) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.656 (+/-0.312) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.626 (+/-0.318) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.024) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.797 (+/-0.492) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.658 (+/-0.290) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.658 (+/-0.388) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.656 (+/-0.312) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.614 (+/-0.327) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.626 (+/-0.318) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.699 (+/-0.797) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.681 (+/-0.372) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.646 (+/-0.298) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.628 (+/-0.312) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.643 (+/-0.306) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.614 (+/-0.327) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.626 (+/-0.318) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.699 (+/-0.797) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.668 (+/-0.371) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.625 (+/-0.315) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.628 (+/-0.312) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.626 (+/-0.318) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.614 (+/-0.327) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.626 (+/-0.318) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.671 (+/-0.378) for {'C': 10.0, 'kernel': 'linear'}
0.638 (+/-0.310) for {'C': 100.0, 'kernel': 'linear'}
0.624 (+/-0.318) for {'C': 1000.0, 'kernel': 'linear'}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'linear'}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.006) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.666 (+/-0.315) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.612 (+/-0.240) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.682 (+/-0.295) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.610 (+/-0.241) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.682 (+/-0.295) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.661 (+/-0.314) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.589 (+/-0.230) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.682 (+/-0.294) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.660 (+/-0.313) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.639 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.617 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.642 (+/-0.236) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.682 (+/-0.295) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.661 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.588 (+/-0.232) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.523 (+/-0.138) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.642 (+/-0.236) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.681 (+/-0.292) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.658 (+/-0.315) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.317) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.588 (+/-0.232) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.588 (+/-0.233) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.642 (+/-0.236) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.641 (+/-0.235) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.677 (+/-0.293) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.659 (+/-0.316) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.639 (+/-0.237) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.588 (+/-0.233) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.588 (+/-0.233) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.642 (+/-0.236) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.640 (+/-0.234) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.658 (+/-0.314) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.660 (+/-0.315) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.613 (+/-0.241) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.588 (+/-0.233) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.588 (+/-0.233) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.663 (+/-0.316) for {'C': 10.0, 'kernel': 'linear'}
0.663 (+/-0.316) for {'C': 100.0, 'kernel': 'linear'}
0.614 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.588 (+/-0.233) for {'C': 10000.0, 'kernel': 'linear'}
0.588 (+/-0.233) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.17      0.17      0.17         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6823717948717949
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.747 (+/-0.502) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.747 (+/-0.502) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.747 (+/-0.502) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.747 (+/-0.502) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.747 (+/-0.502) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.747 (+/-0.502) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.747 (+/-0.502) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.747 (+/-0.502) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.722 (+/-0.473) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.748 (+/-0.449) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.747 (+/-0.502) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.747 (+/-0.502) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.747 (+/-0.502) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.747 (+/-0.502) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.747 (+/-0.502) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.747 (+/-0.502) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.747 (+/-0.502) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.747 (+/-0.502) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.797 (+/-0.492) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.748 (+/-0.449) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.773 (+/-0.423) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.747 (+/-0.502) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.747 (+/-0.502) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.797 (+/-0.492) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.747 (+/-0.502) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.747 (+/-0.502) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.747 (+/-0.502) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.747 (+/-0.502) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.797 (+/-0.492) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.748 (+/-0.449) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.731 (+/-0.422) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.723 (+/-0.423) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.747 (+/-0.502) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.797 (+/-0.492) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.797 (+/-0.492) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.747 (+/-0.502) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.797 (+/-0.492) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.797 (+/-0.492) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.722 (+/-0.473) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.748 (+/-0.449) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.723 (+/-0.423) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.723 (+/-0.423) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.698 (+/-0.383) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.747 (+/-0.502) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.797 (+/-0.492) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.797 (+/-0.492) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.797 (+/-0.492) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.797 (+/-0.492) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.798 (+/-0.492) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.697 (+/-0.437) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.731 (+/-0.422) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.673 (+/-0.330) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.714 (+/-0.418) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.697 (+/-0.375) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.797 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.797 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.798 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.797 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.748 (+/-0.449) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.798 (+/-0.437) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.722 (+/-0.417) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.689 (+/-0.382) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.698 (+/-0.383) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.689 (+/-0.375) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.714 (+/-0.418) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.797 (+/-0.492) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.798 (+/-0.492) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.798 (+/-0.492) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.797 (+/-0.492) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.748 (+/-0.449) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.798 (+/-0.437) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.689 (+/-0.374) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.698 (+/-0.383) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.656 (+/-0.312) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.681 (+/-0.373) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.708 (+/-0.423) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.797 (+/-0.492) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.798 (+/-0.492) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.773 (+/-0.474) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.797 (+/-0.492) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.774 (+/-0.458) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.694 (+/-0.354) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.689 (+/-0.375) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.706 (+/-0.383) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.681 (+/-0.380) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.671 (+/-0.379) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.671 (+/-0.380) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.797 (+/-0.492) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.798 (+/-0.492) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.764 (+/-0.478) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.714 (+/-0.418) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.691 (+/-0.364) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.694 (+/-0.354) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.689 (+/-0.375) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.660 (+/-0.318) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.673 (+/-0.381) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.670 (+/-0.381) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.643 (+/-0.320) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.797 (+/-0.492) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.798 (+/-0.492) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.806 (+/-0.473) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.702 (+/-0.420) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.684 (+/-0.353) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.669 (+/-0.295) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.698 (+/-0.376) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.652 (+/-0.313) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.665 (+/-0.383) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.669 (+/-0.381) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.634 (+/-0.311) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.797 (+/-0.492) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.798 (+/-0.492) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.753 (+/-0.491) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.702 (+/-0.421) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.666 (+/-0.354) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.676 (+/-0.294) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.681 (+/-0.373) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.653 (+/-0.329) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.630 (+/-0.313) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.668 (+/-0.382) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.632 (+/-0.311) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.706 (+/-0.383) for {'C': 10.0, 'kernel': 'linear'}
0.641 (+/-0.311) for {'C': 100.0, 'kernel': 'linear'}
0.624 (+/-0.318) for {'C': 1000.0, 'kernel': 'linear'}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'linear'}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.617 (+/-0.238) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.617 (+/-0.238) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.617 (+/-0.238) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.617 (+/-0.238) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.617 (+/-0.238) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.617 (+/-0.238) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.617 (+/-0.238) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.617 (+/-0.238) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.617 (+/-0.238) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.666 (+/-0.316) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.617 (+/-0.238) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.617 (+/-0.238) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.617 (+/-0.238) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.617 (+/-0.238) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.617 (+/-0.238) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.617 (+/-0.238) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.617 (+/-0.238) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.617 (+/-0.238) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.642 (+/-0.236) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.666 (+/-0.316) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.683 (+/-0.295) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.617 (+/-0.238) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.617 (+/-0.238) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.642 (+/-0.236) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.617 (+/-0.238) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.617 (+/-0.238) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.617 (+/-0.238) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.617 (+/-0.238) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.642 (+/-0.236) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.666 (+/-0.316) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.666 (+/-0.315) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.666 (+/-0.315) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.617 (+/-0.238) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.642 (+/-0.236) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.642 (+/-0.236) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.617 (+/-0.238) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.642 (+/-0.236) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.642 (+/-0.236) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.617 (+/-0.238) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.666 (+/-0.316) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.666 (+/-0.315) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.666 (+/-0.315) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.666 (+/-0.315) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.617 (+/-0.238) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.642 (+/-0.236) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.642 (+/-0.236) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.642 (+/-0.236) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.642 (+/-0.236) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.667 (+/-0.316) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.616 (+/-0.237) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.666 (+/-0.315) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.666 (+/-0.315) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.666 (+/-0.316) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.641 (+/-0.236) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.642 (+/-0.236) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.642 (+/-0.236) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.667 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.642 (+/-0.236) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.666 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.683 (+/-0.296) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.641 (+/-0.235) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.666 (+/-0.315) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.666 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.666 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.666 (+/-0.317) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.642 (+/-0.236) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.667 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.667 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.642 (+/-0.236) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.666 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.683 (+/-0.296) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.641 (+/-0.235) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.666 (+/-0.315) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.665 (+/-0.315) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.665 (+/-0.317) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.664 (+/-0.319) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.642 (+/-0.236) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.667 (+/-0.316) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.667 (+/-0.316) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.642 (+/-0.236) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.682 (+/-0.295) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.682 (+/-0.294) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.666 (+/-0.316) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.666 (+/-0.316) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.664 (+/-0.315) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.664 (+/-0.317) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.663 (+/-0.319) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.642 (+/-0.236) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.667 (+/-0.316) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.666 (+/-0.316) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.641 (+/-0.235) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.682 (+/-0.295) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.698 (+/-0.304) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.665 (+/-0.316) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.665 (+/-0.315) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.663 (+/-0.316) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.662 (+/-0.320) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.661 (+/-0.319) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.642 (+/-0.236) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.667 (+/-0.316) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.683 (+/-0.296) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.641 (+/-0.235) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.681 (+/-0.291) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.697 (+/-0.305) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.665 (+/-0.317) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.664 (+/-0.315) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.660 (+/-0.318) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.661 (+/-0.321) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.661 (+/-0.318) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.642 (+/-0.236) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.667 (+/-0.316) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.657 (+/-0.310) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.666 (+/-0.315) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.680 (+/-0.290) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.681 (+/-0.295) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.664 (+/-0.318) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.664 (+/-0.316) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.659 (+/-0.318) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.660 (+/-0.322) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.660 (+/-0.318) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.666 (+/-0.315) for {'C': 10.0, 'kernel': 'linear'}
0.663 (+/-0.316) for {'C': 100.0, 'kernel': 'linear'}
0.614 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.588 (+/-0.233) for {'C': 10000.0, 'kernel': 'linear'}
0.588 (+/-0.233) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.17      0.17      0.17         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6979156360788193
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.747 (+/-0.502) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.747 (+/-0.502) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.797 (+/-0.492) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.797 (+/-0.492) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.797 (+/-0.492) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.797 (+/-0.492) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.797 (+/-0.492) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.798 (+/-0.492) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.748 (+/-0.449) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.689 (+/-0.382) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.710 (+/-0.363) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.747 (+/-0.502) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.797 (+/-0.492) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.797 (+/-0.492) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.797 (+/-0.492) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.798 (+/-0.492) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.798 (+/-0.492) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.798 (+/-0.492) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.798 (+/-0.437) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.681 (+/-0.372) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.714 (+/-0.351) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.723 (+/-0.358) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.747 (+/-0.502) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.797 (+/-0.492) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.797 (+/-0.492) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.797 (+/-0.492) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.798 (+/-0.492) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.798 (+/-0.492) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.748 (+/-0.449) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.739 (+/-0.398) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.701 (+/-0.351) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.689 (+/-0.374) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.677 (+/-0.374) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.747 (+/-0.502) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.797 (+/-0.492) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.798 (+/-0.492) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.798 (+/-0.492) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.798 (+/-0.437) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.798 (+/-0.437) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.727 (+/-0.397) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.701 (+/-0.351) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.714 (+/-0.351) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.689 (+/-0.382) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.669 (+/-0.373) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.747 (+/-0.502) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.798 (+/-0.492) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.848 (+/-0.460) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.798 (+/-0.492) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.773 (+/-0.416) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.739 (+/-0.392) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.660 (+/-0.327) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.664 (+/-0.317) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.651 (+/-0.308) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.662 (+/-0.376) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.664 (+/-0.383) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.797 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.798 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.773 (+/-0.474) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.723 (+/-0.417) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.716 (+/-0.401) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.658 (+/-0.290) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.664 (+/-0.317) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.639 (+/-0.299) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.657 (+/-0.377) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.630 (+/-0.313) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.658 (+/-0.388) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.797 (+/-0.492) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.798 (+/-0.492) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.735 (+/-0.455) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.674 (+/-0.376) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.656 (+/-0.369) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.658 (+/-0.290) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.656 (+/-0.330) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.638 (+/-0.310) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.627 (+/-0.313) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.624 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.659 (+/-0.387) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.764 (+/-0.478) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.798 (+/-0.492) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.721 (+/-0.463) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.673 (+/-0.391) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.646 (+/-0.317) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.648 (+/-0.284) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.630 (+/-0.313) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.664 (+/-0.382) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.657 (+/-0.389) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.623 (+/-0.316) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.625 (+/-0.314) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.764 (+/-0.478) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.798 (+/-0.492) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.696 (+/-0.439) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.677 (+/-0.432) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.631 (+/-0.289) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.658 (+/-0.292) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.624 (+/-0.315) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.623 (+/-0.313) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.656 (+/-0.389) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.624 (+/-0.316) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.627 (+/-0.313) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.714 (+/-0.418) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.748 (+/-0.449) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.663 (+/-0.390) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.653 (+/-0.381) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.646 (+/-0.289) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.647 (+/-0.297) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.630 (+/-0.326) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.619 (+/-0.315) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.656 (+/-0.389) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.623 (+/-0.316) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.631 (+/-0.311) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.681 (+/-0.372) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.748 (+/-0.449) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.656 (+/-0.393) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.653 (+/-0.380) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.630 (+/-0.279) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.646 (+/-0.298) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.621 (+/-0.318) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.618 (+/-0.316) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.623 (+/-0.316) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.624 (+/-0.316) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.628 (+/-0.312) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.671 (+/-0.378) for {'C': 10.0, 'kernel': 'linear'}
0.638 (+/-0.310) for {'C': 100.0, 'kernel': 'linear'}
0.624 (+/-0.318) for {'C': 1000.0, 'kernel': 'linear'}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'linear'}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.617 (+/-0.238) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.617 (+/-0.238) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.642 (+/-0.236) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.642 (+/-0.236) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.642 (+/-0.236) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.642 (+/-0.236) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.642 (+/-0.236) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.667 (+/-0.316) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.666 (+/-0.316) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.666 (+/-0.315) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.682 (+/-0.295) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.617 (+/-0.238) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.642 (+/-0.236) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.642 (+/-0.236) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.642 (+/-0.236) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.667 (+/-0.316) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.667 (+/-0.316) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.667 (+/-0.316) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.683 (+/-0.296) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.666 (+/-0.314) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.682 (+/-0.294) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.682 (+/-0.296) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.617 (+/-0.238) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.642 (+/-0.236) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.642 (+/-0.236) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.642 (+/-0.236) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.667 (+/-0.316) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.667 (+/-0.316) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.666 (+/-0.316) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.682 (+/-0.295) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.682 (+/-0.293) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.665 (+/-0.315) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.665 (+/-0.315) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.617 (+/-0.238) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.642 (+/-0.236) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.667 (+/-0.316) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.667 (+/-0.316) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.683 (+/-0.296) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.683 (+/-0.296) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.682 (+/-0.294) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.682 (+/-0.293) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.682 (+/-0.295) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.665 (+/-0.316) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.664 (+/-0.316) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.617 (+/-0.238) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.667 (+/-0.316) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.683 (+/-0.296) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.667 (+/-0.316) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.683 (+/-0.296) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.683 (+/-0.296) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.665 (+/-0.315) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.665 (+/-0.315) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.664 (+/-0.315) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.663 (+/-0.315) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.661 (+/-0.317) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.642 (+/-0.236) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.667 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.667 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.666 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.682 (+/-0.294) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.681 (+/-0.292) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.665 (+/-0.315) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.664 (+/-0.314) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.662 (+/-0.314) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.660 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.658 (+/-0.315) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.642 (+/-0.236) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.667 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.666 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.665 (+/-0.314) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.679 (+/-0.288) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.681 (+/-0.293) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.664 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.663 (+/-0.315) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.659 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.656 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.658 (+/-0.315) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.641 (+/-0.236) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.667 (+/-0.316) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.665 (+/-0.315) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.665 (+/-0.314) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.695 (+/-0.301) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.680 (+/-0.293) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.661 (+/-0.315) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.661 (+/-0.317) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.656 (+/-0.315) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.656 (+/-0.316) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.658 (+/-0.314) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.641 (+/-0.236) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.667 (+/-0.316) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.656 (+/-0.308) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.663 (+/-0.315) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.678 (+/-0.289) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.696 (+/-0.307) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.659 (+/-0.311) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.657 (+/-0.319) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.656 (+/-0.314) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.656 (+/-0.316) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.659 (+/-0.316) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.641 (+/-0.235) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.666 (+/-0.316) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.656 (+/-0.308) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.663 (+/-0.316) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.678 (+/-0.291) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.694 (+/-0.306) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.656 (+/-0.311) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.655 (+/-0.317) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.656 (+/-0.315) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.656 (+/-0.315) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.660 (+/-0.318) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.641 (+/-0.235) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.666 (+/-0.316) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.655 (+/-0.308) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.663 (+/-0.315) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.677 (+/-0.294) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.677 (+/-0.293) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.655 (+/-0.310) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.653 (+/-0.318) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.656 (+/-0.315) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.656 (+/-0.315) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.659 (+/-0.316) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.663 (+/-0.316) for {'C': 10.0, 'kernel': 'linear'}
0.663 (+/-0.316) for {'C': 100.0, 'kernel': 'linear'}
0.614 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.588 (+/-0.233) for {'C': 10000.0, 'kernel': 'linear'}
0.588 (+/-0.233) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.08      0.17      0.11         6
          1       0.99      0.98      0.99       623

avg / total       0.98      0.97      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.695832302745486
循环迭代之前，delta is: [2.98107171e+06 2.11758237e-22]
执行tunemodel()函数前，使用的grid search parameter是：
 [{'C': array([2511886.43150958, 2754228.70333817, 3019951.72040201,
       3311311.21482591, 3630780.54770102, 3981071.70553498,
       4365158.32240166, 4786300.92322638, 5248074.60249773,
       5754399.37337156, 6309573.44480193]), 'kernel': ['rbf'], 'gamma': array([6.30957344e-07, 6.91830971e-07, 7.58577575e-07, 8.31763771e-07,
       9.12010839e-07, 1.00000000e-06, 1.09647820e-06, 1.20226443e-06,
       1.31825674e-06, 1.44543977e-06, 1.58489319e-06])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.774 (+/-0.458) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.702 (+/-0.421) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.756 (+/-0.432) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.681 (+/-0.373) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.789 (+/-0.443) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.694 (+/-0.354) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.684 (+/-0.372) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.689 (+/-0.375) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.652 (+/-0.313) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.664 (+/-0.291) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.689 (+/-0.375) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.774 (+/-0.458) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.685 (+/-0.384) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.698 (+/-0.367) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.677 (+/-0.374) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.765 (+/-0.422) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.710 (+/-0.363) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.684 (+/-0.372) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.689 (+/-0.375) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.652 (+/-0.313) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.647 (+/-0.308) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.681 (+/-0.373) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.773 (+/-0.461) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.699 (+/-0.423) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.714 (+/-0.404) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.677 (+/-0.374) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.765 (+/-0.422) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.694 (+/-0.354) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.684 (+/-0.372) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.689 (+/-0.375) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.652 (+/-0.313) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.651 (+/-0.308) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.681 (+/-0.373) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.756 (+/-0.438) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.683 (+/-0.386) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.664 (+/-0.299) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.677 (+/-0.374) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.764 (+/-0.421) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.694 (+/-0.354) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.684 (+/-0.372) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.689 (+/-0.375) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.648 (+/-0.306) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.651 (+/-0.308) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.689 (+/-0.375) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.731 (+/-0.408) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.677 (+/-0.374) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.639 (+/-0.309) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.677 (+/-0.374) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.756 (+/-0.425) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.694 (+/-0.354) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.684 (+/-0.372) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.689 (+/-0.375) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.645 (+/-0.306) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.651 (+/-0.308) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.689 (+/-0.375) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.691 (+/-0.364) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.685 (+/-0.384) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.658 (+/-0.292) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.668 (+/-0.371) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.706 (+/-0.418) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.694 (+/-0.354) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.689 (+/-0.374) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.689 (+/-0.375) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.648 (+/-0.306) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.656 (+/-0.312) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.689 (+/-0.375) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.675 (+/-0.351) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.669 (+/-0.373) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.660 (+/-0.290) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.677 (+/-0.374) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.756 (+/-0.425) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.694 (+/-0.354) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.689 (+/-0.374) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.689 (+/-0.375) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.651 (+/-0.308) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.651 (+/-0.308) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.698 (+/-0.376) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.673 (+/-0.351) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.666 (+/-0.372) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.660 (+/-0.290) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.677 (+/-0.374) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.764 (+/-0.421) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.702 (+/-0.355) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.673 (+/-0.321) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.689 (+/-0.375) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.651 (+/-0.308) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.651 (+/-0.308) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.698 (+/-0.376) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.673 (+/-0.351) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.672 (+/-0.371) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.664 (+/-0.291) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.668 (+/-0.371) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.764 (+/-0.421) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.702 (+/-0.355) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.673 (+/-0.321) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.689 (+/-0.375) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.651 (+/-0.308) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.643 (+/-0.301) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.698 (+/-0.376) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.676 (+/-0.351) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.666 (+/-0.371) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.658 (+/-0.290) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.668 (+/-0.371) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.764 (+/-0.421) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.694 (+/-0.354) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.673 (+/-0.321) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.681 (+/-0.373) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.651 (+/-0.308) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.651 (+/-0.308) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.698 (+/-0.376) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.684 (+/-0.353) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.664 (+/-0.372) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.635 (+/-0.299) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.677 (+/-0.374) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.764 (+/-0.421) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.669 (+/-0.295) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.673 (+/-0.321) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.681 (+/-0.373) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.645 (+/-0.306) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.651 (+/-0.308) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.698 (+/-0.376) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.706 (+/-0.383) for {'C': 10.0, 'kernel': 'linear'}
0.641 (+/-0.311) for {'C': 100.0, 'kernel': 'linear'}
0.624 (+/-0.318) for {'C': 1000.0, 'kernel': 'linear'}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'linear'}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.14      0.17      0.15         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.682 (+/-0.295) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.666 (+/-0.315) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.682 (+/-0.295) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.666 (+/-0.315) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.683 (+/-0.296) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.682 (+/-0.294) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.666 (+/-0.314) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.666 (+/-0.315) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.665 (+/-0.314) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.681 (+/-0.293) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.666 (+/-0.316) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.682 (+/-0.295) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.666 (+/-0.315) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.682 (+/-0.295) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.665 (+/-0.315) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.699 (+/-0.309) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.699 (+/-0.307) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.666 (+/-0.314) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.666 (+/-0.315) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.665 (+/-0.314) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.665 (+/-0.314) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.665 (+/-0.316) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.682 (+/-0.295) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.666 (+/-0.315) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.682 (+/-0.295) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.665 (+/-0.315) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.699 (+/-0.309) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.682 (+/-0.294) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.666 (+/-0.314) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.666 (+/-0.316) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.665 (+/-0.314) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.665 (+/-0.314) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.665 (+/-0.316) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.682 (+/-0.295) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.665 (+/-0.314) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.681 (+/-0.294) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.665 (+/-0.315) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.683 (+/-0.296) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.698 (+/-0.305) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.666 (+/-0.314) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.666 (+/-0.316) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.665 (+/-0.313) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.665 (+/-0.314) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.666 (+/-0.316) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.682 (+/-0.295) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.665 (+/-0.314) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.664 (+/-0.313) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.665 (+/-0.315) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.682 (+/-0.296) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.698 (+/-0.304) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.666 (+/-0.314) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.666 (+/-0.316) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.665 (+/-0.312) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.665 (+/-0.314) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.665 (+/-0.316) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.682 (+/-0.295) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.666 (+/-0.315) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.681 (+/-0.292) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.665 (+/-0.315) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.666 (+/-0.316) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.698 (+/-0.304) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.666 (+/-0.314) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.666 (+/-0.316) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.665 (+/-0.313) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.665 (+/-0.315) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.665 (+/-0.316) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.681 (+/-0.292) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.665 (+/-0.313) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.681 (+/-0.293) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.665 (+/-0.315) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.682 (+/-0.296) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.698 (+/-0.304) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.666 (+/-0.314) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.666 (+/-0.316) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.665 (+/-0.314) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.665 (+/-0.315) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.666 (+/-0.317) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.681 (+/-0.291) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.665 (+/-0.312) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.681 (+/-0.293) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.665 (+/-0.315) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.683 (+/-0.296) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.698 (+/-0.304) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.666 (+/-0.314) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.666 (+/-0.316) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.665 (+/-0.314) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.665 (+/-0.315) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.666 (+/-0.317) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.681 (+/-0.291) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.665 (+/-0.313) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.681 (+/-0.294) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.665 (+/-0.315) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.683 (+/-0.296) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.698 (+/-0.304) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.666 (+/-0.315) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.666 (+/-0.316) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.665 (+/-0.314) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.664 (+/-0.314) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.666 (+/-0.317) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.681 (+/-0.291) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.665 (+/-0.312) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.681 (+/-0.292) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.665 (+/-0.315) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.682 (+/-0.296) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.698 (+/-0.305) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.665 (+/-0.315) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.665 (+/-0.316) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.665 (+/-0.315) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.665 (+/-0.315) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.665 (+/-0.317) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.681 (+/-0.291) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.665 (+/-0.311) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.656 (+/-0.308) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.665 (+/-0.315) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.682 (+/-0.296) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.697 (+/-0.305) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.665 (+/-0.315) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.665 (+/-0.316) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.664 (+/-0.313) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.665 (+/-0.315) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.665 (+/-0.317) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.666 (+/-0.315) for {'C': 10.0, 'kernel': 'linear'}
0.663 (+/-0.316) for {'C': 100.0, 'kernel': 'linear'}
0.614 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.588 (+/-0.233) for {'C': 10000.0, 'kernel': 'linear'}
0.588 (+/-0.233) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.12      0.17      0.14         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6993589743589744
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.646 (+/-0.317) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.641 (+/-0.306) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.655 (+/-0.295) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.658 (+/-0.329) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.701 (+/-0.351) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.648 (+/-0.284) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.656 (+/-0.312) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.664 (+/-0.326) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.622 (+/-0.298) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.647 (+/-0.289) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.630 (+/-0.313) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.646 (+/-0.317) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.691 (+/-0.355) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.656 (+/-0.293) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.660 (+/-0.327) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.701 (+/-0.351) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.656 (+/-0.291) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.651 (+/-0.308) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.664 (+/-0.326) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.622 (+/-0.298) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.630 (+/-0.302) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.620 (+/-0.304) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.637 (+/-0.301) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.689 (+/-0.357) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.648 (+/-0.287) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.660 (+/-0.327) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.701 (+/-0.351) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.648 (+/-0.284) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.648 (+/-0.306) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.658 (+/-0.329) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.620 (+/-0.299) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.630 (+/-0.302) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.618 (+/-0.305) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.629 (+/-0.292) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.680 (+/-0.355) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.645 (+/-0.287) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.652 (+/-0.322) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.701 (+/-0.351) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.656 (+/-0.291) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.648 (+/-0.306) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.658 (+/-0.329) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.624 (+/-0.300) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.626 (+/-0.302) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.627 (+/-0.314) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.631 (+/-0.291) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.683 (+/-0.353) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.629 (+/-0.300) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.652 (+/-0.322) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.693 (+/-0.350) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.660 (+/-0.290) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.648 (+/-0.306) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.656 (+/-0.330) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.623 (+/-0.301) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.624 (+/-0.302) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.625 (+/-0.315) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.631 (+/-0.289) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.633 (+/-0.298) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.647 (+/-0.285) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.643 (+/-0.316) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.637 (+/-0.299) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.658 (+/-0.292) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.648 (+/-0.306) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.656 (+/-0.330) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.623 (+/-0.301) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.624 (+/-0.302) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.624 (+/-0.315) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.642 (+/-0.288) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.626 (+/-0.298) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.650 (+/-0.284) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.652 (+/-0.322) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.683 (+/-0.353) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.658 (+/-0.292) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.645 (+/-0.306) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.655 (+/-0.331) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.623 (+/-0.301) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.623 (+/-0.302) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.632 (+/-0.324) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.642 (+/-0.288) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.626 (+/-0.298) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.645 (+/-0.284) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.652 (+/-0.322) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.689 (+/-0.357) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.660 (+/-0.299) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.649 (+/-0.313) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.655 (+/-0.331) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.623 (+/-0.301) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.623 (+/-0.302) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.631 (+/-0.325) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.634 (+/-0.280) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.629 (+/-0.298) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.645 (+/-0.284) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.635 (+/-0.299) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.689 (+/-0.357) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.657 (+/-0.302) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.647 (+/-0.314) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.655 (+/-0.331) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.623 (+/-0.301) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.620 (+/-0.303) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.631 (+/-0.325) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.638 (+/-0.281) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.629 (+/-0.298) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.644 (+/-0.285) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.635 (+/-0.299) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.689 (+/-0.357) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.649 (+/-0.296) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.647 (+/-0.314) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.645 (+/-0.318) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.621 (+/-0.302) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.620 (+/-0.304) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.631 (+/-0.325) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.646 (+/-0.289) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.629 (+/-0.298) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.618 (+/-0.288) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.643 (+/-0.307) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.689 (+/-0.357) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.647 (+/-0.297) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.643 (+/-0.315) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.639 (+/-0.314) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.620 (+/-0.303) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.618 (+/-0.304) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.630 (+/-0.326) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.671 (+/-0.378) for {'C': 10.0, 'kernel': 'linear'}
0.638 (+/-0.310) for {'C': 100.0, 'kernel': 'linear'}
0.624 (+/-0.318) for {'C': 1000.0, 'kernel': 'linear'}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'linear'}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.20      0.17      0.18         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.99      0.99       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.695 (+/-0.301) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.664 (+/-0.313) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.680 (+/-0.293) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.665 (+/-0.315) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.681 (+/-0.295) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.680 (+/-0.293) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.665 (+/-0.315) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.664 (+/-0.318) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.662 (+/-0.313) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.679 (+/-0.297) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.661 (+/-0.315) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.695 (+/-0.301) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.681 (+/-0.293) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.681 (+/-0.293) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.665 (+/-0.315) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.698 (+/-0.309) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.696 (+/-0.307) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.665 (+/-0.314) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.664 (+/-0.318) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.662 (+/-0.314) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.662 (+/-0.318) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.660 (+/-0.314) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.678 (+/-0.287) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.681 (+/-0.293) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.680 (+/-0.293) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.665 (+/-0.315) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.698 (+/-0.309) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.680 (+/-0.293) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.665 (+/-0.314) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.663 (+/-0.318) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.661 (+/-0.315) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.661 (+/-0.318) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.660 (+/-0.313) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.678 (+/-0.288) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.681 (+/-0.293) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.680 (+/-0.293) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.665 (+/-0.316) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.681 (+/-0.296) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.696 (+/-0.307) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.664 (+/-0.314) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.663 (+/-0.318) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.661 (+/-0.315) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.661 (+/-0.317) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.660 (+/-0.313) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.678 (+/-0.288) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.681 (+/-0.293) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.663 (+/-0.314) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.665 (+/-0.316) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.681 (+/-0.296) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.696 (+/-0.307) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.664 (+/-0.314) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.663 (+/-0.319) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.661 (+/-0.315) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.661 (+/-0.317) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.659 (+/-0.311) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.678 (+/-0.289) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.664 (+/-0.313) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.680 (+/-0.294) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.664 (+/-0.316) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.663 (+/-0.314) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.696 (+/-0.307) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.664 (+/-0.315) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.663 (+/-0.319) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.660 (+/-0.316) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.660 (+/-0.318) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.659 (+/-0.311) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.678 (+/-0.290) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.663 (+/-0.313) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.680 (+/-0.294) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.664 (+/-0.316) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.680 (+/-0.295) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.696 (+/-0.307) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.664 (+/-0.314) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.662 (+/-0.319) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.660 (+/-0.317) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.659 (+/-0.319) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.658 (+/-0.311) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.678 (+/-0.290) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.663 (+/-0.313) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.680 (+/-0.294) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.664 (+/-0.317) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.680 (+/-0.295) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.696 (+/-0.307) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.664 (+/-0.314) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.662 (+/-0.319) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.660 (+/-0.317) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.659 (+/-0.319) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.657 (+/-0.311) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.678 (+/-0.290) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.663 (+/-0.314) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.680 (+/-0.294) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.664 (+/-0.316) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.680 (+/-0.296) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.695 (+/-0.307) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.664 (+/-0.314) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.662 (+/-0.320) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.659 (+/-0.318) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.659 (+/-0.317) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.657 (+/-0.311) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.678 (+/-0.290) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.663 (+/-0.314) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.679 (+/-0.294) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.664 (+/-0.316) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.679 (+/-0.296) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.695 (+/-0.307) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.664 (+/-0.314) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.662 (+/-0.319) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.659 (+/-0.318) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.658 (+/-0.318) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.657 (+/-0.311) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.678 (+/-0.291) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.663 (+/-0.314) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.654 (+/-0.309) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.664 (+/-0.316) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.679 (+/-0.297) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.694 (+/-0.306) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.663 (+/-0.313) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.661 (+/-0.318) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.658 (+/-0.318) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.658 (+/-0.316) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.656 (+/-0.311) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.663 (+/-0.316) for {'C': 10.0, 'kernel': 'linear'}
0.663 (+/-0.316) for {'C': 100.0, 'kernel': 'linear'}
0.614 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.588 (+/-0.233) for {'C': 10000.0, 'kernel': 'linear'}
0.588 (+/-0.233) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.14      0.17      0.15         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6975961538461539
第0轮loop中，tunemodel函数得到的最优参数是： ([{'C': array([2511886.43150958, 2558585.88690565, 2606153.5499989 ,
       2654605.56197554, 2703958.36410884, 2754228.70333816,
       2805433.63795172, 2857590.54337495, 2910717.11806661,
       2964831.38952434, 3019951.72040201]), 'kernel': ['rbf'], 'gamma': array([8.31763771e-07, 8.47227414e-07, 8.62978548e-07, 8.79022517e-07,
       8.95364766e-07, 9.12010839e-07, 9.28966387e-07, 9.46237161e-07,
       9.63829024e-07, 9.81747943e-07, 1.00000000e-06])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}], {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}, 0.6993589743589744)
这是第0次迭代微调C和gamma。
第0次迭代，得到delta： [1.22684300e+06 8.79891606e-08]
执行tunemodel()函数前，使用的grid search parameter是：
 [{'C': array([2511886.43150958, 2558585.88690565, 2606153.5499989 ,
       2654605.56197554, 2703958.36410884, 2754228.70333816,
       2805433.63795172, 2857590.54337495, 2910717.11806661,
       2964831.38952434, 3019951.72040201]), 'kernel': ['rbf'], 'gamma': array([8.31763771e-07, 8.47227414e-07, 8.62978548e-07, 8.79022517e-07,
       8.95364766e-07, 9.12010839e-07, 9.28966387e-07, 9.46237161e-07,
       9.63829024e-07, 9.81747943e-07, 1.00000000e-06])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.681 (+/-0.373) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.693 (+/-0.420) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 8.472274141405952e-07}
0.702 (+/-0.421) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 8.629785477669687e-07}
0.699 (+/-0.423) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 8.790225168308846e-07}
0.773 (+/-0.416) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 8.953647655495937e-07}
0.789 (+/-0.443) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.748 (+/-0.395) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.714 (+/-0.418) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.756 (+/-0.425) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.764 (+/-0.421) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.694 (+/-0.354) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.681 (+/-0.373) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.693 (+/-0.420) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 8.472274141405952e-07}
0.702 (+/-0.421) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 8.629785477669687e-07}
0.699 (+/-0.423) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 8.790225168308846e-07}
0.773 (+/-0.416) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 8.953647655495937e-07}
0.789 (+/-0.443) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.748 (+/-0.395) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.714 (+/-0.418) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.756 (+/-0.425) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.764 (+/-0.421) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.702 (+/-0.355) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.677 (+/-0.374) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.693 (+/-0.420) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 8.472274141405952e-07}
0.702 (+/-0.421) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 8.629785477669687e-07}
0.702 (+/-0.421) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 8.790225168308846e-07}
0.764 (+/-0.421) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 8.953647655495937e-07}
0.789 (+/-0.443) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.748 (+/-0.395) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.714 (+/-0.418) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.756 (+/-0.425) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.764 (+/-0.421) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.702 (+/-0.355) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.677 (+/-0.374) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.693 (+/-0.420) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 8.472274141405952e-07}
0.702 (+/-0.421) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 8.629785477669687e-07}
0.699 (+/-0.423) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 8.790225168308846e-07}
0.764 (+/-0.421) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 8.953647655495937e-07}
0.764 (+/-0.421) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.748 (+/-0.395) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.714 (+/-0.418) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.756 (+/-0.425) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.764 (+/-0.421) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.694 (+/-0.354) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.677 (+/-0.374) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.693 (+/-0.420) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 8.472274141405952e-07}
0.702 (+/-0.421) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 8.629785477669687e-07}
0.699 (+/-0.423) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 8.790225168308846e-07}
0.764 (+/-0.421) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 8.953647655495937e-07}
0.765 (+/-0.422) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.748 (+/-0.395) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.714 (+/-0.418) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.756 (+/-0.425) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.764 (+/-0.421) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.702 (+/-0.363) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.677 (+/-0.374) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.693 (+/-0.420) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 8.472274141405952e-07}
0.702 (+/-0.421) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 8.629785477669687e-07}
0.702 (+/-0.421) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 8.790225168308846e-07}
0.756 (+/-0.425) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 8.953647655495937e-07}
0.765 (+/-0.422) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.748 (+/-0.395) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.714 (+/-0.418) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.756 (+/-0.425) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.764 (+/-0.421) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.710 (+/-0.363) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.677 (+/-0.374) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.693 (+/-0.420) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 8.472274141405952e-07}
0.702 (+/-0.421) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 8.629785477669687e-07}
0.698 (+/-0.424) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 8.790225168308846e-07}
0.764 (+/-0.421) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 8.953647655495937e-07}
0.765 (+/-0.422) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.748 (+/-0.395) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.714 (+/-0.418) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.756 (+/-0.425) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.764 (+/-0.421) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.710 (+/-0.363) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.677 (+/-0.374) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.693 (+/-0.420) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 8.472274141405952e-07}
0.702 (+/-0.421) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 8.629785477669687e-07}
0.702 (+/-0.421) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 8.790225168308846e-07}
0.756 (+/-0.425) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 8.953647655495937e-07}
0.765 (+/-0.422) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.698 (+/-0.383) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.714 (+/-0.418) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.756 (+/-0.425) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.748 (+/-0.395) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.702 (+/-0.355) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.677 (+/-0.374) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.693 (+/-0.420) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 8.472274141405952e-07}
0.706 (+/-0.418) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 8.629785477669687e-07}
0.702 (+/-0.421) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 8.790225168308846e-07}
0.756 (+/-0.425) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 8.953647655495937e-07}
0.765 (+/-0.422) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.698 (+/-0.383) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.714 (+/-0.418) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.756 (+/-0.425) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.764 (+/-0.421) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.694 (+/-0.354) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.677 (+/-0.374) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.693 (+/-0.420) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 8.472274141405952e-07}
0.706 (+/-0.418) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 8.629785477669687e-07}
0.702 (+/-0.421) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 8.790225168308846e-07}
0.756 (+/-0.425) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 8.953647655495937e-07}
0.764 (+/-0.421) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.698 (+/-0.383) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.714 (+/-0.418) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.756 (+/-0.425) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.748 (+/-0.395) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.694 (+/-0.354) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.677 (+/-0.374) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.693 (+/-0.420) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 8.472274141405952e-07}
0.706 (+/-0.418) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 8.629785477669687e-07}
0.699 (+/-0.423) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 8.790225168308846e-07}
0.756 (+/-0.425) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 8.953647655495937e-07}
0.765 (+/-0.422) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.698 (+/-0.383) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.698 (+/-0.383) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.756 (+/-0.425) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.764 (+/-0.421) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.694 (+/-0.354) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.706 (+/-0.383) for {'C': 10.0, 'kernel': 'linear'}
0.641 (+/-0.311) for {'C': 100.0, 'kernel': 'linear'}
0.624 (+/-0.318) for {'C': 1000.0, 'kernel': 'linear'}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'linear'}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.14      0.17      0.15         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.666 (+/-0.315) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.666 (+/-0.315) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 8.472274141405952e-07}
0.666 (+/-0.315) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 8.629785477669687e-07}
0.666 (+/-0.315) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 8.790225168308846e-07}
0.683 (+/-0.296) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 8.953647655495937e-07}
0.683 (+/-0.296) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.683 (+/-0.295) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.666 (+/-0.316) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.683 (+/-0.296) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.683 (+/-0.296) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.682 (+/-0.294) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.666 (+/-0.315) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.666 (+/-0.315) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 8.472274141405952e-07}
0.666 (+/-0.315) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 8.629785477669687e-07}
0.666 (+/-0.315) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 8.790225168308846e-07}
0.683 (+/-0.296) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 8.953647655495937e-07}
0.683 (+/-0.296) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.683 (+/-0.295) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.666 (+/-0.316) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.683 (+/-0.296) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.683 (+/-0.296) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.698 (+/-0.307) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.666 (+/-0.315) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.666 (+/-0.315) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 8.472274141405952e-07}
0.666 (+/-0.315) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 8.629785477669687e-07}
0.666 (+/-0.315) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 8.790225168308846e-07}
0.683 (+/-0.296) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 8.953647655495937e-07}
0.683 (+/-0.296) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.683 (+/-0.295) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.666 (+/-0.316) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.683 (+/-0.296) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.683 (+/-0.296) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.698 (+/-0.307) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.666 (+/-0.315) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.666 (+/-0.315) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 8.472274141405952e-07}
0.666 (+/-0.315) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 8.629785477669687e-07}
0.666 (+/-0.315) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 8.790225168308846e-07}
0.683 (+/-0.296) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 8.953647655495937e-07}
0.683 (+/-0.296) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.683 (+/-0.295) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.666 (+/-0.316) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.683 (+/-0.296) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.683 (+/-0.296) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.682 (+/-0.294) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.665 (+/-0.315) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.666 (+/-0.315) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 8.472274141405952e-07}
0.666 (+/-0.315) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 8.629785477669687e-07}
0.666 (+/-0.315) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 8.790225168308846e-07}
0.683 (+/-0.296) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 8.953647655495937e-07}
0.699 (+/-0.309) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.683 (+/-0.295) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.666 (+/-0.316) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.683 (+/-0.296) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.683 (+/-0.296) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.682 (+/-0.295) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.665 (+/-0.315) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.666 (+/-0.315) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 8.472274141405952e-07}
0.666 (+/-0.315) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 8.629785477669687e-07}
0.666 (+/-0.315) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 8.790225168308846e-07}
0.682 (+/-0.296) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 8.953647655495937e-07}
0.699 (+/-0.309) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.683 (+/-0.295) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.666 (+/-0.316) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.683 (+/-0.296) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.683 (+/-0.296) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.699 (+/-0.307) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.665 (+/-0.315) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.666 (+/-0.315) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 8.472274141405952e-07}
0.666 (+/-0.315) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 8.629785477669687e-07}
0.665 (+/-0.315) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 8.790225168308846e-07}
0.683 (+/-0.296) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 8.953647655495937e-07}
0.699 (+/-0.309) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.683 (+/-0.295) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.666 (+/-0.316) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.683 (+/-0.296) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.683 (+/-0.296) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.699 (+/-0.307) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.665 (+/-0.315) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.666 (+/-0.315) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 8.472274141405952e-07}
0.666 (+/-0.315) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 8.629785477669687e-07}
0.666 (+/-0.315) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 8.790225168308846e-07}
0.682 (+/-0.296) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 8.953647655495937e-07}
0.699 (+/-0.309) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.666 (+/-0.315) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.666 (+/-0.316) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.683 (+/-0.296) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.682 (+/-0.295) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.698 (+/-0.307) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.665 (+/-0.315) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.666 (+/-0.315) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 8.472274141405952e-07}
0.666 (+/-0.315) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 8.629785477669687e-07}
0.666 (+/-0.315) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 8.790225168308846e-07}
0.682 (+/-0.296) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 8.953647655495937e-07}
0.699 (+/-0.309) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.666 (+/-0.315) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.666 (+/-0.316) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.683 (+/-0.296) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.683 (+/-0.296) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.682 (+/-0.294) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.665 (+/-0.315) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.666 (+/-0.315) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 8.472274141405952e-07}
0.666 (+/-0.315) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 8.629785477669687e-07}
0.666 (+/-0.315) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 8.790225168308846e-07}
0.682 (+/-0.296) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 8.953647655495937e-07}
0.683 (+/-0.296) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.666 (+/-0.315) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.666 (+/-0.316) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.683 (+/-0.296) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.682 (+/-0.295) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.682 (+/-0.294) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.665 (+/-0.315) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.666 (+/-0.315) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 8.472274141405952e-07}
0.666 (+/-0.315) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 8.629785477669687e-07}
0.666 (+/-0.315) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 8.790225168308846e-07}
0.682 (+/-0.296) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 8.953647655495937e-07}
0.699 (+/-0.309) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.666 (+/-0.315) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.666 (+/-0.315) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.683 (+/-0.296) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.683 (+/-0.296) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.682 (+/-0.294) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.666 (+/-0.315) for {'C': 10.0, 'kernel': 'linear'}
0.663 (+/-0.316) for {'C': 100.0, 'kernel': 'linear'}
0.614 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.588 (+/-0.233) for {'C': 10000.0, 'kernel': 'linear'}
0.588 (+/-0.233) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.12      0.17      0.14         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6993589743589744
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.658 (+/-0.329) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.652 (+/-0.322) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 8.472274141405952e-07}
0.677 (+/-0.374) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 8.629785477669687e-07}
0.664 (+/-0.374) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 8.790225168308846e-07}
0.740 (+/-0.392) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 8.953647655495937e-07}
0.701 (+/-0.351) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.691 (+/-0.356) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.650 (+/-0.313) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.702 (+/-0.354) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.691 (+/-0.355) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.648 (+/-0.284) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.658 (+/-0.329) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.652 (+/-0.322) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 8.472274141405952e-07}
0.677 (+/-0.374) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 8.629785477669687e-07}
0.664 (+/-0.374) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 8.790225168308846e-07}
0.740 (+/-0.392) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 8.953647655495937e-07}
0.701 (+/-0.351) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.691 (+/-0.356) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.650 (+/-0.313) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.694 (+/-0.353) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.691 (+/-0.355) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.656 (+/-0.291) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.660 (+/-0.327) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.652 (+/-0.322) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 8.472274141405952e-07}
0.677 (+/-0.374) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 8.629785477669687e-07}
0.664 (+/-0.374) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 8.790225168308846e-07}
0.740 (+/-0.392) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 8.953647655495937e-07}
0.709 (+/-0.351) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.687 (+/-0.358) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.650 (+/-0.313) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.684 (+/-0.353) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.691 (+/-0.355) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.656 (+/-0.291) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.660 (+/-0.327) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.652 (+/-0.322) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 8.472274141405952e-07}
0.677 (+/-0.374) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 8.629785477669687e-07}
0.664 (+/-0.374) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 8.790225168308846e-07}
0.731 (+/-0.394) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 8.953647655495937e-07}
0.701 (+/-0.351) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.687 (+/-0.358) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.649 (+/-0.314) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.684 (+/-0.353) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.689 (+/-0.357) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.648 (+/-0.284) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.660 (+/-0.327) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.652 (+/-0.322) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 8.472274141405952e-07}
0.677 (+/-0.374) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 8.629785477669687e-07}
0.664 (+/-0.374) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 8.790225168308846e-07}
0.731 (+/-0.394) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 8.953647655495937e-07}
0.701 (+/-0.351) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.687 (+/-0.358) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.649 (+/-0.314) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.684 (+/-0.353) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.687 (+/-0.358) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.648 (+/-0.284) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.660 (+/-0.327) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.652 (+/-0.322) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 8.472274141405952e-07}
0.677 (+/-0.374) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 8.629785477669687e-07}
0.668 (+/-0.371) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 8.790225168308846e-07}
0.731 (+/-0.394) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 8.953647655495937e-07}
0.701 (+/-0.351) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.687 (+/-0.358) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.649 (+/-0.314) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.684 (+/-0.353) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.687 (+/-0.358) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.656 (+/-0.291) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.660 (+/-0.327) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.652 (+/-0.322) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 8.472274141405952e-07}
0.677 (+/-0.374) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 8.629785477669687e-07}
0.668 (+/-0.371) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 8.790225168308846e-07}
0.731 (+/-0.394) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 8.953647655495937e-07}
0.701 (+/-0.351) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.687 (+/-0.358) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.649 (+/-0.314) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.684 (+/-0.353) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.687 (+/-0.358) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.656 (+/-0.291) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.660 (+/-0.327) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.643 (+/-0.316) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 8.472274141405952e-07}
0.677 (+/-0.374) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 8.629785477669687e-07}
0.664 (+/-0.374) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 8.790225168308846e-07}
0.731 (+/-0.394) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 8.953647655495937e-07}
0.701 (+/-0.351) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.637 (+/-0.307) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.640 (+/-0.308) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.684 (+/-0.353) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.687 (+/-0.358) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.656 (+/-0.291) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.660 (+/-0.327) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.643 (+/-0.316) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 8.472274141405952e-07}
0.681 (+/-0.373) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 8.629785477669687e-07}
0.664 (+/-0.374) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 8.790225168308846e-07}
0.731 (+/-0.394) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 8.953647655495937e-07}
0.701 (+/-0.351) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.636 (+/-0.308) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.640 (+/-0.308) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.684 (+/-0.353) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.687 (+/-0.358) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.648 (+/-0.284) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.660 (+/-0.327) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.643 (+/-0.316) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 8.472274141405952e-07}
0.681 (+/-0.373) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 8.629785477669687e-07}
0.664 (+/-0.374) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 8.790225168308846e-07}
0.731 (+/-0.394) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 8.953647655495937e-07}
0.701 (+/-0.351) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.637 (+/-0.307) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.640 (+/-0.308) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.684 (+/-0.353) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.687 (+/-0.358) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.648 (+/-0.284) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.660 (+/-0.327) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.643 (+/-0.316) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 8.472274141405952e-07}
0.681 (+/-0.373) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 8.629785477669687e-07}
0.668 (+/-0.371) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 8.790225168308846e-07}
0.731 (+/-0.394) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 8.953647655495937e-07}
0.701 (+/-0.351) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.636 (+/-0.308) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.640 (+/-0.308) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.684 (+/-0.353) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.685 (+/-0.360) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.648 (+/-0.284) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.671 (+/-0.378) for {'C': 10.0, 'kernel': 'linear'}
0.638 (+/-0.310) for {'C': 100.0, 'kernel': 'linear'}
0.624 (+/-0.318) for {'C': 1000.0, 'kernel': 'linear'}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'linear'}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.20      0.17      0.18         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.99      0.99       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.665 (+/-0.315) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.665 (+/-0.315) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 8.472274141405952e-07}
0.665 (+/-0.316) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 8.629785477669687e-07}
0.665 (+/-0.315) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 8.790225168308846e-07}
0.699 (+/-0.309) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 8.953647655495937e-07}
0.681 (+/-0.295) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.680 (+/-0.292) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.664 (+/-0.312) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.681 (+/-0.293) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.681 (+/-0.293) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.680 (+/-0.293) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.665 (+/-0.315) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.665 (+/-0.315) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 8.472274141405952e-07}
0.665 (+/-0.316) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 8.629785477669687e-07}
0.665 (+/-0.316) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 8.790225168308846e-07}
0.699 (+/-0.310) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 8.953647655495937e-07}
0.681 (+/-0.295) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.680 (+/-0.292) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.664 (+/-0.312) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.681 (+/-0.293) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.681 (+/-0.293) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.696 (+/-0.307) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.665 (+/-0.315) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.665 (+/-0.315) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 8.472274141405952e-07}
0.665 (+/-0.316) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 8.629785477669687e-07}
0.665 (+/-0.316) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 8.790225168308846e-07}
0.699 (+/-0.310) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 8.953647655495937e-07}
0.681 (+/-0.295) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.680 (+/-0.291) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.664 (+/-0.312) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.680 (+/-0.293) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.681 (+/-0.293) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.696 (+/-0.307) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.665 (+/-0.315) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.665 (+/-0.315) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 8.472274141405952e-07}
0.665 (+/-0.316) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 8.629785477669687e-07}
0.665 (+/-0.316) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 8.790225168308846e-07}
0.682 (+/-0.297) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 8.953647655495937e-07}
0.681 (+/-0.295) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.680 (+/-0.291) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.664 (+/-0.311) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.680 (+/-0.293) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.681 (+/-0.293) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.680 (+/-0.293) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.665 (+/-0.315) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.665 (+/-0.315) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 8.472274141405952e-07}
0.665 (+/-0.316) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 8.629785477669687e-07}
0.665 (+/-0.316) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 8.790225168308846e-07}
0.682 (+/-0.297) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 8.953647655495937e-07}
0.698 (+/-0.309) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.680 (+/-0.292) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.664 (+/-0.311) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.680 (+/-0.293) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.681 (+/-0.292) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.680 (+/-0.293) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.665 (+/-0.315) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.665 (+/-0.315) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 8.472274141405952e-07}
0.665 (+/-0.316) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 8.629785477669687e-07}
0.665 (+/-0.316) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 8.790225168308846e-07}
0.682 (+/-0.297) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 8.953647655495937e-07}
0.698 (+/-0.309) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.680 (+/-0.292) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.664 (+/-0.311) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.680 (+/-0.293) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.681 (+/-0.292) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.696 (+/-0.307) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.665 (+/-0.315) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.665 (+/-0.315) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 8.472274141405952e-07}
0.665 (+/-0.316) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 8.629785477669687e-07}
0.665 (+/-0.316) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 8.790225168308846e-07}
0.681 (+/-0.298) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 8.953647655495937e-07}
0.698 (+/-0.309) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.680 (+/-0.292) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.664 (+/-0.311) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.680 (+/-0.293) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.681 (+/-0.292) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.696 (+/-0.307) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.665 (+/-0.315) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.665 (+/-0.315) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 8.472274141405952e-07}
0.665 (+/-0.316) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 8.629785477669687e-07}
0.665 (+/-0.316) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 8.790225168308846e-07}
0.681 (+/-0.298) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 8.953647655495937e-07}
0.698 (+/-0.309) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.663 (+/-0.311) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.663 (+/-0.311) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.680 (+/-0.293) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.681 (+/-0.292) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.696 (+/-0.307) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.665 (+/-0.315) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.665 (+/-0.315) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 8.472274141405952e-07}
0.665 (+/-0.317) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 8.629785477669687e-07}
0.665 (+/-0.316) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 8.790225168308846e-07}
0.681 (+/-0.298) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 8.953647655495937e-07}
0.698 (+/-0.309) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.663 (+/-0.311) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.663 (+/-0.311) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.680 (+/-0.293) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.681 (+/-0.292) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.680 (+/-0.293) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.665 (+/-0.315) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.665 (+/-0.315) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 8.472274141405952e-07}
0.665 (+/-0.317) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 8.629785477669687e-07}
0.665 (+/-0.316) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 8.790225168308846e-07}
0.682 (+/-0.297) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 8.953647655495937e-07}
0.681 (+/-0.296) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.663 (+/-0.312) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.663 (+/-0.311) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.680 (+/-0.293) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.681 (+/-0.292) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.680 (+/-0.293) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.665 (+/-0.315) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.665 (+/-0.315) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 8.472274141405952e-07}
0.665 (+/-0.317) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 8.629785477669687e-07}
0.665 (+/-0.316) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 8.790225168308846e-07}
0.681 (+/-0.298) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 8.953647655495937e-07}
0.698 (+/-0.309) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.663 (+/-0.311) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.663 (+/-0.311) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.680 (+/-0.293) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.680 (+/-0.292) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.680 (+/-0.293) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.663 (+/-0.316) for {'C': 10.0, 'kernel': 'linear'}
0.663 (+/-0.316) for {'C': 100.0, 'kernel': 'linear'}
0.614 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.588 (+/-0.233) for {'C': 10000.0, 'kernel': 'linear'}
0.588 (+/-0.233) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 8.953647655495937e-07}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6988782051282051
第1轮loop中，tunemodel函数得到的最优参数是： ([{'C': array([2654605.56197554, 2664403.5277249 , 2674237.65708899,
       2684108.08354523, 2694014.94106366, 2703958.36410884,
       2713938.48764159, 2723955.44712086, 2734009.37850561,
       2744100.41825657, 2754228.70333816]), 'kernel': ['rbf'], 'gamma': array([8.95364766e-07, 8.98669495e-07, 9.01986422e-07, 9.05315592e-07,
       9.08657049e-07, 9.12010839e-07, 9.15377008e-07, 9.18755602e-07,
       9.22146665e-07, 9.25550245e-07, 9.28966387e-07])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}], {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}, 0.6993589743589744)
这是第1次迭代微调C和gamma。
第1次迭代，得到delta： [50270.33922933     0.        ]
SVC模型clf_op_iter的参数是： {'kernel': 'rbf', 'decision_function_shape': 'ovr', 'class_weight': {-1: 15}, 'tol': 0.001, 'gamma': 9.120108393559093e-07, 'random_state': 0, 'cache_size': 200, 'verbose': False, 'degree': 3, 'C': 2703958.364108842, 'probability': False, 'shrinking': True, 'coef0': 0.0, 'max_iter': -1}

训练模型SVC对训练样本的预测准确率： 0.9347980155917789

测试集中，预测为舞弊样本的有： (array([1255], dtype=int64),) 
测试集中，实际为舞弊样本的有： (array([1246, 1247, 1248, 1249, 1250, 1251, 1252, 1253, 1254, 1255, 1256],
      dtype=int64),) 
预测的舞弊样本数目： 1

训练模型SVC对测试样本的预测准确率： 0.8936924167257264
以上是第28次特征筛选。
第28次特征筛选，AUC值是： 0.5454545454545454
X_train_iter_svc.shape is: (1257, 24)
X_test_iter_svc.shape is: (1257, 24)
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.622 (+/-0.336) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.747 (+/-0.449) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.17      0.17      0.17         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.589 (+/-0.231) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.641 (+/-0.236) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.17      0.17      0.17         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6413461538461539
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.630 (+/-0.310) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.20      0.17      0.18         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.99      0.99       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.20      0.17      0.18         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.99      0.99       629

本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6663461538461538
RBF的粗grid search最佳超参： {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001} RBF的粗grid search最高分值： 0.6413461538461539
linear的粗grid search最佳超参： {'C': 1.0, 'kernel': 'linear'} linear的粗grid search最高分值： 0.6663461538461538
粗grid search得到的parameter是：
 [{'C': array([1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03, 1.e+04, 1.e+05,
       1.e+06, 1.e+07, 1.e+08]), 'kernel': ['rbf'], 'gamma': array([1.e-08, 1.e-07, 1.e-06, 1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01,
       1.e+00, 1.e+01, 1.e+02])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.647 (+/-0.333) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.449) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.664 (+/-0.388) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.635 (+/-0.326) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.449) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.375) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.610 (+/-0.326) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.622 (+/-0.336) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.449) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.375) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.625 (+/-0.314) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.610 (+/-0.326) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.622 (+/-0.336) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.747 (+/-0.502) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.748 (+/-0.449) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.375) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.625 (+/-0.314) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.610 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.622 (+/-0.336) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.797 (+/-0.492) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.798 (+/-0.437) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.723 (+/-0.417) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.638 (+/-0.301) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.620 (+/-0.320) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.610 (+/-0.326) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.622 (+/-0.336) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.649 (+/-0.778) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.797 (+/-0.492) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.676 (+/-0.294) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.627 (+/-0.313) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.614 (+/-0.327) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.619 (+/-0.322) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.610 (+/-0.326) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.622 (+/-0.336) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.699 (+/-0.797) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.797 (+/-0.492) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.651 (+/-0.308) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.625 (+/-0.314) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.614 (+/-0.327) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.619 (+/-0.322) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.610 (+/-0.326) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.622 (+/-0.336) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.706 (+/-0.383) for {'C': 10.0, 'kernel': 'linear'}
0.643 (+/-0.320) for {'C': 100.0, 'kernel': 'linear'}
0.630 (+/-0.310) for {'C': 1000.0, 'kernel': 'linear'}
0.619 (+/-0.321) for {'C': 10000.0, 'kernel': 'linear'}
0.627 (+/-0.313) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.006) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.004) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.641 (+/-0.236) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.240) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.641 (+/-0.236) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.641 (+/-0.236) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.587 (+/-0.233) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.589 (+/-0.231) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.641 (+/-0.236) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.641 (+/-0.236) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.636 (+/-0.236) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.232) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.589 (+/-0.231) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.617 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.666 (+/-0.316) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.641 (+/-0.236) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.636 (+/-0.235) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.588 (+/-0.233) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.231) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.589 (+/-0.231) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.642 (+/-0.236) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.683 (+/-0.296) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.666 (+/-0.316) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.662 (+/-0.224) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.612 (+/-0.241) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.588 (+/-0.233) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.231) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.589 (+/-0.231) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.617 (+/-0.238) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.642 (+/-0.236) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.681 (+/-0.295) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.658 (+/-0.318) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.586 (+/-0.235) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.611 (+/-0.240) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.588 (+/-0.233) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.231) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.589 (+/-0.231) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.642 (+/-0.236) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.642 (+/-0.236) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.664 (+/-0.316) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.657 (+/-0.316) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.586 (+/-0.235) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.611 (+/-0.240) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.588 (+/-0.233) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.231) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.589 (+/-0.231) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.666 (+/-0.315) for {'C': 10.0, 'kernel': 'linear'}
0.662 (+/-0.317) for {'C': 100.0, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.611 (+/-0.241) for {'C': 10000.0, 'kernel': 'linear'}
0.636 (+/-0.237) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6830128205128205
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.673 (+/-0.330) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.627 (+/-0.330) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.326) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.643 (+/-0.391) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.635 (+/-0.326) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.673 (+/-0.330) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.658 (+/-0.387) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.610 (+/-0.326) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.622 (+/-0.336) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.673 (+/-0.330) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.658 (+/-0.387) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.625 (+/-0.314) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.610 (+/-0.326) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.622 (+/-0.336) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.747 (+/-0.502) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.797 (+/-0.492) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.702 (+/-0.355) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.658 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.625 (+/-0.314) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.610 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.622 (+/-0.336) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.024) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.797 (+/-0.492) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.656 (+/-0.291) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.657 (+/-0.389) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.638 (+/-0.301) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.620 (+/-0.320) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.610 (+/-0.326) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.622 (+/-0.336) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.699 (+/-0.797) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.681 (+/-0.372) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.645 (+/-0.299) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.626 (+/-0.314) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.614 (+/-0.327) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.619 (+/-0.322) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.610 (+/-0.326) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.622 (+/-0.336) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.699 (+/-0.797) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.672 (+/-0.370) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.617 (+/-0.306) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.625 (+/-0.314) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.614 (+/-0.327) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.619 (+/-0.322) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.610 (+/-0.326) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.622 (+/-0.336) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.673 (+/-0.377) for {'C': 10.0, 'kernel': 'linear'}
0.631 (+/-0.312) for {'C': 100.0, 'kernel': 'linear'}
0.630 (+/-0.310) for {'C': 1000.0, 'kernel': 'linear'}
0.619 (+/-0.321) for {'C': 10000.0, 'kernel': 'linear'}
0.627 (+/-0.313) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.006) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.666 (+/-0.315) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.611 (+/-0.242) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.004) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.315) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.609 (+/-0.243) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.240) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.666 (+/-0.315) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.658 (+/-0.314) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.587 (+/-0.233) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.589 (+/-0.231) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.315) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.658 (+/-0.314) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.636 (+/-0.236) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.232) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.589 (+/-0.231) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.617 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.642 (+/-0.236) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.682 (+/-0.294) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.658 (+/-0.313) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.636 (+/-0.235) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.588 (+/-0.233) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.231) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.589 (+/-0.231) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.523 (+/-0.138) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.642 (+/-0.236) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.681 (+/-0.292) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.657 (+/-0.315) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.662 (+/-0.224) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.612 (+/-0.241) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.588 (+/-0.233) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.231) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.589 (+/-0.231) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.642 (+/-0.236) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.641 (+/-0.235) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.677 (+/-0.294) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.657 (+/-0.317) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.586 (+/-0.235) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.611 (+/-0.240) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.588 (+/-0.233) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.231) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.589 (+/-0.231) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.642 (+/-0.236) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.641 (+/-0.234) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.658 (+/-0.315) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.657 (+/-0.316) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.586 (+/-0.235) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.611 (+/-0.240) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.588 (+/-0.233) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.231) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.589 (+/-0.231) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.663 (+/-0.316) for {'C': 10.0, 'kernel': 'linear'}
0.661 (+/-0.315) for {'C': 100.0, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.611 (+/-0.241) for {'C': 10000.0, 'kernel': 'linear'}
0.636 (+/-0.237) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.17      0.17      0.17         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6818910256410255
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.747 (+/-0.502) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.747 (+/-0.502) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.747 (+/-0.502) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.747 (+/-0.502) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.747 (+/-0.502) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.747 (+/-0.502) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.747 (+/-0.502) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.747 (+/-0.502) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.772 (+/-0.473) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.748 (+/-0.449) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.747 (+/-0.502) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.747 (+/-0.502) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.747 (+/-0.502) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.747 (+/-0.502) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.747 (+/-0.502) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.747 (+/-0.502) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.747 (+/-0.502) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.747 (+/-0.502) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.797 (+/-0.492) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.747 (+/-0.449) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.731 (+/-0.422) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.747 (+/-0.502) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.747 (+/-0.502) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.797 (+/-0.492) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.747 (+/-0.502) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.747 (+/-0.502) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.747 (+/-0.502) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.747 (+/-0.502) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.797 (+/-0.492) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.748 (+/-0.449) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.731 (+/-0.422) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.723 (+/-0.423) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.747 (+/-0.502) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.797 (+/-0.492) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.797 (+/-0.492) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.747 (+/-0.502) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.797 (+/-0.492) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.797 (+/-0.492) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.722 (+/-0.473) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.748 (+/-0.449) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.731 (+/-0.422) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.698 (+/-0.383) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.698 (+/-0.383) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.747 (+/-0.502) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.797 (+/-0.492) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.797 (+/-0.492) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.747 (+/-0.502) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.798 (+/-0.492) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.798 (+/-0.492) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.747 (+/-0.449) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.731 (+/-0.422) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.698 (+/-0.383) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.698 (+/-0.383) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.706 (+/-0.383) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.797 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.797 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.798 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.797 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.748 (+/-0.449) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.798 (+/-0.437) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.722 (+/-0.417) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.685 (+/-0.384) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.698 (+/-0.383) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.664 (+/-0.326) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.723 (+/-0.417) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.797 (+/-0.492) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.798 (+/-0.492) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.798 (+/-0.492) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.798 (+/-0.492) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.748 (+/-0.449) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.773 (+/-0.416) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.664 (+/-0.388) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.664 (+/-0.326) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.664 (+/-0.317) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.656 (+/-0.312) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.714 (+/-0.418) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.797 (+/-0.492) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.798 (+/-0.492) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.773 (+/-0.474) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.798 (+/-0.492) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.735 (+/-0.455) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.702 (+/-0.363) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.689 (+/-0.375) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.706 (+/-0.383) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.648 (+/-0.315) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.646 (+/-0.316) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.648 (+/-0.316) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.797 (+/-0.492) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.798 (+/-0.492) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.760 (+/-0.482) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.714 (+/-0.418) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.708 (+/-0.401) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.685 (+/-0.352) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.698 (+/-0.376) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.652 (+/-0.313) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.635 (+/-0.310) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.653 (+/-0.333) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.632 (+/-0.310) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.797 (+/-0.492) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.798 (+/-0.492) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.806 (+/-0.473) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.702 (+/-0.421) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.684 (+/-0.353) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.662 (+/-0.289) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.689 (+/-0.375) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.652 (+/-0.313) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.633 (+/-0.312) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.668 (+/-0.382) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.629 (+/-0.311) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.797 (+/-0.492) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.798 (+/-0.492) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.753 (+/-0.491) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.685 (+/-0.384) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.666 (+/-0.354) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.676 (+/-0.294) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.689 (+/-0.375) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.645 (+/-0.314) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.632 (+/-0.313) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.668 (+/-0.383) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.627 (+/-0.313) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.706 (+/-0.383) for {'C': 10.0, 'kernel': 'linear'}
0.643 (+/-0.320) for {'C': 100.0, 'kernel': 'linear'}
0.630 (+/-0.310) for {'C': 1000.0, 'kernel': 'linear'}
0.619 (+/-0.321) for {'C': 10000.0, 'kernel': 'linear'}
0.627 (+/-0.313) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.617 (+/-0.238) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.617 (+/-0.238) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.617 (+/-0.238) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.617 (+/-0.238) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.617 (+/-0.238) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.617 (+/-0.238) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.617 (+/-0.238) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.617 (+/-0.238) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.642 (+/-0.236) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.666 (+/-0.316) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.617 (+/-0.238) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.617 (+/-0.238) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.617 (+/-0.238) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.617 (+/-0.238) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.617 (+/-0.238) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.617 (+/-0.238) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.617 (+/-0.238) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.617 (+/-0.238) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.642 (+/-0.236) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.641 (+/-0.236) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.666 (+/-0.315) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.617 (+/-0.238) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.617 (+/-0.238) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.642 (+/-0.236) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.617 (+/-0.238) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.617 (+/-0.238) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.617 (+/-0.238) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.617 (+/-0.238) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.642 (+/-0.236) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.666 (+/-0.316) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.666 (+/-0.315) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.666 (+/-0.315) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.617 (+/-0.238) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.642 (+/-0.236) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.642 (+/-0.236) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.617 (+/-0.238) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.642 (+/-0.236) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.642 (+/-0.236) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.617 (+/-0.238) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.666 (+/-0.316) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.666 (+/-0.315) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.666 (+/-0.315) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.666 (+/-0.315) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.617 (+/-0.238) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.642 (+/-0.236) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.642 (+/-0.236) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.617 (+/-0.238) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.667 (+/-0.316) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.667 (+/-0.316) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.641 (+/-0.236) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.666 (+/-0.315) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.666 (+/-0.315) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.666 (+/-0.315) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.666 (+/-0.315) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.642 (+/-0.236) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.642 (+/-0.236) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.667 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.642 (+/-0.236) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.666 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.683 (+/-0.296) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.641 (+/-0.235) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.666 (+/-0.315) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.666 (+/-0.315) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.665 (+/-0.315) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.666 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.642 (+/-0.236) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.667 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.667 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.667 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.666 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.683 (+/-0.296) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.616 (+/-0.237) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.665 (+/-0.315) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.665 (+/-0.315) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.640 (+/-0.236) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.664 (+/-0.319) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.642 (+/-0.236) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.667 (+/-0.316) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.667 (+/-0.316) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.667 (+/-0.316) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.666 (+/-0.316) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.682 (+/-0.295) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.666 (+/-0.316) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.666 (+/-0.316) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.664 (+/-0.316) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.638 (+/-0.237) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.663 (+/-0.318) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.642 (+/-0.236) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.667 (+/-0.316) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.666 (+/-0.316) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.666 (+/-0.316) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.681 (+/-0.292) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.681 (+/-0.292) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.666 (+/-0.316) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.665 (+/-0.315) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.662 (+/-0.315) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.663 (+/-0.317) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.661 (+/-0.317) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.642 (+/-0.236) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.667 (+/-0.316) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.683 (+/-0.296) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.666 (+/-0.315) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.681 (+/-0.291) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.681 (+/-0.293) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.665 (+/-0.317) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.665 (+/-0.315) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.661 (+/-0.317) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.662 (+/-0.318) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.660 (+/-0.316) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.642 (+/-0.236) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.667 (+/-0.316) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.657 (+/-0.310) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.666 (+/-0.315) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.680 (+/-0.290) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.681 (+/-0.295) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.665 (+/-0.317) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.664 (+/-0.316) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.660 (+/-0.317) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.662 (+/-0.319) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.658 (+/-0.318) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.666 (+/-0.315) for {'C': 10.0, 'kernel': 'linear'}
0.662 (+/-0.317) for {'C': 100.0, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.611 (+/-0.241) for {'C': 10000.0, 'kernel': 'linear'}
0.636 (+/-0.237) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6830128205128205
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.747 (+/-0.502) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.747 (+/-0.502) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.797 (+/-0.492) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.797 (+/-0.492) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.797 (+/-0.492) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.797 (+/-0.492) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.797 (+/-0.492) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.797 (+/-0.492) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.748 (+/-0.449) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.685 (+/-0.384) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.702 (+/-0.355) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.747 (+/-0.502) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.797 (+/-0.492) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.797 (+/-0.492) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.797 (+/-0.492) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.798 (+/-0.492) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.798 (+/-0.492) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.798 (+/-0.492) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.798 (+/-0.437) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.689 (+/-0.374) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.714 (+/-0.359) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.723 (+/-0.358) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.747 (+/-0.502) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.797 (+/-0.492) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.797 (+/-0.492) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.797 (+/-0.492) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.798 (+/-0.492) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.798 (+/-0.492) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.748 (+/-0.449) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.739 (+/-0.398) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.651 (+/-0.308) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.664 (+/-0.317) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.681 (+/-0.372) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.747 (+/-0.502) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.797 (+/-0.492) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.798 (+/-0.492) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.797 (+/-0.492) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.823 (+/-0.451) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.798 (+/-0.437) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.677 (+/-0.374) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.697 (+/-0.353) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.664 (+/-0.317) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.681 (+/-0.387) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.669 (+/-0.373) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.747 (+/-0.502) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.798 (+/-0.492) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.848 (+/-0.460) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.798 (+/-0.492) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.764 (+/-0.421) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.739 (+/-0.392) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.660 (+/-0.327) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.651 (+/-0.308) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.673 (+/-0.370) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.666 (+/-0.376) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.660 (+/-0.385) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.797 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.798 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.773 (+/-0.474) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.714 (+/-0.418) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.691 (+/-0.356) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.656 (+/-0.291) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.659 (+/-0.313) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.631 (+/-0.298) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.654 (+/-0.379) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.630 (+/-0.314) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.657 (+/-0.389) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.797 (+/-0.492) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.798 (+/-0.492) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.735 (+/-0.455) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.674 (+/-0.376) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.654 (+/-0.370) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.666 (+/-0.295) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.647 (+/-0.308) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.626 (+/-0.302) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.626 (+/-0.314) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.624 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.656 (+/-0.389) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.772 (+/-0.473) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.798 (+/-0.492) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.721 (+/-0.463) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.673 (+/-0.391) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.638 (+/-0.299) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.646 (+/-0.285) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.639 (+/-0.314) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.665 (+/-0.382) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.623 (+/-0.317) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.623 (+/-0.317) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.625 (+/-0.315) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.764 (+/-0.478) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.773 (+/-0.474) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.746 (+/-0.451) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.676 (+/-0.433) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.639 (+/-0.289) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.649 (+/-0.285) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.633 (+/-0.323) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.620 (+/-0.315) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.622 (+/-0.318) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.622 (+/-0.317) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.626 (+/-0.314) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.689 (+/-0.374) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.748 (+/-0.449) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.663 (+/-0.390) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.652 (+/-0.381) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.647 (+/-0.288) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.639 (+/-0.289) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.622 (+/-0.318) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.619 (+/-0.316) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.622 (+/-0.317) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.623 (+/-0.316) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.628 (+/-0.312) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.681 (+/-0.372) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.748 (+/-0.449) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.656 (+/-0.393) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.648 (+/-0.382) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.630 (+/-0.279) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.645 (+/-0.299) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.622 (+/-0.318) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.618 (+/-0.317) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.622 (+/-0.318) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.623 (+/-0.317) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.626 (+/-0.314) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.673 (+/-0.377) for {'C': 10.0, 'kernel': 'linear'}
0.631 (+/-0.312) for {'C': 100.0, 'kernel': 'linear'}
0.630 (+/-0.310) for {'C': 1000.0, 'kernel': 'linear'}
0.619 (+/-0.321) for {'C': 10000.0, 'kernel': 'linear'}
0.627 (+/-0.313) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.617 (+/-0.238) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.617 (+/-0.238) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.642 (+/-0.236) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.642 (+/-0.236) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.642 (+/-0.236) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.642 (+/-0.236) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.642 (+/-0.236) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.642 (+/-0.236) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.666 (+/-0.316) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.666 (+/-0.315) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.682 (+/-0.294) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.617 (+/-0.238) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.642 (+/-0.236) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.642 (+/-0.236) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.642 (+/-0.236) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.667 (+/-0.316) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.667 (+/-0.316) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.667 (+/-0.316) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.683 (+/-0.296) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.666 (+/-0.314) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.682 (+/-0.295) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.682 (+/-0.296) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.617 (+/-0.238) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.642 (+/-0.236) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.642 (+/-0.236) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.642 (+/-0.236) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.667 (+/-0.316) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.667 (+/-0.316) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.666 (+/-0.316) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.682 (+/-0.295) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.665 (+/-0.313) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.665 (+/-0.315) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.665 (+/-0.315) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.617 (+/-0.238) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.642 (+/-0.236) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.667 (+/-0.316) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.642 (+/-0.236) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.683 (+/-0.296) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.683 (+/-0.296) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.665 (+/-0.314) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.682 (+/-0.294) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.665 (+/-0.315) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.665 (+/-0.316) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.664 (+/-0.316) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.617 (+/-0.238) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.667 (+/-0.316) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.683 (+/-0.296) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.667 (+/-0.316) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.683 (+/-0.296) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.683 (+/-0.296) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.665 (+/-0.315) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.665 (+/-0.314) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.665 (+/-0.314) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.663 (+/-0.316) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.661 (+/-0.313) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.642 (+/-0.236) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.667 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.667 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.666 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.682 (+/-0.294) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.681 (+/-0.292) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.665 (+/-0.314) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.664 (+/-0.314) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.662 (+/-0.312) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.660 (+/-0.315) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.657 (+/-0.315) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.642 (+/-0.236) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.667 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.666 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.665 (+/-0.314) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.679 (+/-0.288) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.681 (+/-0.293) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.664 (+/-0.314) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.662 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.659 (+/-0.313) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.656 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.656 (+/-0.315) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.642 (+/-0.236) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.667 (+/-0.316) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.665 (+/-0.315) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.664 (+/-0.314) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.679 (+/-0.287) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.680 (+/-0.292) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.662 (+/-0.317) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.660 (+/-0.319) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.655 (+/-0.314) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.655 (+/-0.316) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.658 (+/-0.315) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.641 (+/-0.236) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.667 (+/-0.316) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.681 (+/-0.294) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.663 (+/-0.314) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.678 (+/-0.289) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.679 (+/-0.294) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.659 (+/-0.312) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.656 (+/-0.316) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.654 (+/-0.315) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.654 (+/-0.316) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.658 (+/-0.316) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.641 (+/-0.235) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.666 (+/-0.316) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.656 (+/-0.308) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.663 (+/-0.315) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.678 (+/-0.291) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.678 (+/-0.293) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.657 (+/-0.309) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.654 (+/-0.316) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.655 (+/-0.315) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.655 (+/-0.316) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.659 (+/-0.316) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.641 (+/-0.235) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.666 (+/-0.316) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.655 (+/-0.308) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.662 (+/-0.315) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.677 (+/-0.294) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.677 (+/-0.294) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.655 (+/-0.310) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.653 (+/-0.316) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.655 (+/-0.313) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.656 (+/-0.314) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.657 (+/-0.317) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.663 (+/-0.316) for {'C': 10.0, 'kernel': 'linear'}
0.661 (+/-0.315) for {'C': 100.0, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.611 (+/-0.241) for {'C': 10000.0, 'kernel': 'linear'}
0.636 (+/-0.237) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6833333333333332
循环迭代之前，delta is: [3.69042656e+05 7.48811357e-07]
执行tunemodel()函数前，使用的grid search parameter是：
 [{'C': array([ 398107.1705535 ,  436515.83224017,  478630.09232264,
        524807.46024977,  575439.93733716,  630957.34448019,
        691830.97091894,  758577.57502918,  831763.77110267,
        912010.83935591, 1000000.        ]), 'kernel': ['rbf'], 'gamma': array([1.58489319e-07, 1.73780083e-07, 1.90546072e-07, 2.08929613e-07,
       2.29086765e-07, 2.51188643e-07, 2.75422870e-07, 3.01995172e-07,
       3.31131121e-07, 3.63078055e-07, 3.98107171e-07])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.797 (+/-0.492) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.797 (+/-0.492) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.747 (+/-0.502) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.797 (+/-0.492) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.797 (+/-0.492) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.797 (+/-0.492) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.797 (+/-0.492) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.747 (+/-0.502) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.747 (+/-0.502) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.747 (+/-0.502) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.797 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.797 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.747 (+/-0.502) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.797 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.797 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.797 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.797 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.747 (+/-0.502) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.747 (+/-0.502) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.747 (+/-0.502) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.797 (+/-0.492) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.797 (+/-0.492) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.797 (+/-0.492) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.797 (+/-0.492) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.797 (+/-0.492) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.797 (+/-0.492) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.797 (+/-0.492) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.747 (+/-0.502) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.747 (+/-0.502) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.797 (+/-0.492) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.797 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.797 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.747 (+/-0.502) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.798 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.797 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.797 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.797 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.747 (+/-0.502) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.747 (+/-0.502) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.797 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.797 (+/-0.492) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.798 (+/-0.492) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.747 (+/-0.502) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.798 (+/-0.492) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.797 (+/-0.492) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.797 (+/-0.492) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.797 (+/-0.492) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.747 (+/-0.502) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.747 (+/-0.502) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.797 (+/-0.492) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.797 (+/-0.492) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.798 (+/-0.492) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.747 (+/-0.502) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.798 (+/-0.492) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.797 (+/-0.492) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.797 (+/-0.492) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.797 (+/-0.492) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.747 (+/-0.502) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.747 (+/-0.502) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.747 (+/-0.502) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.797 (+/-0.492) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.798 (+/-0.492) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.747 (+/-0.502) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.798 (+/-0.492) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.797 (+/-0.492) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.798 (+/-0.492) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.797 (+/-0.492) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.747 (+/-0.502) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.747 (+/-0.502) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.747 (+/-0.502) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.797 (+/-0.492) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.798 (+/-0.492) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.747 (+/-0.502) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.798 (+/-0.492) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.797 (+/-0.492) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.798 (+/-0.492) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.798 (+/-0.492) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.797 (+/-0.492) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.747 (+/-0.502) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.747 (+/-0.502) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.797 (+/-0.492) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.797 (+/-0.492) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.798 (+/-0.492) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.797 (+/-0.492) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.798 (+/-0.492) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.797 (+/-0.492) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.798 (+/-0.492) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.797 (+/-0.492) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.747 (+/-0.502) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.747 (+/-0.502) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.797 (+/-0.492) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.797 (+/-0.492) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.798 (+/-0.492) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.797 (+/-0.492) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.798 (+/-0.492) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.797 (+/-0.492) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.798 (+/-0.492) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.797 (+/-0.492) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.747 (+/-0.502) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.747 (+/-0.502) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.797 (+/-0.492) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.797 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.798 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.797 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.798 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.797 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.798 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.798 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.797 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.747 (+/-0.502) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.747 (+/-0.502) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.797 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.706 (+/-0.383) for {'C': 10.0, 'kernel': 'linear'}
0.643 (+/-0.320) for {'C': 100.0, 'kernel': 'linear'}
0.630 (+/-0.310) for {'C': 1000.0, 'kernel': 'linear'}
0.619 (+/-0.321) for {'C': 10000.0, 'kernel': 'linear'}
0.627 (+/-0.313) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.642 (+/-0.236) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.642 (+/-0.236) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.617 (+/-0.238) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.642 (+/-0.236) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.642 (+/-0.236) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.642 (+/-0.236) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.642 (+/-0.236) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.617 (+/-0.238) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.617 (+/-0.238) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.617 (+/-0.238) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.642 (+/-0.236) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.642 (+/-0.236) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.617 (+/-0.238) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.642 (+/-0.236) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.642 (+/-0.236) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.642 (+/-0.236) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.642 (+/-0.236) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.617 (+/-0.238) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.617 (+/-0.238) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.617 (+/-0.238) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.642 (+/-0.236) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.642 (+/-0.236) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.642 (+/-0.236) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.642 (+/-0.236) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.642 (+/-0.236) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.642 (+/-0.236) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.642 (+/-0.236) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.617 (+/-0.238) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.617 (+/-0.238) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.642 (+/-0.236) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.642 (+/-0.236) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.642 (+/-0.236) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.617 (+/-0.238) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.667 (+/-0.316) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.642 (+/-0.236) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.642 (+/-0.236) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.642 (+/-0.236) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.617 (+/-0.238) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.617 (+/-0.238) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.642 (+/-0.236) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.642 (+/-0.236) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.667 (+/-0.316) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.617 (+/-0.238) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.667 (+/-0.316) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.642 (+/-0.236) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.642 (+/-0.236) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.642 (+/-0.236) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.617 (+/-0.238) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.617 (+/-0.238) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.642 (+/-0.236) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.642 (+/-0.236) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.667 (+/-0.316) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.617 (+/-0.238) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.667 (+/-0.316) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.642 (+/-0.236) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.642 (+/-0.236) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.642 (+/-0.236) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.617 (+/-0.238) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.617 (+/-0.238) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.617 (+/-0.238) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.642 (+/-0.236) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.667 (+/-0.316) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.617 (+/-0.238) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.667 (+/-0.316) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.642 (+/-0.236) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.667 (+/-0.316) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.642 (+/-0.236) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.617 (+/-0.238) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.617 (+/-0.238) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.617 (+/-0.238) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.642 (+/-0.236) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.667 (+/-0.316) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.617 (+/-0.238) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.667 (+/-0.316) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.642 (+/-0.236) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.667 (+/-0.316) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.667 (+/-0.316) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.642 (+/-0.236) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.617 (+/-0.238) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.617 (+/-0.238) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.642 (+/-0.236) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.642 (+/-0.236) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.667 (+/-0.316) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.642 (+/-0.236) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.667 (+/-0.316) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.642 (+/-0.236) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.667 (+/-0.316) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.642 (+/-0.236) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.617 (+/-0.238) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.617 (+/-0.238) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.642 (+/-0.236) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.642 (+/-0.236) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.667 (+/-0.316) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.642 (+/-0.236) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.667 (+/-0.316) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.642 (+/-0.236) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.667 (+/-0.316) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.642 (+/-0.236) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.617 (+/-0.238) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.617 (+/-0.238) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.642 (+/-0.236) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.642 (+/-0.236) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.667 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.642 (+/-0.236) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.667 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.642 (+/-0.236) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.667 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.667 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.642 (+/-0.236) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.617 (+/-0.238) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.617 (+/-0.238) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.642 (+/-0.236) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.666 (+/-0.315) for {'C': 10.0, 'kernel': 'linear'}
0.662 (+/-0.317) for {'C': 100.0, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.611 (+/-0.241) for {'C': 10000.0, 'kernel': 'linear'}
0.636 (+/-0.237) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6666666666666665
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.797 (+/-0.492) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.797 (+/-0.492) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.797 (+/-0.492) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.798 (+/-0.492) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.798 (+/-0.492) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.798 (+/-0.492) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.798 (+/-0.492) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.798 (+/-0.492) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.797 (+/-0.492) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.797 (+/-0.492) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.797 (+/-0.492) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.798 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.798 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.798 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.798 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.798 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.798 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.798 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.798 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.797 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.798 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.798 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.797 (+/-0.492) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.798 (+/-0.492) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.798 (+/-0.492) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.798 (+/-0.492) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.798 (+/-0.492) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.798 (+/-0.492) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.798 (+/-0.492) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.798 (+/-0.492) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.797 (+/-0.492) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.798 (+/-0.492) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.798 (+/-0.492) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.798 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.798 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.798 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.798 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.798 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.848 (+/-0.460) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.798 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.798 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.797 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.798 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.798 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.798 (+/-0.492) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.798 (+/-0.492) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.798 (+/-0.492) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.798 (+/-0.492) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.798 (+/-0.492) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.848 (+/-0.460) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.798 (+/-0.492) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.798 (+/-0.492) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.797 (+/-0.492) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.798 (+/-0.492) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.798 (+/-0.492) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.798 (+/-0.492) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.798 (+/-0.492) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.798 (+/-0.492) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.798 (+/-0.492) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.773 (+/-0.474) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.848 (+/-0.460) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.798 (+/-0.492) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.798 (+/-0.492) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.797 (+/-0.492) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.798 (+/-0.492) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.798 (+/-0.492) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.798 (+/-0.492) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.798 (+/-0.492) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.798 (+/-0.492) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.798 (+/-0.492) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.773 (+/-0.474) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.798 (+/-0.492) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.798 (+/-0.492) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.798 (+/-0.492) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.798 (+/-0.492) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.798 (+/-0.492) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.798 (+/-0.492) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.798 (+/-0.492) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.798 (+/-0.492) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.798 (+/-0.492) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.798 (+/-0.492) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.773 (+/-0.474) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.773 (+/-0.474) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.848 (+/-0.460) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.798 (+/-0.492) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.798 (+/-0.492) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.798 (+/-0.492) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.798 (+/-0.492) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.798 (+/-0.492) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.798 (+/-0.492) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.798 (+/-0.492) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.798 (+/-0.492) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.773 (+/-0.474) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.773 (+/-0.474) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.848 (+/-0.460) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.798 (+/-0.492) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.798 (+/-0.492) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.798 (+/-0.492) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.798 (+/-0.492) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.798 (+/-0.492) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.798 (+/-0.492) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.798 (+/-0.492) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.798 (+/-0.492) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.773 (+/-0.474) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.773 (+/-0.474) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.848 (+/-0.460) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.848 (+/-0.460) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.798 (+/-0.492) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.798 (+/-0.492) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.773 (+/-0.474) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.798 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.798 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.798 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.798 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.764 (+/-0.478) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.773 (+/-0.474) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.848 (+/-0.460) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.848 (+/-0.460) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.798 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.798 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.714 (+/-0.418) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.673 (+/-0.377) for {'C': 10.0, 'kernel': 'linear'}
0.631 (+/-0.312) for {'C': 100.0, 'kernel': 'linear'}
0.630 (+/-0.310) for {'C': 1000.0, 'kernel': 'linear'}
0.619 (+/-0.321) for {'C': 10000.0, 'kernel': 'linear'}
0.627 (+/-0.313) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.642 (+/-0.236) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.642 (+/-0.236) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.642 (+/-0.236) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.667 (+/-0.316) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.667 (+/-0.316) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.667 (+/-0.316) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.667 (+/-0.316) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.667 (+/-0.316) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.642 (+/-0.236) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.642 (+/-0.236) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.642 (+/-0.236) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.667 (+/-0.316) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.667 (+/-0.316) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.667 (+/-0.316) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.667 (+/-0.316) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.667 (+/-0.316) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.667 (+/-0.316) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.667 (+/-0.316) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.667 (+/-0.316) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.642 (+/-0.236) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.667 (+/-0.316) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.667 (+/-0.316) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.642 (+/-0.236) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.667 (+/-0.316) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.667 (+/-0.316) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.667 (+/-0.316) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.667 (+/-0.316) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.667 (+/-0.316) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.667 (+/-0.316) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.667 (+/-0.316) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.642 (+/-0.236) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.667 (+/-0.316) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.667 (+/-0.316) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.667 (+/-0.316) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.667 (+/-0.316) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.667 (+/-0.316) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.667 (+/-0.316) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.667 (+/-0.316) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.683 (+/-0.296) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.667 (+/-0.316) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.667 (+/-0.316) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.642 (+/-0.236) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.667 (+/-0.316) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.667 (+/-0.316) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.667 (+/-0.316) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.667 (+/-0.316) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.667 (+/-0.316) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.667 (+/-0.316) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.667 (+/-0.316) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.683 (+/-0.296) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.667 (+/-0.316) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.667 (+/-0.316) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.642 (+/-0.236) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.667 (+/-0.316) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.667 (+/-0.316) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.667 (+/-0.316) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.667 (+/-0.316) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.667 (+/-0.316) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.667 (+/-0.316) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.667 (+/-0.316) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.683 (+/-0.296) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.667 (+/-0.316) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.667 (+/-0.316) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.642 (+/-0.236) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.667 (+/-0.316) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.667 (+/-0.316) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.667 (+/-0.316) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.667 (+/-0.316) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.667 (+/-0.316) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.667 (+/-0.316) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.667 (+/-0.316) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.667 (+/-0.316) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.667 (+/-0.316) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.667 (+/-0.316) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.667 (+/-0.316) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.667 (+/-0.316) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.667 (+/-0.316) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.667 (+/-0.316) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.667 (+/-0.316) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.667 (+/-0.316) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.667 (+/-0.316) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.667 (+/-0.316) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.667 (+/-0.316) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.683 (+/-0.296) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.667 (+/-0.316) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.667 (+/-0.316) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.667 (+/-0.316) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.667 (+/-0.316) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.667 (+/-0.316) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.667 (+/-0.316) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.667 (+/-0.316) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.667 (+/-0.316) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.667 (+/-0.316) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.667 (+/-0.316) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.683 (+/-0.296) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.667 (+/-0.316) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.667 (+/-0.316) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.667 (+/-0.316) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.667 (+/-0.316) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.667 (+/-0.316) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.667 (+/-0.316) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.667 (+/-0.316) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.667 (+/-0.316) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.667 (+/-0.316) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.667 (+/-0.316) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.683 (+/-0.296) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.683 (+/-0.296) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.667 (+/-0.316) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.667 (+/-0.316) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.667 (+/-0.316) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.667 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.667 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.667 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.667 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.666 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.667 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.683 (+/-0.296) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.683 (+/-0.296) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.667 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.667 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.666 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.663 (+/-0.316) for {'C': 10.0, 'kernel': 'linear'}
0.661 (+/-0.315) for {'C': 100.0, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.611 (+/-0.241) for {'C': 10000.0, 'kernel': 'linear'}
0.636 (+/-0.237) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6833333333333332
第0轮loop中，tunemodel函数得到的最优参数是： ([{'C': array([478630.09232264, 487528.49010339, 496592.32145034, 505824.66200311,
       515228.64458176, 524807.46024977, 534564.35939697, 544502.65284242,
       554625.71295791, 564936.9748123 , 575439.93733716]), 'kernel': ['rbf'], 'gamma': array([2.29086765e-07, 2.33345806e-07, 2.37684029e-07, 2.42102905e-07,
       2.46603934e-07, 2.51188643e-07, 2.55858589e-07, 2.60615355e-07,
       2.65460556e-07, 2.70395836e-07, 2.75422870e-07])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}], {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}, 0.6833333333333332)
这是第0次迭代微调C和gamma。
第0次迭代，得到delta： [1.06149884e+05 6.35274710e-22]
执行tunemodel()函数前，使用的grid search parameter是：
 [{'C': array([478630.09232264, 487528.49010339, 496592.32145034, 505824.66200311,
       515228.64458176, 524807.46024977, 534564.35939697, 544502.65284242,
       554625.71295791, 564936.9748123 , 575439.93733716]), 'kernel': ['rbf'], 'gamma': array([2.29086765e-07, 2.33345806e-07, 2.37684029e-07, 2.42102905e-07,
       2.46603934e-07, 2.51188643e-07, 2.55858589e-07, 2.60615355e-07,
       2.65460556e-07, 2.70395836e-07, 2.75422870e-07])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.797 (+/-0.492) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.747 (+/-0.502) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.797 (+/-0.492) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.797 (+/-0.492) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.797 (+/-0.492) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.797 (+/-0.492) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.797 (+/-0.492) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.797 (+/-0.492) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.797 (+/-0.492) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.797 (+/-0.492) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.797 (+/-0.492) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.747 (+/-0.502) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.797 (+/-0.492) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.797 (+/-0.492) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.797 (+/-0.492) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.797 (+/-0.492) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.797 (+/-0.492) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.797 (+/-0.492) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.797 (+/-0.492) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.797 (+/-0.492) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.797 (+/-0.492) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.747 (+/-0.502) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.797 (+/-0.492) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.798 (+/-0.492) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.797 (+/-0.492) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.797 (+/-0.492) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.797 (+/-0.492) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.797 (+/-0.492) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.797 (+/-0.492) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.797 (+/-0.492) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.797 (+/-0.492) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.747 (+/-0.502) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.797 (+/-0.492) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.798 (+/-0.492) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.797 (+/-0.492) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.797 (+/-0.492) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.797 (+/-0.492) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.797 (+/-0.492) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.797 (+/-0.492) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.797 (+/-0.492) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.797 (+/-0.492) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.747 (+/-0.502) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.797 (+/-0.492) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.798 (+/-0.492) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.797 (+/-0.492) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.797 (+/-0.492) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.797 (+/-0.492) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.797 (+/-0.492) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.797 (+/-0.492) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.797 (+/-0.492) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.797 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.747 (+/-0.502) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.797 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.797 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.797 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.797 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.797 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.797 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.797 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.797 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.797 (+/-0.492) for {'C': 534564.3593969719, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.747 (+/-0.502) for {'C': 534564.3593969719, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.797 (+/-0.492) for {'C': 534564.3593969719, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.798 (+/-0.492) for {'C': 534564.3593969719, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.797 (+/-0.492) for {'C': 534564.3593969719, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.797 (+/-0.492) for {'C': 534564.3593969719, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 534564.3593969719, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.797 (+/-0.492) for {'C': 534564.3593969719, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.797 (+/-0.492) for {'C': 534564.3593969719, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.797 (+/-0.492) for {'C': 534564.3593969719, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.797 (+/-0.492) for {'C': 534564.3593969719, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.797 (+/-0.492) for {'C': 544502.6528424212, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.747 (+/-0.502) for {'C': 544502.6528424212, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.797 (+/-0.492) for {'C': 544502.6528424212, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.798 (+/-0.492) for {'C': 544502.6528424212, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.797 (+/-0.492) for {'C': 544502.6528424212, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.797 (+/-0.492) for {'C': 544502.6528424212, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 544502.6528424212, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.797 (+/-0.492) for {'C': 544502.6528424212, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.797 (+/-0.492) for {'C': 544502.6528424212, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.797 (+/-0.492) for {'C': 544502.6528424212, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.797 (+/-0.492) for {'C': 544502.6528424212, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.797 (+/-0.492) for {'C': 554625.7129579106, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.797 (+/-0.492) for {'C': 554625.7129579106, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.797 (+/-0.492) for {'C': 554625.7129579106, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.798 (+/-0.492) for {'C': 554625.7129579106, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.797 (+/-0.492) for {'C': 554625.7129579106, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.797 (+/-0.492) for {'C': 554625.7129579106, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 554625.7129579106, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.797 (+/-0.492) for {'C': 554625.7129579106, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.797 (+/-0.492) for {'C': 554625.7129579106, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.797 (+/-0.492) for {'C': 554625.7129579106, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.797 (+/-0.492) for {'C': 554625.7129579106, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.797 (+/-0.492) for {'C': 564936.9748123022, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.797 (+/-0.492) for {'C': 564936.9748123022, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.797 (+/-0.492) for {'C': 564936.9748123022, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.798 (+/-0.492) for {'C': 564936.9748123022, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.797 (+/-0.492) for {'C': 564936.9748123022, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.797 (+/-0.492) for {'C': 564936.9748123022, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 564936.9748123022, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.797 (+/-0.492) for {'C': 564936.9748123022, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.797 (+/-0.492) for {'C': 564936.9748123022, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.797 (+/-0.492) for {'C': 564936.9748123022, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.797 (+/-0.492) for {'C': 564936.9748123022, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.797 (+/-0.492) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.747 (+/-0.502) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.797 (+/-0.492) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.798 (+/-0.492) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.797 (+/-0.492) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.797 (+/-0.492) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.797 (+/-0.492) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.797 (+/-0.492) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.797 (+/-0.492) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.797 (+/-0.492) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.706 (+/-0.383) for {'C': 10.0, 'kernel': 'linear'}
0.643 (+/-0.320) for {'C': 100.0, 'kernel': 'linear'}
0.630 (+/-0.310) for {'C': 1000.0, 'kernel': 'linear'}
0.619 (+/-0.321) for {'C': 10000.0, 'kernel': 'linear'}
0.627 (+/-0.313) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.642 (+/-0.236) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.617 (+/-0.238) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.642 (+/-0.236) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.642 (+/-0.236) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.642 (+/-0.236) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.642 (+/-0.236) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.642 (+/-0.236) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.642 (+/-0.236) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.642 (+/-0.236) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.642 (+/-0.236) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.642 (+/-0.236) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.617 (+/-0.238) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.642 (+/-0.236) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.642 (+/-0.236) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.642 (+/-0.236) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.642 (+/-0.236) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.642 (+/-0.236) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.642 (+/-0.236) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.642 (+/-0.236) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.642 (+/-0.236) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.642 (+/-0.236) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.617 (+/-0.238) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.642 (+/-0.236) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.667 (+/-0.316) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.642 (+/-0.236) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.642 (+/-0.236) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.642 (+/-0.236) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.642 (+/-0.236) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.642 (+/-0.236) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.642 (+/-0.236) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.642 (+/-0.236) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.617 (+/-0.238) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.642 (+/-0.236) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.667 (+/-0.316) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.642 (+/-0.236) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.642 (+/-0.236) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.642 (+/-0.236) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.642 (+/-0.236) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.642 (+/-0.236) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.642 (+/-0.236) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.642 (+/-0.236) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.617 (+/-0.238) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.642 (+/-0.236) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.667 (+/-0.316) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.642 (+/-0.236) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.642 (+/-0.236) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.642 (+/-0.236) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.642 (+/-0.236) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.642 (+/-0.236) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.642 (+/-0.236) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.642 (+/-0.236) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.617 (+/-0.238) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.642 (+/-0.236) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.642 (+/-0.236) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.642 (+/-0.236) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.642 (+/-0.236) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.642 (+/-0.236) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.642 (+/-0.236) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.642 (+/-0.236) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.642 (+/-0.236) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.642 (+/-0.236) for {'C': 534564.3593969719, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.617 (+/-0.238) for {'C': 534564.3593969719, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.642 (+/-0.236) for {'C': 534564.3593969719, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.667 (+/-0.316) for {'C': 534564.3593969719, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.642 (+/-0.236) for {'C': 534564.3593969719, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.642 (+/-0.236) for {'C': 534564.3593969719, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 534564.3593969719, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.642 (+/-0.236) for {'C': 534564.3593969719, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.642 (+/-0.236) for {'C': 534564.3593969719, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.642 (+/-0.236) for {'C': 534564.3593969719, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.642 (+/-0.236) for {'C': 534564.3593969719, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.642 (+/-0.236) for {'C': 544502.6528424212, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.617 (+/-0.238) for {'C': 544502.6528424212, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.642 (+/-0.236) for {'C': 544502.6528424212, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.667 (+/-0.316) for {'C': 544502.6528424212, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.642 (+/-0.236) for {'C': 544502.6528424212, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.642 (+/-0.236) for {'C': 544502.6528424212, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 544502.6528424212, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.642 (+/-0.236) for {'C': 544502.6528424212, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.642 (+/-0.236) for {'C': 544502.6528424212, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.642 (+/-0.236) for {'C': 544502.6528424212, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.642 (+/-0.236) for {'C': 544502.6528424212, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.642 (+/-0.236) for {'C': 554625.7129579106, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.642 (+/-0.236) for {'C': 554625.7129579106, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.642 (+/-0.236) for {'C': 554625.7129579106, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.667 (+/-0.316) for {'C': 554625.7129579106, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.642 (+/-0.236) for {'C': 554625.7129579106, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.642 (+/-0.236) for {'C': 554625.7129579106, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 554625.7129579106, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.642 (+/-0.236) for {'C': 554625.7129579106, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.642 (+/-0.236) for {'C': 554625.7129579106, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.642 (+/-0.236) for {'C': 554625.7129579106, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.642 (+/-0.236) for {'C': 554625.7129579106, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.642 (+/-0.236) for {'C': 564936.9748123022, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.642 (+/-0.236) for {'C': 564936.9748123022, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.642 (+/-0.236) for {'C': 564936.9748123022, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.667 (+/-0.316) for {'C': 564936.9748123022, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.642 (+/-0.236) for {'C': 564936.9748123022, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.642 (+/-0.236) for {'C': 564936.9748123022, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 564936.9748123022, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.642 (+/-0.236) for {'C': 564936.9748123022, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.642 (+/-0.236) for {'C': 564936.9748123022, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.642 (+/-0.236) for {'C': 564936.9748123022, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.642 (+/-0.236) for {'C': 564936.9748123022, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.642 (+/-0.236) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.617 (+/-0.238) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.642 (+/-0.236) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.667 (+/-0.316) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.642 (+/-0.236) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.642 (+/-0.236) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.642 (+/-0.236) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.642 (+/-0.236) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.642 (+/-0.236) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.642 (+/-0.236) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.666 (+/-0.315) for {'C': 10.0, 'kernel': 'linear'}
0.662 (+/-0.317) for {'C': 100.0, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.611 (+/-0.241) for {'C': 10000.0, 'kernel': 'linear'}
0.636 (+/-0.237) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6666666666666665
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.798 (+/-0.492) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.797 (+/-0.492) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.798 (+/-0.492) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.798 (+/-0.492) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.798 (+/-0.492) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.798 (+/-0.492) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.848 (+/-0.460) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.798 (+/-0.492) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.798 (+/-0.492) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.798 (+/-0.492) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.798 (+/-0.492) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.798 (+/-0.492) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.797 (+/-0.492) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.798 (+/-0.492) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.798 (+/-0.492) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.798 (+/-0.492) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.798 (+/-0.492) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.798 (+/-0.492) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.798 (+/-0.492) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.798 (+/-0.492) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.798 (+/-0.492) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.798 (+/-0.492) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.798 (+/-0.492) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.797 (+/-0.492) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.798 (+/-0.492) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.798 (+/-0.492) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.798 (+/-0.492) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.798 (+/-0.492) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.798 (+/-0.492) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.798 (+/-0.492) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.798 (+/-0.492) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.798 (+/-0.492) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.798 (+/-0.492) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.798 (+/-0.492) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.797 (+/-0.492) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.798 (+/-0.492) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.798 (+/-0.492) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.798 (+/-0.492) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.798 (+/-0.492) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.798 (+/-0.492) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.798 (+/-0.492) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.798 (+/-0.492) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.798 (+/-0.492) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.798 (+/-0.492) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.798 (+/-0.492) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.797 (+/-0.492) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.798 (+/-0.492) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.798 (+/-0.492) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.798 (+/-0.492) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.798 (+/-0.492) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.798 (+/-0.492) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.798 (+/-0.492) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.798 (+/-0.492) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.798 (+/-0.492) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.798 (+/-0.492) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.798 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.797 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.798 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.798 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.798 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.848 (+/-0.460) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.798 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.798 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.798 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.798 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.798 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.798 (+/-0.492) for {'C': 534564.3593969719, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.797 (+/-0.492) for {'C': 534564.3593969719, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.798 (+/-0.492) for {'C': 534564.3593969719, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.798 (+/-0.492) for {'C': 534564.3593969719, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.798 (+/-0.492) for {'C': 534564.3593969719, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.848 (+/-0.460) for {'C': 534564.3593969719, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.798 (+/-0.492) for {'C': 534564.3593969719, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.798 (+/-0.492) for {'C': 534564.3593969719, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.798 (+/-0.492) for {'C': 534564.3593969719, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.798 (+/-0.492) for {'C': 534564.3593969719, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.798 (+/-0.492) for {'C': 534564.3593969719, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.798 (+/-0.492) for {'C': 544502.6528424212, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.797 (+/-0.492) for {'C': 544502.6528424212, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.798 (+/-0.492) for {'C': 544502.6528424212, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.798 (+/-0.492) for {'C': 544502.6528424212, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.798 (+/-0.492) for {'C': 544502.6528424212, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.848 (+/-0.460) for {'C': 544502.6528424212, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.798 (+/-0.492) for {'C': 544502.6528424212, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.798 (+/-0.492) for {'C': 544502.6528424212, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.798 (+/-0.492) for {'C': 544502.6528424212, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.798 (+/-0.492) for {'C': 544502.6528424212, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.798 (+/-0.492) for {'C': 544502.6528424212, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.798 (+/-0.492) for {'C': 554625.7129579106, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.797 (+/-0.492) for {'C': 554625.7129579106, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.798 (+/-0.492) for {'C': 554625.7129579106, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.798 (+/-0.492) for {'C': 554625.7129579106, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.798 (+/-0.492) for {'C': 554625.7129579106, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.848 (+/-0.460) for {'C': 554625.7129579106, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.798 (+/-0.492) for {'C': 554625.7129579106, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.798 (+/-0.492) for {'C': 554625.7129579106, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.798 (+/-0.492) for {'C': 554625.7129579106, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.798 (+/-0.492) for {'C': 554625.7129579106, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.798 (+/-0.492) for {'C': 554625.7129579106, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.798 (+/-0.492) for {'C': 564936.9748123022, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.797 (+/-0.492) for {'C': 564936.9748123022, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.798 (+/-0.492) for {'C': 564936.9748123022, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.798 (+/-0.492) for {'C': 564936.9748123022, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.798 (+/-0.492) for {'C': 564936.9748123022, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.848 (+/-0.460) for {'C': 564936.9748123022, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.798 (+/-0.492) for {'C': 564936.9748123022, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.798 (+/-0.492) for {'C': 564936.9748123022, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.798 (+/-0.492) for {'C': 564936.9748123022, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.798 (+/-0.492) for {'C': 564936.9748123022, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.798 (+/-0.492) for {'C': 564936.9748123022, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.798 (+/-0.492) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.797 (+/-0.492) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.798 (+/-0.492) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.798 (+/-0.492) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.798 (+/-0.492) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.848 (+/-0.460) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.798 (+/-0.492) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.798 (+/-0.492) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.798 (+/-0.492) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.798 (+/-0.492) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.798 (+/-0.492) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.673 (+/-0.377) for {'C': 10.0, 'kernel': 'linear'}
0.631 (+/-0.312) for {'C': 100.0, 'kernel': 'linear'}
0.630 (+/-0.310) for {'C': 1000.0, 'kernel': 'linear'}
0.619 (+/-0.321) for {'C': 10000.0, 'kernel': 'linear'}
0.627 (+/-0.313) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.667 (+/-0.316) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.642 (+/-0.236) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.667 (+/-0.316) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.667 (+/-0.316) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.667 (+/-0.316) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.667 (+/-0.316) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.683 (+/-0.296) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.667 (+/-0.316) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.667 (+/-0.316) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.667 (+/-0.316) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.667 (+/-0.316) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.667 (+/-0.316) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.642 (+/-0.236) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.667 (+/-0.316) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.667 (+/-0.316) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.667 (+/-0.316) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.667 (+/-0.316) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.667 (+/-0.316) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.667 (+/-0.316) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.667 (+/-0.316) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.667 (+/-0.316) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.667 (+/-0.316) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.667 (+/-0.316) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.642 (+/-0.236) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.667 (+/-0.316) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.667 (+/-0.316) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.667 (+/-0.316) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.667 (+/-0.316) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.667 (+/-0.316) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.667 (+/-0.316) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.667 (+/-0.316) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.667 (+/-0.316) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.667 (+/-0.316) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.667 (+/-0.316) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.642 (+/-0.236) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.667 (+/-0.316) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.667 (+/-0.316) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.667 (+/-0.316) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.667 (+/-0.316) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.667 (+/-0.316) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.667 (+/-0.316) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.667 (+/-0.316) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.667 (+/-0.316) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.667 (+/-0.316) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.667 (+/-0.316) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.642 (+/-0.236) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.667 (+/-0.316) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.667 (+/-0.316) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.667 (+/-0.316) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.667 (+/-0.316) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.667 (+/-0.316) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.667 (+/-0.316) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.667 (+/-0.316) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.667 (+/-0.316) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.667 (+/-0.316) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.667 (+/-0.316) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.642 (+/-0.236) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.667 (+/-0.316) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.667 (+/-0.316) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.667 (+/-0.316) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.683 (+/-0.296) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.667 (+/-0.316) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.667 (+/-0.316) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.667 (+/-0.316) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.667 (+/-0.316) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.667 (+/-0.316) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.667 (+/-0.316) for {'C': 534564.3593969719, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.642 (+/-0.236) for {'C': 534564.3593969719, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.667 (+/-0.316) for {'C': 534564.3593969719, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.667 (+/-0.316) for {'C': 534564.3593969719, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.667 (+/-0.316) for {'C': 534564.3593969719, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.683 (+/-0.296) for {'C': 534564.3593969719, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.667 (+/-0.316) for {'C': 534564.3593969719, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.667 (+/-0.316) for {'C': 534564.3593969719, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.667 (+/-0.316) for {'C': 534564.3593969719, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.667 (+/-0.316) for {'C': 534564.3593969719, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.667 (+/-0.316) for {'C': 534564.3593969719, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.667 (+/-0.316) for {'C': 544502.6528424212, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.642 (+/-0.236) for {'C': 544502.6528424212, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.667 (+/-0.316) for {'C': 544502.6528424212, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.667 (+/-0.316) for {'C': 544502.6528424212, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.667 (+/-0.316) for {'C': 544502.6528424212, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.683 (+/-0.296) for {'C': 544502.6528424212, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.667 (+/-0.316) for {'C': 544502.6528424212, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.667 (+/-0.316) for {'C': 544502.6528424212, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.667 (+/-0.316) for {'C': 544502.6528424212, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.667 (+/-0.316) for {'C': 544502.6528424212, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.667 (+/-0.316) for {'C': 544502.6528424212, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.667 (+/-0.316) for {'C': 554625.7129579106, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.642 (+/-0.236) for {'C': 554625.7129579106, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.667 (+/-0.316) for {'C': 554625.7129579106, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.667 (+/-0.316) for {'C': 554625.7129579106, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.667 (+/-0.316) for {'C': 554625.7129579106, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.683 (+/-0.296) for {'C': 554625.7129579106, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.667 (+/-0.316) for {'C': 554625.7129579106, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.667 (+/-0.316) for {'C': 554625.7129579106, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.667 (+/-0.316) for {'C': 554625.7129579106, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.667 (+/-0.316) for {'C': 554625.7129579106, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.667 (+/-0.316) for {'C': 554625.7129579106, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.667 (+/-0.316) for {'C': 564936.9748123022, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.642 (+/-0.236) for {'C': 564936.9748123022, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.667 (+/-0.316) for {'C': 564936.9748123022, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.667 (+/-0.316) for {'C': 564936.9748123022, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.667 (+/-0.316) for {'C': 564936.9748123022, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.683 (+/-0.296) for {'C': 564936.9748123022, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.667 (+/-0.316) for {'C': 564936.9748123022, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.667 (+/-0.316) for {'C': 564936.9748123022, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.667 (+/-0.316) for {'C': 564936.9748123022, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.667 (+/-0.316) for {'C': 564936.9748123022, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.667 (+/-0.316) for {'C': 564936.9748123022, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.667 (+/-0.316) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.642 (+/-0.236) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.667 (+/-0.316) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.667 (+/-0.316) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.667 (+/-0.316) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.683 (+/-0.296) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.667 (+/-0.316) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.667 (+/-0.316) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.667 (+/-0.316) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.667 (+/-0.316) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.667 (+/-0.316) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.663 (+/-0.316) for {'C': 10.0, 'kernel': 'linear'}
0.661 (+/-0.315) for {'C': 100.0, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.611 (+/-0.241) for {'C': 10000.0, 'kernel': 'linear'}
0.636 (+/-0.237) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6833333333333332
发现最优参数C为原先的最大/最小值，直接重新设置超参。
第1轮loop中，tunemodel函数得到的最优参数是： ([{'C': array([4.78630092e+00, 4.78630092e+01, 4.78630092e+02, 4.78630092e+03,
       4.78630092e+04, 4.78630092e+05, 4.78630092e+06, 4.78630092e+07,
       4.78630092e+08, 4.78630092e+09, 4.78630092e+10]), 'kernel': ['rbf'], 'gamma': array([2.51188643e-07, 2.52115763e-07, 2.53046305e-07, 2.53980281e-07,
       2.54917705e-07, 2.55858589e-07, 2.56802945e-07, 2.57750787e-07,
       2.58702127e-07, 2.59656979e-07, 2.60615355e-07])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}], {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}, 0.6833333333333332)
这是第1次迭代微调C和gamma。
第1次迭代，得到delta： [4.61773679e+04 4.66994554e-09]
执行tunemodel()函数前，使用的grid search parameter是：
 [{'C': array([4.78630092e+00, 4.78630092e+01, 4.78630092e+02, 4.78630092e+03,
       4.78630092e+04, 4.78630092e+05, 4.78630092e+06, 4.78630092e+07,
       4.78630092e+08, 4.78630092e+09, 4.78630092e+10]), 'kernel': ['rbf'], 'gamma': array([2.51188643e-07, 2.52115763e-07, 2.53046305e-07, 2.53980281e-07,
       2.54917705e-07, 2.55858589e-07, 2.56802945e-07, 2.57750787e-07,
       2.58702127e-07, 2.59656979e-07, 2.60615355e-07])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 4.7863009232263805, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.496 (+/-0.001) for {'C': 4.7863009232263805, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.496 (+/-0.001) for {'C': 4.7863009232263805, 'kernel': 'rbf', 'gamma': 2.530463049461392e-07}
0.496 (+/-0.001) for {'C': 4.7863009232263805, 'kernel': 'rbf', 'gamma': 2.539802813772814e-07}
0.496 (+/-0.001) for {'C': 4.7863009232263805, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.496 (+/-0.001) for {'C': 4.7863009232263805, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.496 (+/-0.001) for {'C': 4.7863009232263805, 'kernel': 'rbf', 'gamma': 2.568029450667348e-07}
0.496 (+/-0.001) for {'C': 4.7863009232263805, 'kernel': 'rbf', 'gamma': 2.577507869970538e-07}
0.496 (+/-0.001) for {'C': 4.7863009232263805, 'kernel': 'rbf', 'gamma': 2.587021273464608e-07}
0.496 (+/-0.001) for {'C': 4.7863009232263805, 'kernel': 'rbf', 'gamma': 2.596569790273783e-07}
0.496 (+/-0.001) for {'C': 4.7863009232263805, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.496 (+/-0.001) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.496 (+/-0.001) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.496 (+/-0.001) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 2.530463049461392e-07}
0.496 (+/-0.001) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 2.539802813772814e-07}
0.496 (+/-0.001) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.496 (+/-0.001) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.496 (+/-0.001) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 2.568029450667348e-07}
0.496 (+/-0.001) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 2.577507869970538e-07}
0.496 (+/-0.001) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 2.587021273464608e-07}
0.496 (+/-0.001) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 2.596569790273783e-07}
0.496 (+/-0.001) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.496 (+/-0.001) for {'C': 478.630092322638, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.496 (+/-0.001) for {'C': 478.630092322638, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.496 (+/-0.001) for {'C': 478.630092322638, 'kernel': 'rbf', 'gamma': 2.530463049461392e-07}
0.496 (+/-0.001) for {'C': 478.630092322638, 'kernel': 'rbf', 'gamma': 2.539802813772814e-07}
0.496 (+/-0.001) for {'C': 478.630092322638, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.496 (+/-0.001) for {'C': 478.630092322638, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.496 (+/-0.001) for {'C': 478.630092322638, 'kernel': 'rbf', 'gamma': 2.568029450667348e-07}
0.496 (+/-0.001) for {'C': 478.630092322638, 'kernel': 'rbf', 'gamma': 2.577507869970538e-07}
0.496 (+/-0.001) for {'C': 478.630092322638, 'kernel': 'rbf', 'gamma': 2.587021273464608e-07}
0.496 (+/-0.001) for {'C': 478.630092322638, 'kernel': 'rbf', 'gamma': 2.596569790273783e-07}
0.496 (+/-0.001) for {'C': 478.630092322638, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.496 (+/-0.001) for {'C': 4786.30092322638, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.496 (+/-0.001) for {'C': 4786.30092322638, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.496 (+/-0.001) for {'C': 4786.30092322638, 'kernel': 'rbf', 'gamma': 2.530463049461392e-07}
0.496 (+/-0.001) for {'C': 4786.30092322638, 'kernel': 'rbf', 'gamma': 2.539802813772814e-07}
0.496 (+/-0.001) for {'C': 4786.30092322638, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.496 (+/-0.001) for {'C': 4786.30092322638, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.496 (+/-0.001) for {'C': 4786.30092322638, 'kernel': 'rbf', 'gamma': 2.568029450667348e-07}
0.496 (+/-0.001) for {'C': 4786.30092322638, 'kernel': 'rbf', 'gamma': 2.577507869970538e-07}
0.496 (+/-0.001) for {'C': 4786.30092322638, 'kernel': 'rbf', 'gamma': 2.587021273464608e-07}
0.496 (+/-0.001) for {'C': 4786.30092322638, 'kernel': 'rbf', 'gamma': 2.596569790273783e-07}
0.496 (+/-0.001) for {'C': 4786.30092322638, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.496 (+/-0.001) for {'C': 47863.0092322638, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.496 (+/-0.001) for {'C': 47863.0092322638, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.496 (+/-0.001) for {'C': 47863.0092322638, 'kernel': 'rbf', 'gamma': 2.530463049461392e-07}
0.496 (+/-0.001) for {'C': 47863.0092322638, 'kernel': 'rbf', 'gamma': 2.539802813772814e-07}
0.547 (+/-0.301) for {'C': 47863.0092322638, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.547 (+/-0.301) for {'C': 47863.0092322638, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.547 (+/-0.301) for {'C': 47863.0092322638, 'kernel': 'rbf', 'gamma': 2.568029450667348e-07}
0.547 (+/-0.301) for {'C': 47863.0092322638, 'kernel': 'rbf', 'gamma': 2.577507869970538e-07}
0.547 (+/-0.301) for {'C': 47863.0092322638, 'kernel': 'rbf', 'gamma': 2.587021273464608e-07}
0.547 (+/-0.301) for {'C': 47863.0092322638, 'kernel': 'rbf', 'gamma': 2.596569790273783e-07}
0.547 (+/-0.301) for {'C': 47863.0092322638, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.797 (+/-0.492) for {'C': 478630.092322638, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 478630.092322638, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.797 (+/-0.492) for {'C': 478630.092322638, 'kernel': 'rbf', 'gamma': 2.530463049461392e-07}
0.797 (+/-0.492) for {'C': 478630.092322638, 'kernel': 'rbf', 'gamma': 2.539802813772814e-07}
0.797 (+/-0.492) for {'C': 478630.092322638, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.797 (+/-0.492) for {'C': 478630.092322638, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.797 (+/-0.492) for {'C': 478630.092322638, 'kernel': 'rbf', 'gamma': 2.568029450667348e-07}
0.797 (+/-0.492) for {'C': 478630.092322638, 'kernel': 'rbf', 'gamma': 2.577507869970538e-07}
0.797 (+/-0.492) for {'C': 478630.092322638, 'kernel': 'rbf', 'gamma': 2.587021273464608e-07}
0.797 (+/-0.492) for {'C': 478630.092322638, 'kernel': 'rbf', 'gamma': 2.596569790273783e-07}
0.797 (+/-0.492) for {'C': 478630.092322638, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.810 (+/-0.465) for {'C': 4786300.923226381, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.773 (+/-0.474) for {'C': 4786300.923226381, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.760 (+/-0.482) for {'C': 4786300.923226381, 'kernel': 'rbf', 'gamma': 2.530463049461392e-07}
0.693 (+/-0.446) for {'C': 4786300.923226381, 'kernel': 'rbf', 'gamma': 2.539802813772814e-07}
0.760 (+/-0.482) for {'C': 4786300.923226381, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.743 (+/-0.459) for {'C': 4786300.923226381, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.743 (+/-0.459) for {'C': 4786300.923226381, 'kernel': 'rbf', 'gamma': 2.568029450667348e-07}
0.760 (+/-0.482) for {'C': 4786300.923226381, 'kernel': 'rbf', 'gamma': 2.577507869970538e-07}
0.773 (+/-0.474) for {'C': 4786300.923226381, 'kernel': 'rbf', 'gamma': 2.587021273464608e-07}
0.773 (+/-0.474) for {'C': 4786300.923226381, 'kernel': 'rbf', 'gamma': 2.596569790273783e-07}
0.743 (+/-0.459) for {'C': 4786300.923226381, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.736 (+/-0.468) for {'C': 47863009.2322638, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.686 (+/-0.452) for {'C': 47863009.2322638, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.736 (+/-0.468) for {'C': 47863009.2322638, 'kernel': 'rbf', 'gamma': 2.530463049461392e-07}
0.753 (+/-0.492) for {'C': 47863009.2322638, 'kernel': 'rbf', 'gamma': 2.539802813772814e-07}
0.688 (+/-0.390) for {'C': 47863009.2322638, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.741 (+/-0.402) for {'C': 47863009.2322638, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.741 (+/-0.402) for {'C': 47863009.2322638, 'kernel': 'rbf', 'gamma': 2.568029450667348e-07}
0.691 (+/-0.387) for {'C': 47863009.2322638, 'kernel': 'rbf', 'gamma': 2.577507869970538e-07}
0.679 (+/-0.389) for {'C': 47863009.2322638, 'kernel': 'rbf', 'gamma': 2.587021273464608e-07}
0.674 (+/-0.392) for {'C': 47863009.2322638, 'kernel': 'rbf', 'gamma': 2.596569790273783e-07}
0.678 (+/-0.391) for {'C': 47863009.2322638, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.736 (+/-0.468) for {'C': 478630092.32263803, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.686 (+/-0.452) for {'C': 478630092.32263803, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.736 (+/-0.468) for {'C': 478630092.32263803, 'kernel': 'rbf', 'gamma': 2.530463049461392e-07}
0.736 (+/-0.468) for {'C': 478630092.32263803, 'kernel': 'rbf', 'gamma': 2.539802813772814e-07}
0.686 (+/-0.393) for {'C': 478630092.32263803, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.736 (+/-0.411) for {'C': 478630092.32263803, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.731 (+/-0.407) for {'C': 478630092.32263803, 'kernel': 'rbf', 'gamma': 2.568029450667348e-07}
0.733 (+/-0.405) for {'C': 478630092.32263803, 'kernel': 'rbf', 'gamma': 2.577507869970538e-07}
0.712 (+/-0.372) for {'C': 478630092.32263803, 'kernel': 'rbf', 'gamma': 2.587021273464608e-07}
0.686 (+/-0.393) for {'C': 478630092.32263803, 'kernel': 'rbf', 'gamma': 2.596569790273783e-07}
0.686 (+/-0.393) for {'C': 478630092.32263803, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.736 (+/-0.468) for {'C': 4786300923.22638, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.686 (+/-0.452) for {'C': 4786300923.22638, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.736 (+/-0.468) for {'C': 4786300923.22638, 'kernel': 'rbf', 'gamma': 2.530463049461392e-07}
0.753 (+/-0.492) for {'C': 4786300923.22638, 'kernel': 'rbf', 'gamma': 2.539802813772814e-07}
0.686 (+/-0.393) for {'C': 4786300923.22638, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.739 (+/-0.405) for {'C': 4786300923.22638, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.731 (+/-0.407) for {'C': 4786300923.22638, 'kernel': 'rbf', 'gamma': 2.568029450667348e-07}
0.731 (+/-0.407) for {'C': 4786300923.22638, 'kernel': 'rbf', 'gamma': 2.577507869970538e-07}
0.712 (+/-0.372) for {'C': 4786300923.22638, 'kernel': 'rbf', 'gamma': 2.587021273464608e-07}
0.686 (+/-0.393) for {'C': 4786300923.22638, 'kernel': 'rbf', 'gamma': 2.596569790273783e-07}
0.686 (+/-0.393) for {'C': 4786300923.22638, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.736 (+/-0.468) for {'C': 47863009232.2638, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.686 (+/-0.452) for {'C': 47863009232.2638, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.736 (+/-0.468) for {'C': 47863009232.2638, 'kernel': 'rbf', 'gamma': 2.530463049461392e-07}
0.753 (+/-0.492) for {'C': 47863009232.2638, 'kernel': 'rbf', 'gamma': 2.539802813772814e-07}
0.686 (+/-0.393) for {'C': 47863009232.2638, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.736 (+/-0.411) for {'C': 47863009232.2638, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.731 (+/-0.407) for {'C': 47863009232.2638, 'kernel': 'rbf', 'gamma': 2.568029450667348e-07}
0.731 (+/-0.407) for {'C': 47863009232.2638, 'kernel': 'rbf', 'gamma': 2.577507869970538e-07}
0.712 (+/-0.372) for {'C': 47863009232.2638, 'kernel': 'rbf', 'gamma': 2.587021273464608e-07}
0.686 (+/-0.393) for {'C': 47863009232.2638, 'kernel': 'rbf', 'gamma': 2.596569790273783e-07}
0.686 (+/-0.393) for {'C': 47863009232.2638, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.706 (+/-0.383) for {'C': 10.0, 'kernel': 'linear'}
0.643 (+/-0.320) for {'C': 100.0, 'kernel': 'linear'}
0.630 (+/-0.310) for {'C': 1000.0, 'kernel': 'linear'}
0.619 (+/-0.321) for {'C': 10000.0, 'kernel': 'linear'}
0.627 (+/-0.313) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 4.7863009232263805, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.500 (+/-0.000) for {'C': 4.7863009232263805, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.500 (+/-0.000) for {'C': 4.7863009232263805, 'kernel': 'rbf', 'gamma': 2.530463049461392e-07}
0.500 (+/-0.000) for {'C': 4.7863009232263805, 'kernel': 'rbf', 'gamma': 2.539802813772814e-07}
0.500 (+/-0.000) for {'C': 4.7863009232263805, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.500 (+/-0.000) for {'C': 4.7863009232263805, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.500 (+/-0.000) for {'C': 4.7863009232263805, 'kernel': 'rbf', 'gamma': 2.568029450667348e-07}
0.500 (+/-0.000) for {'C': 4.7863009232263805, 'kernel': 'rbf', 'gamma': 2.577507869970538e-07}
0.500 (+/-0.000) for {'C': 4.7863009232263805, 'kernel': 'rbf', 'gamma': 2.587021273464608e-07}
0.500 (+/-0.000) for {'C': 4.7863009232263805, 'kernel': 'rbf', 'gamma': 2.596569790273783e-07}
0.500 (+/-0.000) for {'C': 4.7863009232263805, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.500 (+/-0.000) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.500 (+/-0.000) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.500 (+/-0.000) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 2.530463049461392e-07}
0.500 (+/-0.000) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 2.539802813772814e-07}
0.500 (+/-0.000) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.500 (+/-0.000) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.500 (+/-0.000) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 2.568029450667348e-07}
0.500 (+/-0.000) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 2.577507869970538e-07}
0.500 (+/-0.000) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 2.587021273464608e-07}
0.500 (+/-0.000) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 2.596569790273783e-07}
0.500 (+/-0.000) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.500 (+/-0.000) for {'C': 478.630092322638, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.500 (+/-0.000) for {'C': 478.630092322638, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.500 (+/-0.000) for {'C': 478.630092322638, 'kernel': 'rbf', 'gamma': 2.530463049461392e-07}
0.500 (+/-0.000) for {'C': 478.630092322638, 'kernel': 'rbf', 'gamma': 2.539802813772814e-07}
0.500 (+/-0.000) for {'C': 478.630092322638, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.500 (+/-0.000) for {'C': 478.630092322638, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.500 (+/-0.000) for {'C': 478.630092322638, 'kernel': 'rbf', 'gamma': 2.568029450667348e-07}
0.500 (+/-0.000) for {'C': 478.630092322638, 'kernel': 'rbf', 'gamma': 2.577507869970538e-07}
0.500 (+/-0.000) for {'C': 478.630092322638, 'kernel': 'rbf', 'gamma': 2.587021273464608e-07}
0.500 (+/-0.000) for {'C': 478.630092322638, 'kernel': 'rbf', 'gamma': 2.596569790273783e-07}
0.500 (+/-0.000) for {'C': 478.630092322638, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.500 (+/-0.000) for {'C': 4786.30092322638, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.500 (+/-0.000) for {'C': 4786.30092322638, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.500 (+/-0.000) for {'C': 4786.30092322638, 'kernel': 'rbf', 'gamma': 2.530463049461392e-07}
0.500 (+/-0.000) for {'C': 4786.30092322638, 'kernel': 'rbf', 'gamma': 2.539802813772814e-07}
0.500 (+/-0.000) for {'C': 4786.30092322638, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.500 (+/-0.000) for {'C': 4786.30092322638, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.500 (+/-0.000) for {'C': 4786.30092322638, 'kernel': 'rbf', 'gamma': 2.568029450667348e-07}
0.500 (+/-0.000) for {'C': 4786.30092322638, 'kernel': 'rbf', 'gamma': 2.577507869970538e-07}
0.500 (+/-0.000) for {'C': 4786.30092322638, 'kernel': 'rbf', 'gamma': 2.587021273464608e-07}
0.500 (+/-0.000) for {'C': 4786.30092322638, 'kernel': 'rbf', 'gamma': 2.596569790273783e-07}
0.500 (+/-0.000) for {'C': 4786.30092322638, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.500 (+/-0.000) for {'C': 47863.0092322638, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.500 (+/-0.000) for {'C': 47863.0092322638, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.500 (+/-0.000) for {'C': 47863.0092322638, 'kernel': 'rbf', 'gamma': 2.530463049461392e-07}
0.500 (+/-0.000) for {'C': 47863.0092322638, 'kernel': 'rbf', 'gamma': 2.539802813772814e-07}
0.525 (+/-0.150) for {'C': 47863.0092322638, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.525 (+/-0.150) for {'C': 47863.0092322638, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.525 (+/-0.150) for {'C': 47863.0092322638, 'kernel': 'rbf', 'gamma': 2.568029450667348e-07}
0.525 (+/-0.150) for {'C': 47863.0092322638, 'kernel': 'rbf', 'gamma': 2.577507869970538e-07}
0.525 (+/-0.150) for {'C': 47863.0092322638, 'kernel': 'rbf', 'gamma': 2.587021273464608e-07}
0.525 (+/-0.150) for {'C': 47863.0092322638, 'kernel': 'rbf', 'gamma': 2.596569790273783e-07}
0.525 (+/-0.150) for {'C': 47863.0092322638, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.642 (+/-0.236) for {'C': 478630.092322638, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 478630.092322638, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.642 (+/-0.236) for {'C': 478630.092322638, 'kernel': 'rbf', 'gamma': 2.530463049461392e-07}
0.642 (+/-0.236) for {'C': 478630.092322638, 'kernel': 'rbf', 'gamma': 2.539802813772814e-07}
0.642 (+/-0.236) for {'C': 478630.092322638, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.642 (+/-0.236) for {'C': 478630.092322638, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.642 (+/-0.236) for {'C': 478630.092322638, 'kernel': 'rbf', 'gamma': 2.568029450667348e-07}
0.642 (+/-0.236) for {'C': 478630.092322638, 'kernel': 'rbf', 'gamma': 2.577507869970538e-07}
0.642 (+/-0.236) for {'C': 478630.092322638, 'kernel': 'rbf', 'gamma': 2.587021273464608e-07}
0.642 (+/-0.236) for {'C': 478630.092322638, 'kernel': 'rbf', 'gamma': 2.596569790273783e-07}
0.642 (+/-0.236) for {'C': 478630.092322638, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.683 (+/-0.296) for {'C': 4786300.923226381, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.667 (+/-0.316) for {'C': 4786300.923226381, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.666 (+/-0.316) for {'C': 4786300.923226381, 'kernel': 'rbf', 'gamma': 2.530463049461392e-07}
0.641 (+/-0.324) for {'C': 4786300.923226381, 'kernel': 'rbf', 'gamma': 2.539802813772814e-07}
0.666 (+/-0.316) for {'C': 4786300.923226381, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.666 (+/-0.315) for {'C': 4786300.923226381, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.666 (+/-0.315) for {'C': 4786300.923226381, 'kernel': 'rbf', 'gamma': 2.568029450667348e-07}
0.666 (+/-0.316) for {'C': 4786300.923226381, 'kernel': 'rbf', 'gamma': 2.577507869970538e-07}
0.667 (+/-0.316) for {'C': 4786300.923226381, 'kernel': 'rbf', 'gamma': 2.587021273464608e-07}
0.667 (+/-0.316) for {'C': 4786300.923226381, 'kernel': 'rbf', 'gamma': 2.596569790273783e-07}
0.666 (+/-0.315) for {'C': 4786300.923226381, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.657 (+/-0.309) for {'C': 47863009.2322638, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.632 (+/-0.315) for {'C': 47863009.2322638, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.657 (+/-0.309) for {'C': 47863009.2322638, 'kernel': 'rbf', 'gamma': 2.530463049461392e-07}
0.657 (+/-0.310) for {'C': 47863009.2322638, 'kernel': 'rbf', 'gamma': 2.539802813772814e-07}
0.665 (+/-0.314) for {'C': 47863009.2322638, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.682 (+/-0.295) for {'C': 47863009.2322638, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.682 (+/-0.295) for {'C': 47863009.2322638, 'kernel': 'rbf', 'gamma': 2.568029450667348e-07}
0.666 (+/-0.315) for {'C': 47863009.2322638, 'kernel': 'rbf', 'gamma': 2.577507869970538e-07}
0.665 (+/-0.314) for {'C': 47863009.2322638, 'kernel': 'rbf', 'gamma': 2.587021273464608e-07}
0.665 (+/-0.314) for {'C': 47863009.2322638, 'kernel': 'rbf', 'gamma': 2.596569790273783e-07}
0.665 (+/-0.314) for {'C': 47863009.2322638, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.657 (+/-0.309) for {'C': 478630092.32263803, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.632 (+/-0.315) for {'C': 478630092.32263803, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.657 (+/-0.309) for {'C': 478630092.32263803, 'kernel': 'rbf', 'gamma': 2.530463049461392e-07}
0.657 (+/-0.309) for {'C': 478630092.32263803, 'kernel': 'rbf', 'gamma': 2.539802813772814e-07}
0.665 (+/-0.314) for {'C': 478630092.32263803, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.681 (+/-0.294) for {'C': 478630092.32263803, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.682 (+/-0.294) for {'C': 478630092.32263803, 'kernel': 'rbf', 'gamma': 2.568029450667348e-07}
0.682 (+/-0.295) for {'C': 478630092.32263803, 'kernel': 'rbf', 'gamma': 2.577507869970538e-07}
0.681 (+/-0.294) for {'C': 478630092.32263803, 'kernel': 'rbf', 'gamma': 2.587021273464608e-07}
0.665 (+/-0.314) for {'C': 478630092.32263803, 'kernel': 'rbf', 'gamma': 2.596569790273783e-07}
0.665 (+/-0.314) for {'C': 478630092.32263803, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.657 (+/-0.309) for {'C': 4786300923.22638, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.632 (+/-0.315) for {'C': 4786300923.22638, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.657 (+/-0.309) for {'C': 4786300923.22638, 'kernel': 'rbf', 'gamma': 2.530463049461392e-07}
0.657 (+/-0.310) for {'C': 4786300923.22638, 'kernel': 'rbf', 'gamma': 2.539802813772814e-07}
0.665 (+/-0.314) for {'C': 4786300923.22638, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.682 (+/-0.295) for {'C': 4786300923.22638, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.682 (+/-0.294) for {'C': 4786300923.22638, 'kernel': 'rbf', 'gamma': 2.568029450667348e-07}
0.682 (+/-0.294) for {'C': 4786300923.22638, 'kernel': 'rbf', 'gamma': 2.577507869970538e-07}
0.681 (+/-0.294) for {'C': 4786300923.22638, 'kernel': 'rbf', 'gamma': 2.587021273464608e-07}
0.665 (+/-0.314) for {'C': 4786300923.22638, 'kernel': 'rbf', 'gamma': 2.596569790273783e-07}
0.665 (+/-0.314) for {'C': 4786300923.22638, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.657 (+/-0.309) for {'C': 47863009232.2638, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.632 (+/-0.315) for {'C': 47863009232.2638, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.657 (+/-0.309) for {'C': 47863009232.2638, 'kernel': 'rbf', 'gamma': 2.530463049461392e-07}
0.657 (+/-0.310) for {'C': 47863009232.2638, 'kernel': 'rbf', 'gamma': 2.539802813772814e-07}
0.665 (+/-0.314) for {'C': 47863009232.2638, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.681 (+/-0.294) for {'C': 47863009232.2638, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.682 (+/-0.294) for {'C': 47863009232.2638, 'kernel': 'rbf', 'gamma': 2.568029450667348e-07}
0.682 (+/-0.294) for {'C': 47863009232.2638, 'kernel': 'rbf', 'gamma': 2.577507869970538e-07}
0.681 (+/-0.294) for {'C': 47863009232.2638, 'kernel': 'rbf', 'gamma': 2.587021273464608e-07}
0.665 (+/-0.314) for {'C': 47863009232.2638, 'kernel': 'rbf', 'gamma': 2.596569790273783e-07}
0.665 (+/-0.314) for {'C': 47863009232.2638, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.666 (+/-0.315) for {'C': 10.0, 'kernel': 'linear'}
0.662 (+/-0.317) for {'C': 100.0, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.611 (+/-0.241) for {'C': 10000.0, 'kernel': 'linear'}
0.636 (+/-0.237) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 4786300.923226381, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6828525641025641
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 4.7863009232263805, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.496 (+/-0.001) for {'C': 4.7863009232263805, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.496 (+/-0.001) for {'C': 4.7863009232263805, 'kernel': 'rbf', 'gamma': 2.530463049461392e-07}
0.496 (+/-0.001) for {'C': 4.7863009232263805, 'kernel': 'rbf', 'gamma': 2.539802813772814e-07}
0.496 (+/-0.001) for {'C': 4.7863009232263805, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.496 (+/-0.001) for {'C': 4.7863009232263805, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.496 (+/-0.001) for {'C': 4.7863009232263805, 'kernel': 'rbf', 'gamma': 2.568029450667348e-07}
0.496 (+/-0.001) for {'C': 4.7863009232263805, 'kernel': 'rbf', 'gamma': 2.577507869970538e-07}
0.496 (+/-0.001) for {'C': 4.7863009232263805, 'kernel': 'rbf', 'gamma': 2.587021273464608e-07}
0.496 (+/-0.001) for {'C': 4.7863009232263805, 'kernel': 'rbf', 'gamma': 2.596569790273783e-07}
0.496 (+/-0.001) for {'C': 4.7863009232263805, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.496 (+/-0.001) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.496 (+/-0.001) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.496 (+/-0.001) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 2.530463049461392e-07}
0.496 (+/-0.001) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 2.539802813772814e-07}
0.496 (+/-0.001) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.496 (+/-0.001) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.496 (+/-0.001) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 2.568029450667348e-07}
0.496 (+/-0.001) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 2.577507869970538e-07}
0.496 (+/-0.001) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 2.587021273464608e-07}
0.496 (+/-0.001) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 2.596569790273783e-07}
0.496 (+/-0.001) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.496 (+/-0.001) for {'C': 478.630092322638, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.496 (+/-0.001) for {'C': 478.630092322638, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.496 (+/-0.001) for {'C': 478.630092322638, 'kernel': 'rbf', 'gamma': 2.530463049461392e-07}
0.496 (+/-0.001) for {'C': 478.630092322638, 'kernel': 'rbf', 'gamma': 2.539802813772814e-07}
0.496 (+/-0.001) for {'C': 478.630092322638, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.496 (+/-0.001) for {'C': 478.630092322638, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.496 (+/-0.001) for {'C': 478.630092322638, 'kernel': 'rbf', 'gamma': 2.568029450667348e-07}
0.496 (+/-0.001) for {'C': 478.630092322638, 'kernel': 'rbf', 'gamma': 2.577507869970538e-07}
0.496 (+/-0.001) for {'C': 478.630092322638, 'kernel': 'rbf', 'gamma': 2.587021273464608e-07}
0.496 (+/-0.001) for {'C': 478.630092322638, 'kernel': 'rbf', 'gamma': 2.596569790273783e-07}
0.496 (+/-0.001) for {'C': 478.630092322638, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.496 (+/-0.001) for {'C': 4786.30092322638, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.496 (+/-0.001) for {'C': 4786.30092322638, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.496 (+/-0.001) for {'C': 4786.30092322638, 'kernel': 'rbf', 'gamma': 2.530463049461392e-07}
0.496 (+/-0.001) for {'C': 4786.30092322638, 'kernel': 'rbf', 'gamma': 2.539802813772814e-07}
0.496 (+/-0.001) for {'C': 4786.30092322638, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.496 (+/-0.001) for {'C': 4786.30092322638, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.496 (+/-0.001) for {'C': 4786.30092322638, 'kernel': 'rbf', 'gamma': 2.568029450667348e-07}
0.496 (+/-0.001) for {'C': 4786.30092322638, 'kernel': 'rbf', 'gamma': 2.577507869970538e-07}
0.496 (+/-0.001) for {'C': 4786.30092322638, 'kernel': 'rbf', 'gamma': 2.587021273464608e-07}
0.496 (+/-0.001) for {'C': 4786.30092322638, 'kernel': 'rbf', 'gamma': 2.596569790273783e-07}
0.496 (+/-0.001) for {'C': 4786.30092322638, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.747 (+/-0.502) for {'C': 47863.0092322638, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.747 (+/-0.502) for {'C': 47863.0092322638, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.747 (+/-0.502) for {'C': 47863.0092322638, 'kernel': 'rbf', 'gamma': 2.530463049461392e-07}
0.747 (+/-0.502) for {'C': 47863.0092322638, 'kernel': 'rbf', 'gamma': 2.539802813772814e-07}
0.747 (+/-0.502) for {'C': 47863.0092322638, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.747 (+/-0.502) for {'C': 47863.0092322638, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.747 (+/-0.502) for {'C': 47863.0092322638, 'kernel': 'rbf', 'gamma': 2.568029450667348e-07}
0.747 (+/-0.502) for {'C': 47863.0092322638, 'kernel': 'rbf', 'gamma': 2.577507869970538e-07}
0.747 (+/-0.502) for {'C': 47863.0092322638, 'kernel': 'rbf', 'gamma': 2.587021273464608e-07}
0.747 (+/-0.502) for {'C': 47863.0092322638, 'kernel': 'rbf', 'gamma': 2.596569790273783e-07}
0.747 (+/-0.502) for {'C': 47863.0092322638, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.798 (+/-0.492) for {'C': 478630.092322638, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.848 (+/-0.460) for {'C': 478630.092322638, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.798 (+/-0.492) for {'C': 478630.092322638, 'kernel': 'rbf', 'gamma': 2.530463049461392e-07}
0.798 (+/-0.492) for {'C': 478630.092322638, 'kernel': 'rbf', 'gamma': 2.539802813772814e-07}
0.798 (+/-0.492) for {'C': 478630.092322638, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.848 (+/-0.460) for {'C': 478630.092322638, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.798 (+/-0.492) for {'C': 478630.092322638, 'kernel': 'rbf', 'gamma': 2.568029450667348e-07}
0.798 (+/-0.492) for {'C': 478630.092322638, 'kernel': 'rbf', 'gamma': 2.577507869970538e-07}
0.798 (+/-0.492) for {'C': 478630.092322638, 'kernel': 'rbf', 'gamma': 2.587021273464608e-07}
0.798 (+/-0.492) for {'C': 478630.092322638, 'kernel': 'rbf', 'gamma': 2.596569790273783e-07}
0.798 (+/-0.492) for {'C': 478630.092322638, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.696 (+/-0.439) for {'C': 4786300.923226381, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.646 (+/-0.403) for {'C': 4786300.923226381, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.662 (+/-0.444) for {'C': 4786300.923226381, 'kernel': 'rbf', 'gamma': 2.530463049461392e-07}
0.662 (+/-0.444) for {'C': 4786300.923226381, 'kernel': 'rbf', 'gamma': 2.539802813772814e-07}
0.663 (+/-0.391) for {'C': 4786300.923226381, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.688 (+/-0.432) for {'C': 4786300.923226381, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.688 (+/-0.432) for {'C': 4786300.923226381, 'kernel': 'rbf', 'gamma': 2.568029450667348e-07}
0.669 (+/-0.395) for {'C': 4786300.923226381, 'kernel': 'rbf', 'gamma': 2.577507869970538e-07}
0.668 (+/-0.396) for {'C': 4786300.923226381, 'kernel': 'rbf', 'gamma': 2.587021273464608e-07}
0.668 (+/-0.396) for {'C': 4786300.923226381, 'kernel': 'rbf', 'gamma': 2.596569790273783e-07}
0.669 (+/-0.395) for {'C': 4786300.923226381, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.650 (+/-0.397) for {'C': 47863009.2322638, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.650 (+/-0.397) for {'C': 47863009.2322638, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.655 (+/-0.394) for {'C': 47863009.2322638, 'kernel': 'rbf', 'gamma': 2.530463049461392e-07}
0.642 (+/-0.405) for {'C': 47863009.2322638, 'kernel': 'rbf', 'gamma': 2.539802813772814e-07}
0.648 (+/-0.398) for {'C': 47863009.2322638, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.654 (+/-0.394) for {'C': 47863009.2322638, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.651 (+/-0.396) for {'C': 47863009.2322638, 'kernel': 'rbf', 'gamma': 2.568029450667348e-07}
0.658 (+/-0.395) for {'C': 47863009.2322638, 'kernel': 'rbf', 'gamma': 2.577507869970538e-07}
0.651 (+/-0.396) for {'C': 47863009.2322638, 'kernel': 'rbf', 'gamma': 2.587021273464608e-07}
0.651 (+/-0.396) for {'C': 47863009.2322638, 'kernel': 'rbf', 'gamma': 2.596569790273783e-07}
0.654 (+/-0.395) for {'C': 47863009.2322638, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.625 (+/-0.332) for {'C': 478630092.32263803, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.650 (+/-0.397) for {'C': 478630092.32263803, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.654 (+/-0.395) for {'C': 478630092.32263803, 'kernel': 'rbf', 'gamma': 2.530463049461392e-07}
0.642 (+/-0.405) for {'C': 478630092.32263803, 'kernel': 'rbf', 'gamma': 2.539802813772814e-07}
0.623 (+/-0.334) for {'C': 478630092.32263803, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.628 (+/-0.331) for {'C': 478630092.32263803, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.650 (+/-0.397) for {'C': 478630092.32263803, 'kernel': 'rbf', 'gamma': 2.568029450667348e-07}
0.654 (+/-0.395) for {'C': 478630092.32263803, 'kernel': 'rbf', 'gamma': 2.577507869970538e-07}
0.651 (+/-0.396) for {'C': 478630092.32263803, 'kernel': 'rbf', 'gamma': 2.587021273464608e-07}
0.650 (+/-0.397) for {'C': 478630092.32263803, 'kernel': 'rbf', 'gamma': 2.596569790273783e-07}
0.654 (+/-0.395) for {'C': 478630092.32263803, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.625 (+/-0.332) for {'C': 4786300923.22638, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.650 (+/-0.397) for {'C': 4786300923.22638, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.654 (+/-0.395) for {'C': 4786300923.22638, 'kernel': 'rbf', 'gamma': 2.530463049461392e-07}
0.642 (+/-0.406) for {'C': 4786300923.22638, 'kernel': 'rbf', 'gamma': 2.539802813772814e-07}
0.623 (+/-0.334) for {'C': 4786300923.22638, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.627 (+/-0.331) for {'C': 4786300923.22638, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.650 (+/-0.398) for {'C': 4786300923.22638, 'kernel': 'rbf', 'gamma': 2.568029450667348e-07}
0.653 (+/-0.396) for {'C': 4786300923.22638, 'kernel': 'rbf', 'gamma': 2.577507869970538e-07}
0.651 (+/-0.396) for {'C': 4786300923.22638, 'kernel': 'rbf', 'gamma': 2.587021273464608e-07}
0.650 (+/-0.397) for {'C': 4786300923.22638, 'kernel': 'rbf', 'gamma': 2.596569790273783e-07}
0.654 (+/-0.395) for {'C': 4786300923.22638, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.625 (+/-0.332) for {'C': 47863009232.2638, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.650 (+/-0.397) for {'C': 47863009232.2638, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.654 (+/-0.395) for {'C': 47863009232.2638, 'kernel': 'rbf', 'gamma': 2.530463049461392e-07}
0.641 (+/-0.406) for {'C': 47863009232.2638, 'kernel': 'rbf', 'gamma': 2.539802813772814e-07}
0.623 (+/-0.334) for {'C': 47863009232.2638, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.627 (+/-0.331) for {'C': 47863009232.2638, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.650 (+/-0.398) for {'C': 47863009232.2638, 'kernel': 'rbf', 'gamma': 2.568029450667348e-07}
0.653 (+/-0.396) for {'C': 47863009232.2638, 'kernel': 'rbf', 'gamma': 2.577507869970538e-07}
0.651 (+/-0.396) for {'C': 47863009232.2638, 'kernel': 'rbf', 'gamma': 2.587021273464608e-07}
0.650 (+/-0.397) for {'C': 47863009232.2638, 'kernel': 'rbf', 'gamma': 2.596569790273783e-07}
0.654 (+/-0.395) for {'C': 47863009232.2638, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.673 (+/-0.377) for {'C': 10.0, 'kernel': 'linear'}
0.631 (+/-0.312) for {'C': 100.0, 'kernel': 'linear'}
0.630 (+/-0.310) for {'C': 1000.0, 'kernel': 'linear'}
0.619 (+/-0.321) for {'C': 10000.0, 'kernel': 'linear'}
0.627 (+/-0.313) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 4.7863009232263805, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.500 (+/-0.000) for {'C': 4.7863009232263805, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.500 (+/-0.000) for {'C': 4.7863009232263805, 'kernel': 'rbf', 'gamma': 2.530463049461392e-07}
0.500 (+/-0.000) for {'C': 4.7863009232263805, 'kernel': 'rbf', 'gamma': 2.539802813772814e-07}
0.500 (+/-0.000) for {'C': 4.7863009232263805, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.500 (+/-0.000) for {'C': 4.7863009232263805, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.500 (+/-0.000) for {'C': 4.7863009232263805, 'kernel': 'rbf', 'gamma': 2.568029450667348e-07}
0.500 (+/-0.000) for {'C': 4.7863009232263805, 'kernel': 'rbf', 'gamma': 2.577507869970538e-07}
0.500 (+/-0.000) for {'C': 4.7863009232263805, 'kernel': 'rbf', 'gamma': 2.587021273464608e-07}
0.500 (+/-0.000) for {'C': 4.7863009232263805, 'kernel': 'rbf', 'gamma': 2.596569790273783e-07}
0.500 (+/-0.000) for {'C': 4.7863009232263805, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.500 (+/-0.000) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.500 (+/-0.000) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.500 (+/-0.000) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 2.530463049461392e-07}
0.500 (+/-0.000) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 2.539802813772814e-07}
0.500 (+/-0.000) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.500 (+/-0.000) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.500 (+/-0.000) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 2.568029450667348e-07}
0.500 (+/-0.000) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 2.577507869970538e-07}
0.500 (+/-0.000) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 2.587021273464608e-07}
0.500 (+/-0.000) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 2.596569790273783e-07}
0.500 (+/-0.000) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.500 (+/-0.000) for {'C': 478.630092322638, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.500 (+/-0.000) for {'C': 478.630092322638, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.500 (+/-0.000) for {'C': 478.630092322638, 'kernel': 'rbf', 'gamma': 2.530463049461392e-07}
0.500 (+/-0.000) for {'C': 478.630092322638, 'kernel': 'rbf', 'gamma': 2.539802813772814e-07}
0.500 (+/-0.000) for {'C': 478.630092322638, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.500 (+/-0.000) for {'C': 478.630092322638, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.500 (+/-0.000) for {'C': 478.630092322638, 'kernel': 'rbf', 'gamma': 2.568029450667348e-07}
0.500 (+/-0.000) for {'C': 478.630092322638, 'kernel': 'rbf', 'gamma': 2.577507869970538e-07}
0.500 (+/-0.000) for {'C': 478.630092322638, 'kernel': 'rbf', 'gamma': 2.587021273464608e-07}
0.500 (+/-0.000) for {'C': 478.630092322638, 'kernel': 'rbf', 'gamma': 2.596569790273783e-07}
0.500 (+/-0.000) for {'C': 478.630092322638, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.500 (+/-0.000) for {'C': 4786.30092322638, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.500 (+/-0.000) for {'C': 4786.30092322638, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.500 (+/-0.000) for {'C': 4786.30092322638, 'kernel': 'rbf', 'gamma': 2.530463049461392e-07}
0.500 (+/-0.000) for {'C': 4786.30092322638, 'kernel': 'rbf', 'gamma': 2.539802813772814e-07}
0.500 (+/-0.000) for {'C': 4786.30092322638, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.500 (+/-0.000) for {'C': 4786.30092322638, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.500 (+/-0.000) for {'C': 4786.30092322638, 'kernel': 'rbf', 'gamma': 2.568029450667348e-07}
0.500 (+/-0.000) for {'C': 4786.30092322638, 'kernel': 'rbf', 'gamma': 2.577507869970538e-07}
0.500 (+/-0.000) for {'C': 4786.30092322638, 'kernel': 'rbf', 'gamma': 2.587021273464608e-07}
0.500 (+/-0.000) for {'C': 4786.30092322638, 'kernel': 'rbf', 'gamma': 2.596569790273783e-07}
0.500 (+/-0.000) for {'C': 4786.30092322638, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.617 (+/-0.238) for {'C': 47863.0092322638, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.617 (+/-0.238) for {'C': 47863.0092322638, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.617 (+/-0.238) for {'C': 47863.0092322638, 'kernel': 'rbf', 'gamma': 2.530463049461392e-07}
0.617 (+/-0.238) for {'C': 47863.0092322638, 'kernel': 'rbf', 'gamma': 2.539802813772814e-07}
0.617 (+/-0.238) for {'C': 47863.0092322638, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.617 (+/-0.238) for {'C': 47863.0092322638, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.617 (+/-0.238) for {'C': 47863.0092322638, 'kernel': 'rbf', 'gamma': 2.568029450667348e-07}
0.617 (+/-0.238) for {'C': 47863.0092322638, 'kernel': 'rbf', 'gamma': 2.577507869970538e-07}
0.617 (+/-0.238) for {'C': 47863.0092322638, 'kernel': 'rbf', 'gamma': 2.587021273464608e-07}
0.617 (+/-0.238) for {'C': 47863.0092322638, 'kernel': 'rbf', 'gamma': 2.596569790273783e-07}
0.617 (+/-0.238) for {'C': 47863.0092322638, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.667 (+/-0.316) for {'C': 478630.092322638, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.683 (+/-0.296) for {'C': 478630.092322638, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.667 (+/-0.316) for {'C': 478630.092322638, 'kernel': 'rbf', 'gamma': 2.530463049461392e-07}
0.667 (+/-0.316) for {'C': 478630.092322638, 'kernel': 'rbf', 'gamma': 2.539802813772814e-07}
0.667 (+/-0.316) for {'C': 478630.092322638, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.683 (+/-0.296) for {'C': 478630.092322638, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.667 (+/-0.316) for {'C': 478630.092322638, 'kernel': 'rbf', 'gamma': 2.568029450667348e-07}
0.667 (+/-0.316) for {'C': 478630.092322638, 'kernel': 'rbf', 'gamma': 2.577507869970538e-07}
0.667 (+/-0.316) for {'C': 478630.092322638, 'kernel': 'rbf', 'gamma': 2.587021273464608e-07}
0.667 (+/-0.316) for {'C': 478630.092322638, 'kernel': 'rbf', 'gamma': 2.596569790273783e-07}
0.667 (+/-0.316) for {'C': 478630.092322638, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.656 (+/-0.308) for {'C': 4786300.923226381, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.639 (+/-0.322) for {'C': 4786300.923226381, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.640 (+/-0.323) for {'C': 4786300.923226381, 'kernel': 'rbf', 'gamma': 2.530463049461392e-07}
0.640 (+/-0.323) for {'C': 4786300.923226381, 'kernel': 'rbf', 'gamma': 2.539802813772814e-07}
0.664 (+/-0.313) for {'C': 4786300.923226381, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.664 (+/-0.314) for {'C': 4786300.923226381, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.664 (+/-0.314) for {'C': 4786300.923226381, 'kernel': 'rbf', 'gamma': 2.568029450667348e-07}
0.664 (+/-0.313) for {'C': 4786300.923226381, 'kernel': 'rbf', 'gamma': 2.577507869970538e-07}
0.663 (+/-0.314) for {'C': 4786300.923226381, 'kernel': 'rbf', 'gamma': 2.587021273464608e-07}
0.664 (+/-0.314) for {'C': 4786300.923226381, 'kernel': 'rbf', 'gamma': 2.596569790273783e-07}
0.664 (+/-0.314) for {'C': 4786300.923226381, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.654 (+/-0.308) for {'C': 47863009.2322638, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.654 (+/-0.308) for {'C': 47863009.2322638, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.655 (+/-0.308) for {'C': 47863009.2322638, 'kernel': 'rbf', 'gamma': 2.530463049461392e-07}
0.638 (+/-0.323) for {'C': 47863009.2322638, 'kernel': 'rbf', 'gamma': 2.539802813772814e-07}
0.662 (+/-0.313) for {'C': 47863009.2322638, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.663 (+/-0.313) for {'C': 47863009.2322638, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.663 (+/-0.313) for {'C': 47863009.2322638, 'kernel': 'rbf', 'gamma': 2.568029450667348e-07}
0.663 (+/-0.313) for {'C': 47863009.2322638, 'kernel': 'rbf', 'gamma': 2.577507869970538e-07}
0.662 (+/-0.314) for {'C': 47863009.2322638, 'kernel': 'rbf', 'gamma': 2.587021273464608e-07}
0.662 (+/-0.314) for {'C': 47863009.2322638, 'kernel': 'rbf', 'gamma': 2.596569790273783e-07}
0.662 (+/-0.314) for {'C': 47863009.2322638, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.654 (+/-0.308) for {'C': 478630092.32263803, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.654 (+/-0.308) for {'C': 478630092.32263803, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.654 (+/-0.308) for {'C': 478630092.32263803, 'kernel': 'rbf', 'gamma': 2.530463049461392e-07}
0.638 (+/-0.323) for {'C': 478630092.32263803, 'kernel': 'rbf', 'gamma': 2.539802813772814e-07}
0.661 (+/-0.313) for {'C': 478630092.32263803, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.662 (+/-0.314) for {'C': 478630092.32263803, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.662 (+/-0.313) for {'C': 478630092.32263803, 'kernel': 'rbf', 'gamma': 2.568029450667348e-07}
0.663 (+/-0.312) for {'C': 478630092.32263803, 'kernel': 'rbf', 'gamma': 2.577507869970538e-07}
0.662 (+/-0.314) for {'C': 478630092.32263803, 'kernel': 'rbf', 'gamma': 2.587021273464608e-07}
0.662 (+/-0.314) for {'C': 478630092.32263803, 'kernel': 'rbf', 'gamma': 2.596569790273783e-07}
0.662 (+/-0.315) for {'C': 478630092.32263803, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.654 (+/-0.308) for {'C': 4786300923.22638, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.654 (+/-0.308) for {'C': 4786300923.22638, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.654 (+/-0.308) for {'C': 4786300923.22638, 'kernel': 'rbf', 'gamma': 2.530463049461392e-07}
0.638 (+/-0.323) for {'C': 4786300923.22638, 'kernel': 'rbf', 'gamma': 2.539802813772814e-07}
0.661 (+/-0.313) for {'C': 4786300923.22638, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.662 (+/-0.313) for {'C': 4786300923.22638, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.662 (+/-0.313) for {'C': 4786300923.22638, 'kernel': 'rbf', 'gamma': 2.568029450667348e-07}
0.663 (+/-0.312) for {'C': 4786300923.22638, 'kernel': 'rbf', 'gamma': 2.577507869970538e-07}
0.662 (+/-0.315) for {'C': 4786300923.22638, 'kernel': 'rbf', 'gamma': 2.587021273464608e-07}
0.662 (+/-0.314) for {'C': 4786300923.22638, 'kernel': 'rbf', 'gamma': 2.596569790273783e-07}
0.662 (+/-0.314) for {'C': 4786300923.22638, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.654 (+/-0.308) for {'C': 47863009232.2638, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.654 (+/-0.308) for {'C': 47863009232.2638, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.654 (+/-0.308) for {'C': 47863009232.2638, 'kernel': 'rbf', 'gamma': 2.530463049461392e-07}
0.638 (+/-0.323) for {'C': 47863009232.2638, 'kernel': 'rbf', 'gamma': 2.539802813772814e-07}
0.661 (+/-0.313) for {'C': 47863009232.2638, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.662 (+/-0.313) for {'C': 47863009232.2638, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.662 (+/-0.313) for {'C': 47863009232.2638, 'kernel': 'rbf', 'gamma': 2.568029450667348e-07}
0.663 (+/-0.312) for {'C': 47863009232.2638, 'kernel': 'rbf', 'gamma': 2.577507869970538e-07}
0.662 (+/-0.314) for {'C': 47863009232.2638, 'kernel': 'rbf', 'gamma': 2.587021273464608e-07}
0.662 (+/-0.314) for {'C': 47863009232.2638, 'kernel': 'rbf', 'gamma': 2.596569790273783e-07}
0.662 (+/-0.314) for {'C': 47863009232.2638, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.663 (+/-0.316) for {'C': 10.0, 'kernel': 'linear'}
0.661 (+/-0.315) for {'C': 100.0, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.611 (+/-0.241) for {'C': 10000.0, 'kernel': 'linear'}
0.636 (+/-0.237) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 478630.092322638, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6833333333333332
第2轮loop中，tunemodel函数得到的最优参数是： ([{'C': array([  47863.00923226,   75857.75750292,  120226.44346174,
        190546.07179632,  301995.1720402 ,  478630.09232264,
        758577.57502918, 1202264.43461741, 1905460.71796325,
       3019951.72040201, 4786300.92322638]), 'kernel': ['rbf'], 'gamma': array([2.51188643e-07, 2.51373794e-07, 2.51559081e-07, 2.51744505e-07,
       2.51930066e-07, 2.52115763e-07, 2.52301597e-07, 2.52487568e-07,
       2.52673677e-07, 2.52859922e-07, 2.53046305e-07])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}], {'C': 478630.092322638, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}, 0.6833333333333332)
这是第2次迭代微调C和gamma。
第2次迭代，得到delta： [4.65661287e-10 3.74282561e-09]
SVC模型clf_op_iter的参数是： {'kernel': 'rbf', 'decision_function_shape': 'ovr', 'class_weight': {-1: 15}, 'tol': 0.001, 'gamma': 2.5211576308074103e-07, 'random_state': 0, 'cache_size': 200, 'verbose': False, 'degree': 3, 'C': 478630.092322638, 'probability': False, 'shrinking': True, 'coef0': 0.0, 'max_iter': -1}

训练模型SVC对训练样本的预测准确率： 0.9362154500354358

测试集中，预测为舞弊样本的有： (array([ 370,  658, 1246, 1247, 1248, 1255, 1256], dtype=int64),) 
测试集中，实际为舞弊样本的有： (array([1246, 1247, 1248, 1249, 1250, 1251, 1252, 1253, 1254, 1255, 1256],
      dtype=int64),) 
预测的舞弊样本数目： 7

训练模型SVC对测试样本的预测准确率： 0.9347980155917789
以上是第29次特征筛选。
第29次特征筛选，AUC值是： 0.7264701590544287
X_train_iter_svc.shape is: (1257, 23)
X_test_iter_svc.shape is: (1257, 23)
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.647 (+/-0.401) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.747 (+/-0.449) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.17      0.17      0.17         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.589 (+/-0.232) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.641 (+/-0.236) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.17      0.17      0.17         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6413461538461539
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.653 (+/-0.293) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.20      0.17      0.18         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.99      0.99       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.662 (+/-0.227) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.20      0.17      0.18         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.99      0.99       629

本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6663461538461538
RBF的粗grid search最佳超参： {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001} RBF的粗grid search最高分值： 0.6413461538461539
linear的粗grid search最佳超参： {'C': 1.0, 'kernel': 'linear'} linear的粗grid search最高分值： 0.6663461538461538
粗grid search得到的parameter是：
 [{'C': array([1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03, 1.e+04, 1.e+05,
       1.e+06, 1.e+07, 1.e+08]), 'kernel': ['rbf'], 'gamma': array([1.e-08, 1.e-07, 1.e-06, 1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01,
       1.e+00, 1.e+01, 1.e+02])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.647 (+/-0.333) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.449) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.449) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.375) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.647 (+/-0.401) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.449) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.706 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.629 (+/-0.312) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.647 (+/-0.401) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.747 (+/-0.502) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.748 (+/-0.449) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.706 (+/-0.383) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.625 (+/-0.314) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.647 (+/-0.401) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.747 (+/-0.502) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.748 (+/-0.449) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.723 (+/-0.417) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.642 (+/-0.299) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.621 (+/-0.320) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.647 (+/-0.401) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.649 (+/-0.778) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.797 (+/-0.492) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.656 (+/-0.284) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.630 (+/-0.312) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.638 (+/-0.302) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.619 (+/-0.322) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.647 (+/-0.401) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.699 (+/-0.797) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.797 (+/-0.492) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.659 (+/-0.313) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.626 (+/-0.313) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.620 (+/-0.321) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.619 (+/-0.322) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.647 (+/-0.401) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.706 (+/-0.383) for {'C': 10.0, 'kernel': 'linear'}
0.643 (+/-0.320) for {'C': 100.0, 'kernel': 'linear'}
0.653 (+/-0.293) for {'C': 1000.0, 'kernel': 'linear'}
0.619 (+/-0.322) for {'C': 10000.0, 'kernel': 'linear'}
0.619 (+/-0.322) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.006) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.004) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.641 (+/-0.236) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.240) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.641 (+/-0.236) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.641 (+/-0.236) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.587 (+/-0.233) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.589 (+/-0.232) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.641 (+/-0.236) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.666 (+/-0.316) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.232) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.589 (+/-0.232) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.617 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.666 (+/-0.316) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.316) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.636 (+/-0.236) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.588 (+/-0.233) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.232) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.589 (+/-0.232) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.617 (+/-0.238) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.666 (+/-0.316) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.666 (+/-0.317) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.662 (+/-0.225) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.612 (+/-0.241) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.587 (+/-0.234) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.232) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.589 (+/-0.232) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.617 (+/-0.238) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.642 (+/-0.236) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.681 (+/-0.294) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.658 (+/-0.320) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.661 (+/-0.223) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.611 (+/-0.240) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.587 (+/-0.234) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.232) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.589 (+/-0.232) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.642 (+/-0.236) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.642 (+/-0.236) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.664 (+/-0.316) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.658 (+/-0.316) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.611 (+/-0.242) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.611 (+/-0.240) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.587 (+/-0.234) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.232) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.589 (+/-0.232) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.666 (+/-0.315) for {'C': 10.0, 'kernel': 'linear'}
0.637 (+/-0.237) for {'C': 100.0, 'kernel': 'linear'}
0.662 (+/-0.227) for {'C': 1000.0, 'kernel': 'linear'}
0.611 (+/-0.241) for {'C': 10000.0, 'kernel': 'linear'}
0.611 (+/-0.240) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.10      0.17      0.12         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6807682001813835
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.673 (+/-0.330) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.627 (+/-0.330) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.714 (+/-0.359) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.609 (+/-0.313) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.714 (+/-0.359) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.658 (+/-0.387) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.647 (+/-0.401) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.714 (+/-0.359) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.658 (+/-0.387) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.629 (+/-0.312) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.647 (+/-0.401) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.747 (+/-0.502) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.797 (+/-0.492) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.652 (+/-0.313) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.658 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.625 (+/-0.314) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.647 (+/-0.401) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.024) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.747 (+/-0.502) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.656 (+/-0.291) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.658 (+/-0.388) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.642 (+/-0.299) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.621 (+/-0.320) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.647 (+/-0.401) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.699 (+/-0.797) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.681 (+/-0.372) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.629 (+/-0.283) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.626 (+/-0.314) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.638 (+/-0.302) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.619 (+/-0.322) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.647 (+/-0.401) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.654 (+/-0.775) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.677 (+/-0.374) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.624 (+/-0.315) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.626 (+/-0.314) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.620 (+/-0.321) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.619 (+/-0.322) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.647 (+/-0.401) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.673 (+/-0.377) for {'C': 10.0, 'kernel': 'linear'}
0.631 (+/-0.312) for {'C': 100.0, 'kernel': 'linear'}
0.653 (+/-0.293) for {'C': 1000.0, 'kernel': 'linear'}
0.619 (+/-0.322) for {'C': 10000.0, 'kernel': 'linear'}
0.619 (+/-0.322) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.006) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.666 (+/-0.315) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.611 (+/-0.241) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.004) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.682 (+/-0.295) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.609 (+/-0.242) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.240) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.682 (+/-0.295) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.658 (+/-0.314) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.587 (+/-0.233) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.589 (+/-0.232) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.682 (+/-0.295) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.658 (+/-0.314) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.232) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.589 (+/-0.232) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.617 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.642 (+/-0.236) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.665 (+/-0.314) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.658 (+/-0.314) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.636 (+/-0.236) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.588 (+/-0.233) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.232) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.589 (+/-0.232) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.523 (+/-0.138) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.617 (+/-0.238) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.681 (+/-0.292) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.658 (+/-0.314) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.662 (+/-0.225) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.612 (+/-0.241) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.587 (+/-0.234) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.232) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.589 (+/-0.232) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.642 (+/-0.236) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.641 (+/-0.235) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.676 (+/-0.295) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.657 (+/-0.319) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.661 (+/-0.223) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.611 (+/-0.240) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.587 (+/-0.234) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.232) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.589 (+/-0.232) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.640 (+/-0.234) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.641 (+/-0.234) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.657 (+/-0.315) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.658 (+/-0.316) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.611 (+/-0.242) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.611 (+/-0.240) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.587 (+/-0.234) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.232) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.589 (+/-0.232) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.663 (+/-0.315) for {'C': 10.0, 'kernel': 'linear'}
0.661 (+/-0.315) for {'C': 100.0, 'kernel': 'linear'}
0.662 (+/-0.227) for {'C': 1000.0, 'kernel': 'linear'}
0.611 (+/-0.241) for {'C': 10000.0, 'kernel': 'linear'}
0.611 (+/-0.240) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.17      0.17      0.17         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6822115384615385
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.747 (+/-0.502) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.747 (+/-0.502) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.747 (+/-0.502) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.747 (+/-0.502) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.747 (+/-0.502) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.747 (+/-0.502) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.747 (+/-0.502) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.747 (+/-0.502) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.697 (+/-0.437) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.747 (+/-0.502) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.747 (+/-0.502) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.747 (+/-0.502) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.747 (+/-0.502) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.747 (+/-0.502) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.747 (+/-0.502) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.747 (+/-0.502) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.747 (+/-0.449) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.689 (+/-0.436) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.747 (+/-0.502) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.747 (+/-0.502) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.747 (+/-0.502) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.747 (+/-0.502) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.747 (+/-0.502) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.747 (+/-0.502) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.747 (+/-0.449) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.723 (+/-0.423) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.664 (+/-0.388) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.747 (+/-0.502) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.747 (+/-0.502) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.747 (+/-0.502) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.747 (+/-0.502) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.747 (+/-0.502) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.747 (+/-0.449) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.723 (+/-0.423) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.664 (+/-0.388) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.631 (+/-0.319) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.747 (+/-0.502) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.747 (+/-0.502) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.747 (+/-0.502) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.747 (+/-0.502) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.747 (+/-0.449) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.723 (+/-0.423) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.698 (+/-0.383) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.664 (+/-0.388) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.664 (+/-0.388) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.747 (+/-0.502) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.747 (+/-0.502) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.747 (+/-0.502) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.747 (+/-0.449) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.723 (+/-0.423) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.723 (+/-0.423) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.664 (+/-0.388) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.672 (+/-0.392) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.631 (+/-0.319) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.747 (+/-0.502) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.722 (+/-0.473) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.747 (+/-0.449) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.723 (+/-0.423) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.723 (+/-0.423) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.689 (+/-0.374) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.672 (+/-0.392) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.664 (+/-0.388) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.626 (+/-0.318) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.722 (+/-0.473) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.747 (+/-0.449) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.723 (+/-0.423) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.723 (+/-0.423) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.698 (+/-0.383) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.697 (+/-0.375) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.672 (+/-0.392) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.614 (+/-0.327) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.614 (+/-0.327) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.001}
0.722 (+/-0.473) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.747 (+/-0.449) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.723 (+/-0.423) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.723 (+/-0.423) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.698 (+/-0.383) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.697 (+/-0.375) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.672 (+/-0.392) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.622 (+/-0.336) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.614 (+/-0.327) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.614 (+/-0.327) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.449) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.723 (+/-0.423) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.723 (+/-0.423) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.698 (+/-0.383) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.697 (+/-0.375) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.672 (+/-0.392) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.657 (+/-0.390) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.614 (+/-0.327) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.614 (+/-0.327) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.614 (+/-0.327) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.449) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.723 (+/-0.423) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.723 (+/-0.423) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.698 (+/-0.383) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.706 (+/-0.383) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.697 (+/-0.375) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.656 (+/-0.392) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.621 (+/-0.320) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.618 (+/-0.322) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.706 (+/-0.383) for {'C': 10.0, 'kernel': 'linear'}
0.643 (+/-0.320) for {'C': 100.0, 'kernel': 'linear'}
0.653 (+/-0.293) for {'C': 1000.0, 'kernel': 'linear'}
0.619 (+/-0.322) for {'C': 10000.0, 'kernel': 'linear'}
0.619 (+/-0.322) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.25      0.17      0.20         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.99       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.617 (+/-0.238) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.617 (+/-0.238) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.617 (+/-0.238) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.617 (+/-0.238) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.617 (+/-0.238) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.617 (+/-0.238) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.617 (+/-0.238) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.617 (+/-0.238) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.616 (+/-0.237) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.617 (+/-0.238) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.617 (+/-0.238) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.617 (+/-0.238) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.617 (+/-0.238) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.617 (+/-0.238) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.617 (+/-0.238) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.617 (+/-0.238) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.641 (+/-0.236) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.616 (+/-0.237) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.617 (+/-0.238) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.617 (+/-0.238) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.617 (+/-0.238) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.617 (+/-0.238) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.617 (+/-0.238) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.617 (+/-0.238) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.641 (+/-0.236) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.666 (+/-0.315) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.616 (+/-0.237) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.617 (+/-0.238) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.617 (+/-0.238) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.617 (+/-0.238) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.617 (+/-0.238) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.617 (+/-0.238) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.641 (+/-0.236) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.666 (+/-0.315) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.616 (+/-0.237) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.616 (+/-0.236) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.617 (+/-0.238) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.617 (+/-0.238) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.617 (+/-0.238) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.617 (+/-0.238) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.641 (+/-0.236) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.666 (+/-0.315) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.666 (+/-0.315) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.616 (+/-0.237) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.616 (+/-0.237) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.617 (+/-0.238) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.617 (+/-0.238) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.617 (+/-0.238) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.641 (+/-0.236) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.666 (+/-0.315) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.666 (+/-0.315) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.616 (+/-0.237) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.616 (+/-0.238) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.615 (+/-0.237) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.617 (+/-0.238) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.617 (+/-0.238) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.641 (+/-0.236) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.666 (+/-0.315) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.666 (+/-0.315) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.641 (+/-0.235) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.616 (+/-0.238) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.616 (+/-0.238) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.614 (+/-0.238) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.617 (+/-0.238) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.641 (+/-0.236) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.666 (+/-0.315) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.666 (+/-0.315) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.666 (+/-0.315) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.641 (+/-0.236) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.616 (+/-0.238) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.589 (+/-0.230) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.587 (+/-0.233) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.641 (+/-0.236) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.666 (+/-0.315) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.666 (+/-0.315) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.666 (+/-0.315) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.641 (+/-0.236) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.616 (+/-0.238) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.589 (+/-0.231) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.588 (+/-0.232) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.587 (+/-0.234) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.001}
0.641 (+/-0.236) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.666 (+/-0.315) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.666 (+/-0.315) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.666 (+/-0.315) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.641 (+/-0.236) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.616 (+/-0.238) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.615 (+/-0.238) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.588 (+/-0.232) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.587 (+/-0.234) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.587 (+/-0.234) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.1}
0.641 (+/-0.236) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.315) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.666 (+/-0.315) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.666 (+/-0.315) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.666 (+/-0.315) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.641 (+/-0.236) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.614 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.613 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.612 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.587 (+/-0.234) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.587 (+/-0.233) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.666 (+/-0.315) for {'C': 10.0, 'kernel': 'linear'}
0.637 (+/-0.237) for {'C': 100.0, 'kernel': 'linear'}
0.662 (+/-0.227) for {'C': 1000.0, 'kernel': 'linear'}
0.611 (+/-0.241) for {'C': 10000.0, 'kernel': 'linear'}
0.611 (+/-0.240) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.20      0.17      0.18         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.99      0.99       629

本轮grid search结果，得到最好的参数选择是： {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.666025641025641
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.747 (+/-0.502) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.747 (+/-0.502) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.747 (+/-0.502) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.747 (+/-0.502) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.747 (+/-0.502) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.747 (+/-0.502) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.797 (+/-0.492) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.748 (+/-0.449) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.689 (+/-0.382) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.673 (+/-0.330) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.747 (+/-0.502) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.747 (+/-0.502) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.747 (+/-0.502) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.747 (+/-0.502) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.797 (+/-0.492) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.748 (+/-0.449) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.698 (+/-0.383) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.664 (+/-0.326) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.673 (+/-0.330) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.747 (+/-0.502) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.747 (+/-0.502) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.747 (+/-0.502) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.797 (+/-0.492) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.748 (+/-0.449) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.706 (+/-0.383) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.664 (+/-0.326) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.673 (+/-0.330) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.651 (+/-0.308) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.747 (+/-0.502) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.747 (+/-0.502) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.797 (+/-0.492) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.748 (+/-0.449) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.706 (+/-0.383) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.664 (+/-0.326) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.673 (+/-0.330) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.651 (+/-0.308) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.626 (+/-0.313) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.747 (+/-0.502) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.797 (+/-0.492) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.748 (+/-0.449) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.706 (+/-0.383) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.714 (+/-0.359) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.673 (+/-0.330) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.651 (+/-0.308) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.659 (+/-0.385) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.645 (+/-0.388) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.797 (+/-0.492) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.748 (+/-0.449) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.706 (+/-0.383) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.714 (+/-0.359) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.673 (+/-0.330) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.689 (+/-0.374) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.669 (+/-0.373) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.659 (+/-0.390) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.609 (+/-0.313) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.001}
0.797 (+/-0.492) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.748 (+/-0.449) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.706 (+/-0.383) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.714 (+/-0.359) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.673 (+/-0.330) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.689 (+/-0.374) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.669 (+/-0.373) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.663 (+/-0.383) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.647 (+/-0.387) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.618 (+/-0.322) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.1}
0.797 (+/-0.492) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.001}
0.748 (+/-0.449) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.706 (+/-0.383) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.714 (+/-0.359) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.673 (+/-0.330) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.689 (+/-0.374) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.669 (+/-0.373) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.662 (+/-0.384) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.659 (+/-0.387) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.622 (+/-0.319) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.614 (+/-0.327) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.1}
0.748 (+/-0.449) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.001}
0.706 (+/-0.383) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.714 (+/-0.359) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.673 (+/-0.330) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.689 (+/-0.374) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.669 (+/-0.373) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.662 (+/-0.384) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.658 (+/-0.387) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.630 (+/-0.328) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.618 (+/-0.322) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.614 (+/-0.327) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.1}
0.706 (+/-0.383) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.001}
0.714 (+/-0.359) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.673 (+/-0.330) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.689 (+/-0.374) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.669 (+/-0.373) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.662 (+/-0.384) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.658 (+/-0.387) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.655 (+/-0.392) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.622 (+/-0.319) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.614 (+/-0.327) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.614 (+/-0.327) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.1}
0.714 (+/-0.359) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.673 (+/-0.330) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.689 (+/-0.374) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.669 (+/-0.373) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.663 (+/-0.384) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.658 (+/-0.387) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.660 (+/-0.385) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.627 (+/-0.313) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.625 (+/-0.314) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.673 (+/-0.377) for {'C': 10.0, 'kernel': 'linear'}
0.631 (+/-0.312) for {'C': 100.0, 'kernel': 'linear'}
0.653 (+/-0.293) for {'C': 1000.0, 'kernel': 'linear'}
0.619 (+/-0.322) for {'C': 10000.0, 'kernel': 'linear'}
0.619 (+/-0.322) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.617 (+/-0.238) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.617 (+/-0.238) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.617 (+/-0.238) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.617 (+/-0.238) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.617 (+/-0.238) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.617 (+/-0.238) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.642 (+/-0.236) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.666 (+/-0.316) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.666 (+/-0.315) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.666 (+/-0.315) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.617 (+/-0.238) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.617 (+/-0.238) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.617 (+/-0.238) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.617 (+/-0.238) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.642 (+/-0.236) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.666 (+/-0.316) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.666 (+/-0.315) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.666 (+/-0.315) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.665 (+/-0.315) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.617 (+/-0.238) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.617 (+/-0.238) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.617 (+/-0.238) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.642 (+/-0.236) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.666 (+/-0.316) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.666 (+/-0.315) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.666 (+/-0.315) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.665 (+/-0.315) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.665 (+/-0.314) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.617 (+/-0.238) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.617 (+/-0.238) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.642 (+/-0.236) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.666 (+/-0.316) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.666 (+/-0.315) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.666 (+/-0.315) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.665 (+/-0.315) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.665 (+/-0.314) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.639 (+/-0.325) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.617 (+/-0.238) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.642 (+/-0.236) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.666 (+/-0.316) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.666 (+/-0.315) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.682 (+/-0.295) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.665 (+/-0.315) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.665 (+/-0.314) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.639 (+/-0.325) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.611 (+/-0.242) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.642 (+/-0.236) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.666 (+/-0.316) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.666 (+/-0.315) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.682 (+/-0.295) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.665 (+/-0.315) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.665 (+/-0.315) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.664 (+/-0.315) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.636 (+/-0.327) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.609 (+/-0.242) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.001}
0.642 (+/-0.236) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.666 (+/-0.316) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.666 (+/-0.315) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.682 (+/-0.295) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.665 (+/-0.315) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.665 (+/-0.315) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.664 (+/-0.315) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.661 (+/-0.315) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.634 (+/-0.327) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.609 (+/-0.242) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.1}
0.642 (+/-0.236) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.316) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.666 (+/-0.315) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.682 (+/-0.295) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.665 (+/-0.315) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.665 (+/-0.315) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.664 (+/-0.315) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.661 (+/-0.314) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.659 (+/-0.314) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.635 (+/-0.325) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.585 (+/-0.237) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.1}
0.666 (+/-0.316) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.315) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.682 (+/-0.295) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.665 (+/-0.315) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.665 (+/-0.315) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.664 (+/-0.315) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.661 (+/-0.314) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.658 (+/-0.314) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.635 (+/-0.325) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.610 (+/-0.242) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.586 (+/-0.236) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.1}
0.666 (+/-0.315) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.001}
0.682 (+/-0.295) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.665 (+/-0.315) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.665 (+/-0.315) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.664 (+/-0.315) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.661 (+/-0.314) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.658 (+/-0.314) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.635 (+/-0.325) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.635 (+/-0.324) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.586 (+/-0.236) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.587 (+/-0.234) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.1}
0.682 (+/-0.295) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.665 (+/-0.315) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.665 (+/-0.315) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.664 (+/-0.315) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.661 (+/-0.315) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.658 (+/-0.314) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.660 (+/-0.313) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.660 (+/-0.313) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.636 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.587 (+/-0.235) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.587 (+/-0.233) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.663 (+/-0.315) for {'C': 10.0, 'kernel': 'linear'}
0.661 (+/-0.315) for {'C': 100.0, 'kernel': 'linear'}
0.662 (+/-0.227) for {'C': 1000.0, 'kernel': 'linear'}
0.611 (+/-0.241) for {'C': 10000.0, 'kernel': 'linear'}
0.611 (+/-0.240) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.17      0.17      0.17         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6822115384615385
循环迭代之前，delta is: [3.69042656e+01 5.84893192e-03]
执行tunemodel()函数前，使用的grid search parameter是：
 [{'C': array([ 39.81071706,  43.65158322,  47.86300923,  52.48074602,
        57.54399373,  63.09573445,  69.18309709,  75.8577575 ,
        83.17637711,  91.20108394, 100.        ]), 'kernel': ['rbf'], 'gamma': array([0.01      , 0.01096478, 0.01202264, 0.01318257, 0.0144544 ,
       0.01584893, 0.01737801, 0.01905461, 0.02089296, 0.02290868,
       0.02511886])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.747 (+/-0.502) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.747 (+/-0.502) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.747 (+/-0.502) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.747 (+/-0.502) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.747 (+/-0.502) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.747 (+/-0.502) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.722 (+/-0.473) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.747 (+/-0.449) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.747 (+/-0.449) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.747 (+/-0.449) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.747 (+/-0.449) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.747 (+/-0.502) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.747 (+/-0.502) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.747 (+/-0.502) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.747 (+/-0.502) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.747 (+/-0.502) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.722 (+/-0.473) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.747 (+/-0.449) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.747 (+/-0.449) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.747 (+/-0.449) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.747 (+/-0.449) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.747 (+/-0.449) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.747 (+/-0.502) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.747 (+/-0.502) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.747 (+/-0.502) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.747 (+/-0.502) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.722 (+/-0.473) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.747 (+/-0.449) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.747 (+/-0.449) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.747 (+/-0.449) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.747 (+/-0.449) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.747 (+/-0.449) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.748 (+/-0.449) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.747 (+/-0.502) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.747 (+/-0.502) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.747 (+/-0.502) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.722 (+/-0.473) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.747 (+/-0.449) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.747 (+/-0.449) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.747 (+/-0.449) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.747 (+/-0.449) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.747 (+/-0.449) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.748 (+/-0.449) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.748 (+/-0.449) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.747 (+/-0.502) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.747 (+/-0.502) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.722 (+/-0.473) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.747 (+/-0.449) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.747 (+/-0.449) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.747 (+/-0.449) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.747 (+/-0.449) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.747 (+/-0.449) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.748 (+/-0.449) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.748 (+/-0.449) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.748 (+/-0.449) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.747 (+/-0.502) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.722 (+/-0.473) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.747 (+/-0.449) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.747 (+/-0.449) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.747 (+/-0.449) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.747 (+/-0.449) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.747 (+/-0.449) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.748 (+/-0.449) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.748 (+/-0.449) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.748 (+/-0.449) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.723 (+/-0.423) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.722 (+/-0.473) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.747 (+/-0.449) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.747 (+/-0.449) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.747 (+/-0.449) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.747 (+/-0.449) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.747 (+/-0.449) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.748 (+/-0.449) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.748 (+/-0.449) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.748 (+/-0.449) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.723 (+/-0.423) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.723 (+/-0.423) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.747 (+/-0.449) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.747 (+/-0.449) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.747 (+/-0.449) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.747 (+/-0.449) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.747 (+/-0.449) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.748 (+/-0.449) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.748 (+/-0.449) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.748 (+/-0.449) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.723 (+/-0.423) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.723 (+/-0.423) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.698 (+/-0.383) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.747 (+/-0.449) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.747 (+/-0.449) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.747 (+/-0.449) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.747 (+/-0.449) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.748 (+/-0.449) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.748 (+/-0.449) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.748 (+/-0.449) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.723 (+/-0.423) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.723 (+/-0.423) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.698 (+/-0.383) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.698 (+/-0.383) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.747 (+/-0.449) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.747 (+/-0.449) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.748 (+/-0.449) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.748 (+/-0.449) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.748 (+/-0.449) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.748 (+/-0.449) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.723 (+/-0.423) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.723 (+/-0.423) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.698 (+/-0.383) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.698 (+/-0.383) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.698 (+/-0.383) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.747 (+/-0.449) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.748 (+/-0.449) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.748 (+/-0.449) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.748 (+/-0.449) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.748 (+/-0.449) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.723 (+/-0.423) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.723 (+/-0.423) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.698 (+/-0.383) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.698 (+/-0.383) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.723 (+/-0.423) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.723 (+/-0.423) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.706 (+/-0.383) for {'C': 10.0, 'kernel': 'linear'}
0.643 (+/-0.320) for {'C': 100.0, 'kernel': 'linear'}
0.653 (+/-0.293) for {'C': 1000.0, 'kernel': 'linear'}
0.619 (+/-0.322) for {'C': 10000.0, 'kernel': 'linear'}
0.619 (+/-0.322) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.20      0.17      0.18         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.99      0.99       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.617 (+/-0.238) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.617 (+/-0.238) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.617 (+/-0.238) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.617 (+/-0.238) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.617 (+/-0.238) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.617 (+/-0.238) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.617 (+/-0.238) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.641 (+/-0.236) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.641 (+/-0.236) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.641 (+/-0.236) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.641 (+/-0.236) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.617 (+/-0.238) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.617 (+/-0.238) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.617 (+/-0.238) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.617 (+/-0.238) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.617 (+/-0.238) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.617 (+/-0.238) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.641 (+/-0.236) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.641 (+/-0.236) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.641 (+/-0.236) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.641 (+/-0.236) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.641 (+/-0.236) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.617 (+/-0.238) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.617 (+/-0.238) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.617 (+/-0.238) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.617 (+/-0.238) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.617 (+/-0.238) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.641 (+/-0.236) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.641 (+/-0.236) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.641 (+/-0.236) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.641 (+/-0.236) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.641 (+/-0.236) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.666 (+/-0.316) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.617 (+/-0.238) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.617 (+/-0.238) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.617 (+/-0.238) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.617 (+/-0.238) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.641 (+/-0.236) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.641 (+/-0.236) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.641 (+/-0.236) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.641 (+/-0.236) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.641 (+/-0.236) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.666 (+/-0.316) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.666 (+/-0.316) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.617 (+/-0.238) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.617 (+/-0.238) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.617 (+/-0.238) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.641 (+/-0.236) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.641 (+/-0.236) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.641 (+/-0.236) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.641 (+/-0.236) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.641 (+/-0.236) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.666 (+/-0.316) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.666 (+/-0.316) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.666 (+/-0.316) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.617 (+/-0.238) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.617 (+/-0.238) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.641 (+/-0.236) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.641 (+/-0.236) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.641 (+/-0.236) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.641 (+/-0.236) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.641 (+/-0.236) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.666 (+/-0.316) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.666 (+/-0.316) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.666 (+/-0.316) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.666 (+/-0.315) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.617 (+/-0.238) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.641 (+/-0.236) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.641 (+/-0.236) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.641 (+/-0.236) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.641 (+/-0.236) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.641 (+/-0.236) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.666 (+/-0.316) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.666 (+/-0.316) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.666 (+/-0.316) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.666 (+/-0.315) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.666 (+/-0.315) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.641 (+/-0.236) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.641 (+/-0.236) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.641 (+/-0.236) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.641 (+/-0.236) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.641 (+/-0.236) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.666 (+/-0.316) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.666 (+/-0.316) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.666 (+/-0.316) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.666 (+/-0.315) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.666 (+/-0.315) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.666 (+/-0.315) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.641 (+/-0.236) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.641 (+/-0.236) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.641 (+/-0.236) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.641 (+/-0.236) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.666 (+/-0.316) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.666 (+/-0.316) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.666 (+/-0.316) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.666 (+/-0.315) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.666 (+/-0.315) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.666 (+/-0.315) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.666 (+/-0.315) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.641 (+/-0.236) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.641 (+/-0.236) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.666 (+/-0.316) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.666 (+/-0.316) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.666 (+/-0.316) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.666 (+/-0.316) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.666 (+/-0.315) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.666 (+/-0.315) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.666 (+/-0.315) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.666 (+/-0.315) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.666 (+/-0.315) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.641 (+/-0.236) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.666 (+/-0.316) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.666 (+/-0.316) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.666 (+/-0.316) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.666 (+/-0.316) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.666 (+/-0.315) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.666 (+/-0.315) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.666 (+/-0.315) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.666 (+/-0.315) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.666 (+/-0.315) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.666 (+/-0.315) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.666 (+/-0.315) for {'C': 10.0, 'kernel': 'linear'}
0.637 (+/-0.237) for {'C': 100.0, 'kernel': 'linear'}
0.662 (+/-0.227) for {'C': 1000.0, 'kernel': 'linear'}
0.611 (+/-0.241) for {'C': 10000.0, 'kernel': 'linear'}
0.611 (+/-0.240) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.20      0.17      0.18         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.99      0.99       629

本轮grid search结果，得到最好的参数选择是： {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6663461538461538
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.748 (+/-0.449) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.748 (+/-0.449) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.748 (+/-0.449) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.748 (+/-0.449) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.748 (+/-0.449) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.706 (+/-0.383) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.685 (+/-0.384) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.677 (+/-0.374) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.727 (+/-0.397) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.710 (+/-0.363) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.664 (+/-0.326) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.748 (+/-0.449) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.748 (+/-0.449) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.748 (+/-0.449) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.748 (+/-0.449) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.706 (+/-0.383) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.685 (+/-0.384) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.677 (+/-0.374) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.727 (+/-0.397) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.710 (+/-0.363) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.664 (+/-0.326) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.664 (+/-0.326) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.748 (+/-0.449) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.748 (+/-0.449) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.748 (+/-0.449) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.706 (+/-0.383) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.685 (+/-0.384) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.677 (+/-0.374) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.727 (+/-0.397) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.710 (+/-0.363) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.664 (+/-0.326) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.664 (+/-0.326) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.673 (+/-0.330) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.748 (+/-0.449) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.748 (+/-0.449) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.706 (+/-0.383) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.685 (+/-0.384) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.677 (+/-0.374) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.727 (+/-0.397) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.710 (+/-0.363) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.664 (+/-0.326) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.664 (+/-0.326) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.673 (+/-0.330) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.673 (+/-0.330) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.748 (+/-0.449) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.706 (+/-0.383) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.685 (+/-0.384) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.677 (+/-0.374) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.727 (+/-0.397) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.710 (+/-0.363) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.714 (+/-0.359) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.664 (+/-0.326) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.673 (+/-0.330) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.673 (+/-0.330) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.673 (+/-0.330) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.706 (+/-0.383) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.685 (+/-0.384) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.677 (+/-0.374) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.727 (+/-0.397) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.710 (+/-0.363) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.714 (+/-0.359) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.714 (+/-0.359) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.673 (+/-0.330) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.673 (+/-0.330) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.673 (+/-0.330) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.673 (+/-0.330) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.685 (+/-0.384) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.677 (+/-0.374) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.727 (+/-0.397) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.710 (+/-0.363) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.714 (+/-0.359) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.714 (+/-0.359) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.673 (+/-0.330) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.673 (+/-0.330) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.673 (+/-0.330) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.673 (+/-0.330) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.673 (+/-0.330) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.677 (+/-0.374) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.727 (+/-0.397) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.710 (+/-0.363) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.714 (+/-0.359) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.714 (+/-0.359) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.673 (+/-0.330) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.673 (+/-0.330) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.673 (+/-0.330) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.673 (+/-0.330) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.673 (+/-0.330) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.673 (+/-0.330) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.727 (+/-0.397) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.710 (+/-0.363) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.714 (+/-0.359) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.714 (+/-0.359) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.673 (+/-0.330) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.673 (+/-0.330) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.673 (+/-0.330) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.673 (+/-0.330) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.673 (+/-0.330) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.673 (+/-0.330) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.664 (+/-0.317) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.710 (+/-0.363) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.714 (+/-0.359) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.714 (+/-0.359) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.673 (+/-0.330) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.673 (+/-0.330) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.673 (+/-0.330) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.673 (+/-0.330) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.673 (+/-0.330) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.673 (+/-0.330) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.664 (+/-0.317) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.664 (+/-0.317) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.714 (+/-0.359) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.714 (+/-0.359) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.673 (+/-0.330) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.673 (+/-0.330) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.673 (+/-0.330) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.673 (+/-0.330) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.673 (+/-0.330) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.673 (+/-0.330) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.664 (+/-0.317) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.689 (+/-0.374) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.689 (+/-0.374) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.673 (+/-0.377) for {'C': 10.0, 'kernel': 'linear'}
0.631 (+/-0.312) for {'C': 100.0, 'kernel': 'linear'}
0.653 (+/-0.293) for {'C': 1000.0, 'kernel': 'linear'}
0.619 (+/-0.322) for {'C': 10000.0, 'kernel': 'linear'}
0.619 (+/-0.322) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.20      0.17      0.18         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.99      0.99       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.666 (+/-0.316) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.666 (+/-0.316) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.666 (+/-0.316) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.666 (+/-0.316) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.666 (+/-0.316) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.666 (+/-0.315) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.666 (+/-0.315) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.665 (+/-0.314) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.682 (+/-0.294) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.682 (+/-0.295) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.666 (+/-0.315) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.666 (+/-0.316) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.666 (+/-0.316) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.666 (+/-0.316) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.666 (+/-0.316) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.666 (+/-0.315) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.666 (+/-0.315) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.665 (+/-0.314) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.682 (+/-0.294) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.682 (+/-0.295) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.666 (+/-0.315) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.666 (+/-0.315) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.666 (+/-0.316) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.666 (+/-0.316) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.666 (+/-0.316) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.666 (+/-0.315) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.666 (+/-0.315) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.665 (+/-0.314) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.682 (+/-0.294) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.682 (+/-0.295) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.666 (+/-0.315) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.666 (+/-0.315) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.666 (+/-0.315) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.666 (+/-0.316) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.666 (+/-0.316) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.666 (+/-0.315) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.666 (+/-0.315) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.665 (+/-0.314) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.682 (+/-0.294) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.682 (+/-0.295) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.666 (+/-0.315) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.666 (+/-0.315) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.666 (+/-0.315) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.666 (+/-0.315) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.666 (+/-0.316) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.666 (+/-0.315) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.666 (+/-0.315) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.665 (+/-0.314) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.682 (+/-0.294) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.682 (+/-0.295) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.682 (+/-0.295) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.666 (+/-0.315) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.666 (+/-0.315) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.666 (+/-0.315) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.666 (+/-0.315) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.666 (+/-0.315) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.666 (+/-0.315) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.665 (+/-0.314) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.682 (+/-0.294) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.682 (+/-0.295) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.682 (+/-0.295) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.682 (+/-0.295) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.666 (+/-0.315) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.666 (+/-0.315) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.666 (+/-0.315) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.665 (+/-0.315) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.666 (+/-0.315) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.665 (+/-0.314) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.682 (+/-0.294) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.682 (+/-0.295) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.682 (+/-0.295) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.682 (+/-0.295) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.666 (+/-0.315) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.666 (+/-0.315) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.666 (+/-0.315) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.665 (+/-0.315) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.665 (+/-0.315) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.665 (+/-0.314) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.682 (+/-0.294) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.682 (+/-0.295) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.682 (+/-0.295) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.682 (+/-0.295) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.666 (+/-0.315) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.666 (+/-0.315) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.666 (+/-0.315) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.665 (+/-0.315) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.665 (+/-0.315) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.665 (+/-0.315) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.682 (+/-0.294) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.682 (+/-0.295) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.682 (+/-0.295) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.682 (+/-0.295) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.666 (+/-0.315) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.666 (+/-0.315) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.666 (+/-0.315) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.665 (+/-0.315) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.665 (+/-0.315) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.665 (+/-0.315) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.665 (+/-0.315) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.682 (+/-0.295) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.682 (+/-0.295) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.682 (+/-0.295) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.666 (+/-0.315) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.666 (+/-0.315) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.666 (+/-0.315) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.665 (+/-0.315) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.665 (+/-0.315) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.665 (+/-0.315) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.665 (+/-0.315) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.665 (+/-0.315) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.682 (+/-0.295) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.682 (+/-0.295) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.666 (+/-0.315) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.666 (+/-0.315) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.666 (+/-0.315) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.665 (+/-0.315) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.665 (+/-0.315) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.665 (+/-0.315) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.665 (+/-0.315) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.665 (+/-0.315) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.665 (+/-0.315) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.663 (+/-0.315) for {'C': 10.0, 'kernel': 'linear'}
0.661 (+/-0.315) for {'C': 100.0, 'kernel': 'linear'}
0.662 (+/-0.227) for {'C': 1000.0, 'kernel': 'linear'}
0.611 (+/-0.241) for {'C': 10000.0, 'kernel': 'linear'}
0.611 (+/-0.240) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.17      0.17      0.17         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6822115384615385
第0轮loop中，tunemodel函数得到的最优参数是： ([{'C': array([52.48074602, 53.45643594, 54.45026528, 55.4625713 , 56.49369748,
       57.54399373, 58.61381645, 59.70352866, 60.81350013, 61.94410751,
       63.09573445]), 'kernel': ['rbf'], 'gamma': array([0.01584893, 0.01614359, 0.01644372, 0.01674943, 0.01706082,
       0.01737801, 0.01770109, 0.01803018, 0.01836538, 0.01870682,
       0.01905461])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}], {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.017378008287493765}, 0.6822115384615385)
这是第0次迭代微调C和gamma。
第0次迭代，得到delta： [5.55174071e+00 1.52907636e-03]
执行tunemodel()函数前，使用的grid search parameter是：
 [{'C': array([52.48074602, 53.45643594, 54.45026528, 55.4625713 , 56.49369748,
       57.54399373, 58.61381645, 59.70352866, 60.81350013, 61.94410751,
       63.09573445]), 'kernel': ['rbf'], 'gamma': array([0.01584893, 0.01614359, 0.01644372, 0.01674943, 0.01706082,
       0.01737801, 0.01770109, 0.01803018, 0.01836538, 0.01870682,
       0.01905461])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.747 (+/-0.449) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.747 (+/-0.449) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.016143585568264868}
0.747 (+/-0.449) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.747 (+/-0.449) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.747 (+/-0.449) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.747 (+/-0.449) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.747 (+/-0.449) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.017701089583174213}
0.747 (+/-0.449) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.018030177408595697}
0.747 (+/-0.449) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.018365383433483477}
0.747 (+/-0.449) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.018706821403658012}
0.747 (+/-0.449) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.747 (+/-0.449) for {'C': 53.45643593969718, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.747 (+/-0.449) for {'C': 53.45643593969718, 'kernel': 'rbf', 'gamma': 0.016143585568264868}
0.747 (+/-0.449) for {'C': 53.45643593969718, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.747 (+/-0.449) for {'C': 53.45643593969718, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.747 (+/-0.449) for {'C': 53.45643593969718, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.747 (+/-0.449) for {'C': 53.45643593969718, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.747 (+/-0.449) for {'C': 53.45643593969718, 'kernel': 'rbf', 'gamma': 0.017701089583174213}
0.747 (+/-0.449) for {'C': 53.45643593969718, 'kernel': 'rbf', 'gamma': 0.018030177408595697}
0.747 (+/-0.449) for {'C': 53.45643593969718, 'kernel': 'rbf', 'gamma': 0.018365383433483477}
0.747 (+/-0.449) for {'C': 53.45643593969718, 'kernel': 'rbf', 'gamma': 0.018706821403658012}
0.747 (+/-0.449) for {'C': 53.45643593969718, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.747 (+/-0.449) for {'C': 54.450265284242114, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.747 (+/-0.449) for {'C': 54.450265284242114, 'kernel': 'rbf', 'gamma': 0.016143585568264868}
0.747 (+/-0.449) for {'C': 54.450265284242114, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.747 (+/-0.449) for {'C': 54.450265284242114, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.747 (+/-0.449) for {'C': 54.450265284242114, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.747 (+/-0.449) for {'C': 54.450265284242114, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.747 (+/-0.449) for {'C': 54.450265284242114, 'kernel': 'rbf', 'gamma': 0.017701089583174213}
0.747 (+/-0.449) for {'C': 54.450265284242114, 'kernel': 'rbf', 'gamma': 0.018030177408595697}
0.747 (+/-0.449) for {'C': 54.450265284242114, 'kernel': 'rbf', 'gamma': 0.018365383433483477}
0.747 (+/-0.449) for {'C': 54.450265284242114, 'kernel': 'rbf', 'gamma': 0.018706821403658012}
0.747 (+/-0.449) for {'C': 54.450265284242114, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.747 (+/-0.449) for {'C': 55.462571295791086, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.747 (+/-0.449) for {'C': 55.462571295791086, 'kernel': 'rbf', 'gamma': 0.016143585568264868}
0.747 (+/-0.449) for {'C': 55.462571295791086, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.747 (+/-0.449) for {'C': 55.462571295791086, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.747 (+/-0.449) for {'C': 55.462571295791086, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.747 (+/-0.449) for {'C': 55.462571295791086, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.747 (+/-0.449) for {'C': 55.462571295791086, 'kernel': 'rbf', 'gamma': 0.017701089583174213}
0.747 (+/-0.449) for {'C': 55.462571295791086, 'kernel': 'rbf', 'gamma': 0.018030177408595697}
0.747 (+/-0.449) for {'C': 55.462571295791086, 'kernel': 'rbf', 'gamma': 0.018365383433483477}
0.747 (+/-0.449) for {'C': 55.462571295791086, 'kernel': 'rbf', 'gamma': 0.018706821403658012}
0.747 (+/-0.449) for {'C': 55.462571295791086, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.747 (+/-0.449) for {'C': 56.49369748123024, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.747 (+/-0.449) for {'C': 56.49369748123024, 'kernel': 'rbf', 'gamma': 0.016143585568264868}
0.747 (+/-0.449) for {'C': 56.49369748123024, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.747 (+/-0.449) for {'C': 56.49369748123024, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.747 (+/-0.449) for {'C': 56.49369748123024, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.747 (+/-0.449) for {'C': 56.49369748123024, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.747 (+/-0.449) for {'C': 56.49369748123024, 'kernel': 'rbf', 'gamma': 0.017701089583174213}
0.747 (+/-0.449) for {'C': 56.49369748123024, 'kernel': 'rbf', 'gamma': 0.018030177408595697}
0.747 (+/-0.449) for {'C': 56.49369748123024, 'kernel': 'rbf', 'gamma': 0.018365383433483477}
0.747 (+/-0.449) for {'C': 56.49369748123024, 'kernel': 'rbf', 'gamma': 0.018706821403658012}
0.747 (+/-0.449) for {'C': 56.49369748123024, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.747 (+/-0.449) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.747 (+/-0.449) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.016143585568264868}
0.747 (+/-0.449) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.747 (+/-0.449) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.747 (+/-0.449) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.747 (+/-0.449) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.747 (+/-0.449) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.017701089583174213}
0.747 (+/-0.449) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.018030177408595697}
0.747 (+/-0.449) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.018365383433483477}
0.747 (+/-0.449) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.018706821403658012}
0.747 (+/-0.449) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.747 (+/-0.449) for {'C': 58.61381645140289, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.747 (+/-0.449) for {'C': 58.61381645140289, 'kernel': 'rbf', 'gamma': 0.016143585568264868}
0.747 (+/-0.449) for {'C': 58.61381645140289, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.747 (+/-0.449) for {'C': 58.61381645140289, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.747 (+/-0.449) for {'C': 58.61381645140289, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.747 (+/-0.449) for {'C': 58.61381645140289, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.747 (+/-0.449) for {'C': 58.61381645140289, 'kernel': 'rbf', 'gamma': 0.017701089583174213}
0.747 (+/-0.449) for {'C': 58.61381645140289, 'kernel': 'rbf', 'gamma': 0.018030177408595697}
0.747 (+/-0.449) for {'C': 58.61381645140289, 'kernel': 'rbf', 'gamma': 0.018365383433483477}
0.747 (+/-0.449) for {'C': 58.61381645140289, 'kernel': 'rbf', 'gamma': 0.018706821403658012}
0.747 (+/-0.449) for {'C': 58.61381645140289, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.747 (+/-0.449) for {'C': 59.70352865838368, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.747 (+/-0.449) for {'C': 59.70352865838368, 'kernel': 'rbf', 'gamma': 0.016143585568264868}
0.747 (+/-0.449) for {'C': 59.70352865838368, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.747 (+/-0.449) for {'C': 59.70352865838368, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.747 (+/-0.449) for {'C': 59.70352865838368, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.747 (+/-0.449) for {'C': 59.70352865838368, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.747 (+/-0.449) for {'C': 59.70352865838368, 'kernel': 'rbf', 'gamma': 0.017701089583174213}
0.747 (+/-0.449) for {'C': 59.70352865838368, 'kernel': 'rbf', 'gamma': 0.018030177408595697}
0.747 (+/-0.449) for {'C': 59.70352865838368, 'kernel': 'rbf', 'gamma': 0.018365383433483477}
0.747 (+/-0.449) for {'C': 59.70352865838368, 'kernel': 'rbf', 'gamma': 0.018706821403658012}
0.748 (+/-0.449) for {'C': 59.70352865838368, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.747 (+/-0.449) for {'C': 60.8135001278718, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.747 (+/-0.449) for {'C': 60.8135001278718, 'kernel': 'rbf', 'gamma': 0.016143585568264868}
0.747 (+/-0.449) for {'C': 60.8135001278718, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.747 (+/-0.449) for {'C': 60.8135001278718, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.747 (+/-0.449) for {'C': 60.8135001278718, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.747 (+/-0.449) for {'C': 60.8135001278718, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.747 (+/-0.449) for {'C': 60.8135001278718, 'kernel': 'rbf', 'gamma': 0.017701089583174213}
0.747 (+/-0.449) for {'C': 60.8135001278718, 'kernel': 'rbf', 'gamma': 0.018030177408595697}
0.747 (+/-0.449) for {'C': 60.8135001278718, 'kernel': 'rbf', 'gamma': 0.018365383433483477}
0.748 (+/-0.449) for {'C': 60.8135001278718, 'kernel': 'rbf', 'gamma': 0.018706821403658012}
0.748 (+/-0.449) for {'C': 60.8135001278718, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.747 (+/-0.449) for {'C': 61.944107507678126, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.747 (+/-0.449) for {'C': 61.944107507678126, 'kernel': 'rbf', 'gamma': 0.016143585568264868}
0.747 (+/-0.449) for {'C': 61.944107507678126, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.747 (+/-0.449) for {'C': 61.944107507678126, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.747 (+/-0.449) for {'C': 61.944107507678126, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.747 (+/-0.449) for {'C': 61.944107507678126, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.747 (+/-0.449) for {'C': 61.944107507678126, 'kernel': 'rbf', 'gamma': 0.017701089583174213}
0.747 (+/-0.449) for {'C': 61.944107507678126, 'kernel': 'rbf', 'gamma': 0.018030177408595697}
0.748 (+/-0.449) for {'C': 61.944107507678126, 'kernel': 'rbf', 'gamma': 0.018365383433483477}
0.748 (+/-0.449) for {'C': 61.944107507678126, 'kernel': 'rbf', 'gamma': 0.018706821403658012}
0.748 (+/-0.449) for {'C': 61.944107507678126, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.747 (+/-0.449) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.747 (+/-0.449) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.016143585568264868}
0.747 (+/-0.449) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.747 (+/-0.449) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.747 (+/-0.449) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.747 (+/-0.449) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.747 (+/-0.449) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.017701089583174213}
0.748 (+/-0.449) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.018030177408595697}
0.748 (+/-0.449) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.018365383433483477}
0.748 (+/-0.449) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.018706821403658012}
0.748 (+/-0.449) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.706 (+/-0.383) for {'C': 10.0, 'kernel': 'linear'}
0.643 (+/-0.320) for {'C': 100.0, 'kernel': 'linear'}
0.653 (+/-0.293) for {'C': 1000.0, 'kernel': 'linear'}
0.619 (+/-0.322) for {'C': 10000.0, 'kernel': 'linear'}
0.619 (+/-0.322) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.20      0.17      0.18         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.99      0.99       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.641 (+/-0.236) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.641 (+/-0.236) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.016143585568264868}
0.641 (+/-0.236) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.641 (+/-0.236) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.641 (+/-0.236) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.641 (+/-0.236) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.641 (+/-0.236) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.017701089583174213}
0.641 (+/-0.236) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.018030177408595697}
0.641 (+/-0.236) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.018365383433483477}
0.641 (+/-0.236) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.018706821403658012}
0.641 (+/-0.236) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.641 (+/-0.236) for {'C': 53.45643593969718, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.641 (+/-0.236) for {'C': 53.45643593969718, 'kernel': 'rbf', 'gamma': 0.016143585568264868}
0.641 (+/-0.236) for {'C': 53.45643593969718, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.641 (+/-0.236) for {'C': 53.45643593969718, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.641 (+/-0.236) for {'C': 53.45643593969718, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.641 (+/-0.236) for {'C': 53.45643593969718, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.641 (+/-0.236) for {'C': 53.45643593969718, 'kernel': 'rbf', 'gamma': 0.017701089583174213}
0.641 (+/-0.236) for {'C': 53.45643593969718, 'kernel': 'rbf', 'gamma': 0.018030177408595697}
0.641 (+/-0.236) for {'C': 53.45643593969718, 'kernel': 'rbf', 'gamma': 0.018365383433483477}
0.641 (+/-0.236) for {'C': 53.45643593969718, 'kernel': 'rbf', 'gamma': 0.018706821403658012}
0.641 (+/-0.236) for {'C': 53.45643593969718, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.641 (+/-0.236) for {'C': 54.450265284242114, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.641 (+/-0.236) for {'C': 54.450265284242114, 'kernel': 'rbf', 'gamma': 0.016143585568264868}
0.641 (+/-0.236) for {'C': 54.450265284242114, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.641 (+/-0.236) for {'C': 54.450265284242114, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.641 (+/-0.236) for {'C': 54.450265284242114, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.641 (+/-0.236) for {'C': 54.450265284242114, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.641 (+/-0.236) for {'C': 54.450265284242114, 'kernel': 'rbf', 'gamma': 0.017701089583174213}
0.641 (+/-0.236) for {'C': 54.450265284242114, 'kernel': 'rbf', 'gamma': 0.018030177408595697}
0.641 (+/-0.236) for {'C': 54.450265284242114, 'kernel': 'rbf', 'gamma': 0.018365383433483477}
0.641 (+/-0.236) for {'C': 54.450265284242114, 'kernel': 'rbf', 'gamma': 0.018706821403658012}
0.641 (+/-0.236) for {'C': 54.450265284242114, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.641 (+/-0.236) for {'C': 55.462571295791086, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.641 (+/-0.236) for {'C': 55.462571295791086, 'kernel': 'rbf', 'gamma': 0.016143585568264868}
0.641 (+/-0.236) for {'C': 55.462571295791086, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.641 (+/-0.236) for {'C': 55.462571295791086, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.641 (+/-0.236) for {'C': 55.462571295791086, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.641 (+/-0.236) for {'C': 55.462571295791086, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.641 (+/-0.236) for {'C': 55.462571295791086, 'kernel': 'rbf', 'gamma': 0.017701089583174213}
0.641 (+/-0.236) for {'C': 55.462571295791086, 'kernel': 'rbf', 'gamma': 0.018030177408595697}
0.641 (+/-0.236) for {'C': 55.462571295791086, 'kernel': 'rbf', 'gamma': 0.018365383433483477}
0.641 (+/-0.236) for {'C': 55.462571295791086, 'kernel': 'rbf', 'gamma': 0.018706821403658012}
0.641 (+/-0.236) for {'C': 55.462571295791086, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.641 (+/-0.236) for {'C': 56.49369748123024, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.641 (+/-0.236) for {'C': 56.49369748123024, 'kernel': 'rbf', 'gamma': 0.016143585568264868}
0.641 (+/-0.236) for {'C': 56.49369748123024, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.641 (+/-0.236) for {'C': 56.49369748123024, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.641 (+/-0.236) for {'C': 56.49369748123024, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.641 (+/-0.236) for {'C': 56.49369748123024, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.641 (+/-0.236) for {'C': 56.49369748123024, 'kernel': 'rbf', 'gamma': 0.017701089583174213}
0.641 (+/-0.236) for {'C': 56.49369748123024, 'kernel': 'rbf', 'gamma': 0.018030177408595697}
0.641 (+/-0.236) for {'C': 56.49369748123024, 'kernel': 'rbf', 'gamma': 0.018365383433483477}
0.641 (+/-0.236) for {'C': 56.49369748123024, 'kernel': 'rbf', 'gamma': 0.018706821403658012}
0.641 (+/-0.236) for {'C': 56.49369748123024, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.641 (+/-0.236) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.641 (+/-0.236) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.016143585568264868}
0.641 (+/-0.236) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.641 (+/-0.236) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.641 (+/-0.236) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.641 (+/-0.236) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.641 (+/-0.236) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.017701089583174213}
0.641 (+/-0.236) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.018030177408595697}
0.641 (+/-0.236) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.018365383433483477}
0.641 (+/-0.236) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.018706821403658012}
0.641 (+/-0.236) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.641 (+/-0.236) for {'C': 58.61381645140289, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.641 (+/-0.236) for {'C': 58.61381645140289, 'kernel': 'rbf', 'gamma': 0.016143585568264868}
0.641 (+/-0.236) for {'C': 58.61381645140289, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.641 (+/-0.236) for {'C': 58.61381645140289, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.641 (+/-0.236) for {'C': 58.61381645140289, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.641 (+/-0.236) for {'C': 58.61381645140289, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.641 (+/-0.236) for {'C': 58.61381645140289, 'kernel': 'rbf', 'gamma': 0.017701089583174213}
0.641 (+/-0.236) for {'C': 58.61381645140289, 'kernel': 'rbf', 'gamma': 0.018030177408595697}
0.641 (+/-0.236) for {'C': 58.61381645140289, 'kernel': 'rbf', 'gamma': 0.018365383433483477}
0.641 (+/-0.236) for {'C': 58.61381645140289, 'kernel': 'rbf', 'gamma': 0.018706821403658012}
0.641 (+/-0.236) for {'C': 58.61381645140289, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.641 (+/-0.236) for {'C': 59.70352865838368, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.641 (+/-0.236) for {'C': 59.70352865838368, 'kernel': 'rbf', 'gamma': 0.016143585568264868}
0.641 (+/-0.236) for {'C': 59.70352865838368, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.641 (+/-0.236) for {'C': 59.70352865838368, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.641 (+/-0.236) for {'C': 59.70352865838368, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.641 (+/-0.236) for {'C': 59.70352865838368, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.641 (+/-0.236) for {'C': 59.70352865838368, 'kernel': 'rbf', 'gamma': 0.017701089583174213}
0.641 (+/-0.236) for {'C': 59.70352865838368, 'kernel': 'rbf', 'gamma': 0.018030177408595697}
0.641 (+/-0.236) for {'C': 59.70352865838368, 'kernel': 'rbf', 'gamma': 0.018365383433483477}
0.641 (+/-0.236) for {'C': 59.70352865838368, 'kernel': 'rbf', 'gamma': 0.018706821403658012}
0.666 (+/-0.316) for {'C': 59.70352865838368, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.641 (+/-0.236) for {'C': 60.8135001278718, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.641 (+/-0.236) for {'C': 60.8135001278718, 'kernel': 'rbf', 'gamma': 0.016143585568264868}
0.641 (+/-0.236) for {'C': 60.8135001278718, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.641 (+/-0.236) for {'C': 60.8135001278718, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.641 (+/-0.236) for {'C': 60.8135001278718, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.641 (+/-0.236) for {'C': 60.8135001278718, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.641 (+/-0.236) for {'C': 60.8135001278718, 'kernel': 'rbf', 'gamma': 0.017701089583174213}
0.641 (+/-0.236) for {'C': 60.8135001278718, 'kernel': 'rbf', 'gamma': 0.018030177408595697}
0.641 (+/-0.236) for {'C': 60.8135001278718, 'kernel': 'rbf', 'gamma': 0.018365383433483477}
0.666 (+/-0.316) for {'C': 60.8135001278718, 'kernel': 'rbf', 'gamma': 0.018706821403658012}
0.666 (+/-0.316) for {'C': 60.8135001278718, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.641 (+/-0.236) for {'C': 61.944107507678126, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.641 (+/-0.236) for {'C': 61.944107507678126, 'kernel': 'rbf', 'gamma': 0.016143585568264868}
0.641 (+/-0.236) for {'C': 61.944107507678126, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.641 (+/-0.236) for {'C': 61.944107507678126, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.641 (+/-0.236) for {'C': 61.944107507678126, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.641 (+/-0.236) for {'C': 61.944107507678126, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.641 (+/-0.236) for {'C': 61.944107507678126, 'kernel': 'rbf', 'gamma': 0.017701089583174213}
0.641 (+/-0.236) for {'C': 61.944107507678126, 'kernel': 'rbf', 'gamma': 0.018030177408595697}
0.666 (+/-0.316) for {'C': 61.944107507678126, 'kernel': 'rbf', 'gamma': 0.018365383433483477}
0.666 (+/-0.316) for {'C': 61.944107507678126, 'kernel': 'rbf', 'gamma': 0.018706821403658012}
0.666 (+/-0.316) for {'C': 61.944107507678126, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.641 (+/-0.236) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.641 (+/-0.236) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.016143585568264868}
0.641 (+/-0.236) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.641 (+/-0.236) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.641 (+/-0.236) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.641 (+/-0.236) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.641 (+/-0.236) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.017701089583174213}
0.666 (+/-0.316) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.018030177408595697}
0.666 (+/-0.316) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.018365383433483477}
0.666 (+/-0.316) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.018706821403658012}
0.666 (+/-0.316) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.666 (+/-0.315) for {'C': 10.0, 'kernel': 'linear'}
0.637 (+/-0.237) for {'C': 100.0, 'kernel': 'linear'}
0.662 (+/-0.227) for {'C': 1000.0, 'kernel': 'linear'}
0.611 (+/-0.241) for {'C': 10000.0, 'kernel': 'linear'}
0.611 (+/-0.240) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.20      0.17      0.18         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.99      0.99       629

本轮grid search结果，得到最好的参数选择是： {'C': 59.70352865838368, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6663461538461538
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.727 (+/-0.397) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.727 (+/-0.397) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.016143585568264868}
0.727 (+/-0.397) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.735 (+/-0.402) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.735 (+/-0.402) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.710 (+/-0.363) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.710 (+/-0.363) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.017701089583174213}
0.710 (+/-0.363) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.018030177408595697}
0.710 (+/-0.363) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.018365383433483477}
0.714 (+/-0.359) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.018706821403658012}
0.664 (+/-0.326) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.727 (+/-0.397) for {'C': 53.45643593969718, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.727 (+/-0.397) for {'C': 53.45643593969718, 'kernel': 'rbf', 'gamma': 0.016143585568264868}
0.735 (+/-0.402) for {'C': 53.45643593969718, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.735 (+/-0.402) for {'C': 53.45643593969718, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.710 (+/-0.363) for {'C': 53.45643593969718, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.710 (+/-0.363) for {'C': 53.45643593969718, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.710 (+/-0.363) for {'C': 53.45643593969718, 'kernel': 'rbf', 'gamma': 0.017701089583174213}
0.710 (+/-0.363) for {'C': 53.45643593969718, 'kernel': 'rbf', 'gamma': 0.018030177408595697}
0.714 (+/-0.359) for {'C': 53.45643593969718, 'kernel': 'rbf', 'gamma': 0.018365383433483477}
0.714 (+/-0.359) for {'C': 53.45643593969718, 'kernel': 'rbf', 'gamma': 0.018706821403658012}
0.714 (+/-0.359) for {'C': 53.45643593969718, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.727 (+/-0.397) for {'C': 54.450265284242114, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.735 (+/-0.402) for {'C': 54.450265284242114, 'kernel': 'rbf', 'gamma': 0.016143585568264868}
0.735 (+/-0.402) for {'C': 54.450265284242114, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.710 (+/-0.363) for {'C': 54.450265284242114, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.710 (+/-0.363) for {'C': 54.450265284242114, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.710 (+/-0.363) for {'C': 54.450265284242114, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.710 (+/-0.363) for {'C': 54.450265284242114, 'kernel': 'rbf', 'gamma': 0.017701089583174213}
0.714 (+/-0.359) for {'C': 54.450265284242114, 'kernel': 'rbf', 'gamma': 0.018030177408595697}
0.714 (+/-0.359) for {'C': 54.450265284242114, 'kernel': 'rbf', 'gamma': 0.018365383433483477}
0.714 (+/-0.359) for {'C': 54.450265284242114, 'kernel': 'rbf', 'gamma': 0.018706821403658012}
0.714 (+/-0.359) for {'C': 54.450265284242114, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.735 (+/-0.402) for {'C': 55.462571295791086, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.735 (+/-0.402) for {'C': 55.462571295791086, 'kernel': 'rbf', 'gamma': 0.016143585568264868}
0.710 (+/-0.363) for {'C': 55.462571295791086, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.710 (+/-0.363) for {'C': 55.462571295791086, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.710 (+/-0.363) for {'C': 55.462571295791086, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.710 (+/-0.363) for {'C': 55.462571295791086, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.714 (+/-0.359) for {'C': 55.462571295791086, 'kernel': 'rbf', 'gamma': 0.017701089583174213}
0.714 (+/-0.359) for {'C': 55.462571295791086, 'kernel': 'rbf', 'gamma': 0.018030177408595697}
0.714 (+/-0.359) for {'C': 55.462571295791086, 'kernel': 'rbf', 'gamma': 0.018365383433483477}
0.714 (+/-0.359) for {'C': 55.462571295791086, 'kernel': 'rbf', 'gamma': 0.018706821403658012}
0.664 (+/-0.326) for {'C': 55.462571295791086, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.735 (+/-0.402) for {'C': 56.49369748123024, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.710 (+/-0.363) for {'C': 56.49369748123024, 'kernel': 'rbf', 'gamma': 0.016143585568264868}
0.710 (+/-0.363) for {'C': 56.49369748123024, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.710 (+/-0.363) for {'C': 56.49369748123024, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.714 (+/-0.359) for {'C': 56.49369748123024, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.714 (+/-0.359) for {'C': 56.49369748123024, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.714 (+/-0.359) for {'C': 56.49369748123024, 'kernel': 'rbf', 'gamma': 0.017701089583174213}
0.714 (+/-0.359) for {'C': 56.49369748123024, 'kernel': 'rbf', 'gamma': 0.018030177408595697}
0.714 (+/-0.359) for {'C': 56.49369748123024, 'kernel': 'rbf', 'gamma': 0.018365383433483477}
0.664 (+/-0.326) for {'C': 56.49369748123024, 'kernel': 'rbf', 'gamma': 0.018706821403658012}
0.664 (+/-0.326) for {'C': 56.49369748123024, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.710 (+/-0.363) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.710 (+/-0.363) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.016143585568264868}
0.710 (+/-0.363) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.714 (+/-0.359) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.714 (+/-0.359) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.714 (+/-0.359) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.714 (+/-0.359) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.017701089583174213}
0.714 (+/-0.359) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.018030177408595697}
0.714 (+/-0.359) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.018365383433483477}
0.714 (+/-0.359) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.018706821403658012}
0.664 (+/-0.326) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.710 (+/-0.363) for {'C': 58.61381645140289, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.710 (+/-0.363) for {'C': 58.61381645140289, 'kernel': 'rbf', 'gamma': 0.016143585568264868}
0.714 (+/-0.359) for {'C': 58.61381645140289, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.714 (+/-0.359) for {'C': 58.61381645140289, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.714 (+/-0.359) for {'C': 58.61381645140289, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.714 (+/-0.359) for {'C': 58.61381645140289, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.714 (+/-0.359) for {'C': 58.61381645140289, 'kernel': 'rbf', 'gamma': 0.017701089583174213}
0.714 (+/-0.359) for {'C': 58.61381645140289, 'kernel': 'rbf', 'gamma': 0.018030177408595697}
0.714 (+/-0.359) for {'C': 58.61381645140289, 'kernel': 'rbf', 'gamma': 0.018365383433483477}
0.664 (+/-0.326) for {'C': 58.61381645140289, 'kernel': 'rbf', 'gamma': 0.018706821403658012}
0.664 (+/-0.326) for {'C': 58.61381645140289, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.710 (+/-0.363) for {'C': 59.70352865838368, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.714 (+/-0.359) for {'C': 59.70352865838368, 'kernel': 'rbf', 'gamma': 0.016143585568264868}
0.714 (+/-0.359) for {'C': 59.70352865838368, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.714 (+/-0.359) for {'C': 59.70352865838368, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.714 (+/-0.359) for {'C': 59.70352865838368, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.714 (+/-0.359) for {'C': 59.70352865838368, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.714 (+/-0.359) for {'C': 59.70352865838368, 'kernel': 'rbf', 'gamma': 0.017701089583174213}
0.714 (+/-0.359) for {'C': 59.70352865838368, 'kernel': 'rbf', 'gamma': 0.018030177408595697}
0.664 (+/-0.326) for {'C': 59.70352865838368, 'kernel': 'rbf', 'gamma': 0.018365383433483477}
0.664 (+/-0.326) for {'C': 59.70352865838368, 'kernel': 'rbf', 'gamma': 0.018706821403658012}
0.673 (+/-0.330) for {'C': 59.70352865838368, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.714 (+/-0.359) for {'C': 60.8135001278718, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.714 (+/-0.359) for {'C': 60.8135001278718, 'kernel': 'rbf', 'gamma': 0.016143585568264868}
0.714 (+/-0.359) for {'C': 60.8135001278718, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.714 (+/-0.359) for {'C': 60.8135001278718, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.714 (+/-0.359) for {'C': 60.8135001278718, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.714 (+/-0.359) for {'C': 60.8135001278718, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.714 (+/-0.359) for {'C': 60.8135001278718, 'kernel': 'rbf', 'gamma': 0.017701089583174213}
0.664 (+/-0.326) for {'C': 60.8135001278718, 'kernel': 'rbf', 'gamma': 0.018030177408595697}
0.664 (+/-0.326) for {'C': 60.8135001278718, 'kernel': 'rbf', 'gamma': 0.018365383433483477}
0.673 (+/-0.330) for {'C': 60.8135001278718, 'kernel': 'rbf', 'gamma': 0.018706821403658012}
0.673 (+/-0.330) for {'C': 60.8135001278718, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.714 (+/-0.359) for {'C': 61.944107507678126, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.714 (+/-0.359) for {'C': 61.944107507678126, 'kernel': 'rbf', 'gamma': 0.016143585568264868}
0.714 (+/-0.359) for {'C': 61.944107507678126, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.714 (+/-0.359) for {'C': 61.944107507678126, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.714 (+/-0.359) for {'C': 61.944107507678126, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.714 (+/-0.359) for {'C': 61.944107507678126, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.664 (+/-0.326) for {'C': 61.944107507678126, 'kernel': 'rbf', 'gamma': 0.017701089583174213}
0.664 (+/-0.326) for {'C': 61.944107507678126, 'kernel': 'rbf', 'gamma': 0.018030177408595697}
0.673 (+/-0.330) for {'C': 61.944107507678126, 'kernel': 'rbf', 'gamma': 0.018365383433483477}
0.673 (+/-0.330) for {'C': 61.944107507678126, 'kernel': 'rbf', 'gamma': 0.018706821403658012}
0.673 (+/-0.330) for {'C': 61.944107507678126, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.714 (+/-0.359) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.714 (+/-0.359) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.016143585568264868}
0.714 (+/-0.359) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.714 (+/-0.359) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.714 (+/-0.359) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.714 (+/-0.359) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.664 (+/-0.326) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.017701089583174213}
0.673 (+/-0.330) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.018030177408595697}
0.673 (+/-0.330) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.018365383433483477}
0.673 (+/-0.330) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.018706821403658012}
0.673 (+/-0.330) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.673 (+/-0.377) for {'C': 10.0, 'kernel': 'linear'}
0.631 (+/-0.312) for {'C': 100.0, 'kernel': 'linear'}
0.653 (+/-0.293) for {'C': 1000.0, 'kernel': 'linear'}
0.619 (+/-0.322) for {'C': 10000.0, 'kernel': 'linear'}
0.619 (+/-0.322) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.20      0.17      0.18         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.99      0.99       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.682 (+/-0.294) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.682 (+/-0.294) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.016143585568264868}
0.682 (+/-0.294) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.682 (+/-0.295) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.682 (+/-0.295) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.682 (+/-0.295) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.682 (+/-0.295) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.017701089583174213}
0.682 (+/-0.295) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.018030177408595697}
0.682 (+/-0.295) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.018365383433483477}
0.682 (+/-0.295) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.018706821403658012}
0.666 (+/-0.315) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.682 (+/-0.294) for {'C': 53.45643593969718, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.682 (+/-0.294) for {'C': 53.45643593969718, 'kernel': 'rbf', 'gamma': 0.016143585568264868}
0.682 (+/-0.295) for {'C': 53.45643593969718, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.682 (+/-0.295) for {'C': 53.45643593969718, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.682 (+/-0.295) for {'C': 53.45643593969718, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.682 (+/-0.295) for {'C': 53.45643593969718, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.682 (+/-0.295) for {'C': 53.45643593969718, 'kernel': 'rbf', 'gamma': 0.017701089583174213}
0.682 (+/-0.295) for {'C': 53.45643593969718, 'kernel': 'rbf', 'gamma': 0.018030177408595697}
0.682 (+/-0.295) for {'C': 53.45643593969718, 'kernel': 'rbf', 'gamma': 0.018365383433483477}
0.682 (+/-0.295) for {'C': 53.45643593969718, 'kernel': 'rbf', 'gamma': 0.018706821403658012}
0.682 (+/-0.295) for {'C': 53.45643593969718, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.682 (+/-0.294) for {'C': 54.450265284242114, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.682 (+/-0.295) for {'C': 54.450265284242114, 'kernel': 'rbf', 'gamma': 0.016143585568264868}
0.682 (+/-0.295) for {'C': 54.450265284242114, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.682 (+/-0.295) for {'C': 54.450265284242114, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.682 (+/-0.295) for {'C': 54.450265284242114, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.682 (+/-0.295) for {'C': 54.450265284242114, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.682 (+/-0.295) for {'C': 54.450265284242114, 'kernel': 'rbf', 'gamma': 0.017701089583174213}
0.682 (+/-0.295) for {'C': 54.450265284242114, 'kernel': 'rbf', 'gamma': 0.018030177408595697}
0.682 (+/-0.295) for {'C': 54.450265284242114, 'kernel': 'rbf', 'gamma': 0.018365383433483477}
0.682 (+/-0.295) for {'C': 54.450265284242114, 'kernel': 'rbf', 'gamma': 0.018706821403658012}
0.682 (+/-0.295) for {'C': 54.450265284242114, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.682 (+/-0.295) for {'C': 55.462571295791086, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.682 (+/-0.295) for {'C': 55.462571295791086, 'kernel': 'rbf', 'gamma': 0.016143585568264868}
0.682 (+/-0.295) for {'C': 55.462571295791086, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.682 (+/-0.295) for {'C': 55.462571295791086, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.682 (+/-0.295) for {'C': 55.462571295791086, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.682 (+/-0.295) for {'C': 55.462571295791086, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.682 (+/-0.295) for {'C': 55.462571295791086, 'kernel': 'rbf', 'gamma': 0.017701089583174213}
0.682 (+/-0.295) for {'C': 55.462571295791086, 'kernel': 'rbf', 'gamma': 0.018030177408595697}
0.682 (+/-0.295) for {'C': 55.462571295791086, 'kernel': 'rbf', 'gamma': 0.018365383433483477}
0.682 (+/-0.295) for {'C': 55.462571295791086, 'kernel': 'rbf', 'gamma': 0.018706821403658012}
0.666 (+/-0.315) for {'C': 55.462571295791086, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.682 (+/-0.295) for {'C': 56.49369748123024, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.682 (+/-0.295) for {'C': 56.49369748123024, 'kernel': 'rbf', 'gamma': 0.016143585568264868}
0.682 (+/-0.295) for {'C': 56.49369748123024, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.682 (+/-0.295) for {'C': 56.49369748123024, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.682 (+/-0.295) for {'C': 56.49369748123024, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.682 (+/-0.295) for {'C': 56.49369748123024, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.682 (+/-0.295) for {'C': 56.49369748123024, 'kernel': 'rbf', 'gamma': 0.017701089583174213}
0.682 (+/-0.295) for {'C': 56.49369748123024, 'kernel': 'rbf', 'gamma': 0.018030177408595697}
0.682 (+/-0.295) for {'C': 56.49369748123024, 'kernel': 'rbf', 'gamma': 0.018365383433483477}
0.666 (+/-0.315) for {'C': 56.49369748123024, 'kernel': 'rbf', 'gamma': 0.018706821403658012}
0.666 (+/-0.315) for {'C': 56.49369748123024, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.682 (+/-0.295) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.682 (+/-0.295) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.016143585568264868}
0.682 (+/-0.295) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.682 (+/-0.295) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.682 (+/-0.295) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.682 (+/-0.295) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.682 (+/-0.295) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.017701089583174213}
0.682 (+/-0.295) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.018030177408595697}
0.682 (+/-0.295) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.018365383433483477}
0.682 (+/-0.295) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.018706821403658012}
0.666 (+/-0.315) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.682 (+/-0.295) for {'C': 58.61381645140289, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.682 (+/-0.295) for {'C': 58.61381645140289, 'kernel': 'rbf', 'gamma': 0.016143585568264868}
0.682 (+/-0.295) for {'C': 58.61381645140289, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.682 (+/-0.295) for {'C': 58.61381645140289, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.682 (+/-0.295) for {'C': 58.61381645140289, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.682 (+/-0.295) for {'C': 58.61381645140289, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.682 (+/-0.295) for {'C': 58.61381645140289, 'kernel': 'rbf', 'gamma': 0.017701089583174213}
0.682 (+/-0.295) for {'C': 58.61381645140289, 'kernel': 'rbf', 'gamma': 0.018030177408595697}
0.682 (+/-0.295) for {'C': 58.61381645140289, 'kernel': 'rbf', 'gamma': 0.018365383433483477}
0.666 (+/-0.315) for {'C': 58.61381645140289, 'kernel': 'rbf', 'gamma': 0.018706821403658012}
0.666 (+/-0.315) for {'C': 58.61381645140289, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.682 (+/-0.295) for {'C': 59.70352865838368, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.682 (+/-0.295) for {'C': 59.70352865838368, 'kernel': 'rbf', 'gamma': 0.016143585568264868}
0.682 (+/-0.295) for {'C': 59.70352865838368, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.682 (+/-0.295) for {'C': 59.70352865838368, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.682 (+/-0.295) for {'C': 59.70352865838368, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.682 (+/-0.295) for {'C': 59.70352865838368, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.682 (+/-0.295) for {'C': 59.70352865838368, 'kernel': 'rbf', 'gamma': 0.017701089583174213}
0.682 (+/-0.295) for {'C': 59.70352865838368, 'kernel': 'rbf', 'gamma': 0.018030177408595697}
0.666 (+/-0.315) for {'C': 59.70352865838368, 'kernel': 'rbf', 'gamma': 0.018365383433483477}
0.666 (+/-0.315) for {'C': 59.70352865838368, 'kernel': 'rbf', 'gamma': 0.018706821403658012}
0.666 (+/-0.315) for {'C': 59.70352865838368, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.682 (+/-0.295) for {'C': 60.8135001278718, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.682 (+/-0.295) for {'C': 60.8135001278718, 'kernel': 'rbf', 'gamma': 0.016143585568264868}
0.682 (+/-0.295) for {'C': 60.8135001278718, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.682 (+/-0.295) for {'C': 60.8135001278718, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.682 (+/-0.295) for {'C': 60.8135001278718, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.682 (+/-0.295) for {'C': 60.8135001278718, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.682 (+/-0.295) for {'C': 60.8135001278718, 'kernel': 'rbf', 'gamma': 0.017701089583174213}
0.666 (+/-0.315) for {'C': 60.8135001278718, 'kernel': 'rbf', 'gamma': 0.018030177408595697}
0.666 (+/-0.315) for {'C': 60.8135001278718, 'kernel': 'rbf', 'gamma': 0.018365383433483477}
0.666 (+/-0.315) for {'C': 60.8135001278718, 'kernel': 'rbf', 'gamma': 0.018706821403658012}
0.666 (+/-0.315) for {'C': 60.8135001278718, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.682 (+/-0.295) for {'C': 61.944107507678126, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.682 (+/-0.295) for {'C': 61.944107507678126, 'kernel': 'rbf', 'gamma': 0.016143585568264868}
0.682 (+/-0.295) for {'C': 61.944107507678126, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.682 (+/-0.295) for {'C': 61.944107507678126, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.682 (+/-0.295) for {'C': 61.944107507678126, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.682 (+/-0.295) for {'C': 61.944107507678126, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.666 (+/-0.315) for {'C': 61.944107507678126, 'kernel': 'rbf', 'gamma': 0.017701089583174213}
0.666 (+/-0.315) for {'C': 61.944107507678126, 'kernel': 'rbf', 'gamma': 0.018030177408595697}
0.666 (+/-0.315) for {'C': 61.944107507678126, 'kernel': 'rbf', 'gamma': 0.018365383433483477}
0.666 (+/-0.315) for {'C': 61.944107507678126, 'kernel': 'rbf', 'gamma': 0.018706821403658012}
0.666 (+/-0.315) for {'C': 61.944107507678126, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.682 (+/-0.295) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.682 (+/-0.295) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.016143585568264868}
0.682 (+/-0.295) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.682 (+/-0.295) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.682 (+/-0.295) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.682 (+/-0.295) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.666 (+/-0.315) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.017701089583174213}
0.666 (+/-0.315) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.018030177408595697}
0.666 (+/-0.315) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.018365383433483477}
0.666 (+/-0.315) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.018706821403658012}
0.666 (+/-0.315) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.663 (+/-0.315) for {'C': 10.0, 'kernel': 'linear'}
0.661 (+/-0.315) for {'C': 100.0, 'kernel': 'linear'}
0.662 (+/-0.227) for {'C': 1000.0, 'kernel': 'linear'}
0.611 (+/-0.241) for {'C': 10000.0, 'kernel': 'linear'}
0.611 (+/-0.240) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.17      0.17      0.17         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6822115384615385
发现最优参数C为原先的最大/最小值，直接重新设置超参。
第1轮loop中，tunemodel函数得到的最优参数是： ([{'C': array([5.2480746e-04, 5.2480746e-03, 5.2480746e-02, 5.2480746e-01,
       5.2480746e+00, 5.2480746e+01, 5.2480746e+02, 5.2480746e+03,
       5.2480746e+04, 5.2480746e+05, 5.2480746e+06]), 'kernel': ['rbf'], 'gamma': array([0.01644372, 0.01650441, 0.01656533, 0.01662647, 0.01668784,
       0.01674943, 0.01681125, 0.0168733 , 0.01693558, 0.01699809,
       0.01706082])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}], {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.016749428760264376}, 0.6822115384615385)
这是第1次迭代微调C和gamma。
第1次迭代，得到delta： [5.06324771e+00 6.28579527e-04]
执行tunemodel()函数前，使用的grid search parameter是：
 [{'C': array([5.2480746e-04, 5.2480746e-03, 5.2480746e-02, 5.2480746e-01,
       5.2480746e+00, 5.2480746e+01, 5.2480746e+02, 5.2480746e+03,
       5.2480746e+04, 5.2480746e+05, 5.2480746e+06]), 'kernel': ['rbf'], 'gamma': array([0.01644372, 0.01650441, 0.01656533, 0.01662647, 0.01668784,
       0.01674943, 0.01681125, 0.0168733 , 0.01693558, 0.01699809,
       0.01706082])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 0.0005248074602497723, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.496 (+/-0.001) for {'C': 0.0005248074602497723, 'kernel': 'rbf', 'gamma': 0.01650440985652279}
0.496 (+/-0.001) for {'C': 0.0005248074602497723, 'kernel': 'rbf', 'gamma': 0.01656532649318018}
0.496 (+/-0.001) for {'C': 0.0005248074602497723, 'kernel': 'rbf', 'gamma': 0.016626467968935365}
0.496 (+/-0.001) for {'C': 0.0005248074602497723, 'kernel': 'rbf', 'gamma': 0.016687835113653904}
0.496 (+/-0.001) for {'C': 0.0005248074602497723, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.496 (+/-0.001) for {'C': 0.0005248074602497723, 'kernel': 'rbf', 'gamma': 0.016811249744769604}
0.496 (+/-0.001) for {'C': 0.0005248074602497723, 'kernel': 'rbf', 'gamma': 0.01687329890625804}
0.496 (+/-0.001) for {'C': 0.0005248074602497723, 'kernel': 'rbf', 'gamma': 0.01693557708691519}
0.496 (+/-0.001) for {'C': 0.0005248074602497723, 'kernel': 'rbf', 'gamma': 0.016998085132034966}
0.496 (+/-0.001) for {'C': 0.0005248074602497723, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.496 (+/-0.001) for {'C': 0.005248074602497723, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.496 (+/-0.001) for {'C': 0.005248074602497723, 'kernel': 'rbf', 'gamma': 0.01650440985652279}
0.496 (+/-0.001) for {'C': 0.005248074602497723, 'kernel': 'rbf', 'gamma': 0.01656532649318018}
0.496 (+/-0.001) for {'C': 0.005248074602497723, 'kernel': 'rbf', 'gamma': 0.016626467968935365}
0.496 (+/-0.001) for {'C': 0.005248074602497723, 'kernel': 'rbf', 'gamma': 0.016687835113653904}
0.496 (+/-0.001) for {'C': 0.005248074602497723, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.496 (+/-0.001) for {'C': 0.005248074602497723, 'kernel': 'rbf', 'gamma': 0.016811249744769604}
0.496 (+/-0.001) for {'C': 0.005248074602497723, 'kernel': 'rbf', 'gamma': 0.01687329890625804}
0.496 (+/-0.001) for {'C': 0.005248074602497723, 'kernel': 'rbf', 'gamma': 0.01693557708691519}
0.496 (+/-0.001) for {'C': 0.005248074602497723, 'kernel': 'rbf', 'gamma': 0.016998085132034966}
0.496 (+/-0.001) for {'C': 0.005248074602497723, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.496 (+/-0.001) for {'C': 0.05248074602497723, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.496 (+/-0.001) for {'C': 0.05248074602497723, 'kernel': 'rbf', 'gamma': 0.01650440985652279}
0.496 (+/-0.001) for {'C': 0.05248074602497723, 'kernel': 'rbf', 'gamma': 0.01656532649318018}
0.496 (+/-0.001) for {'C': 0.05248074602497723, 'kernel': 'rbf', 'gamma': 0.016626467968935365}
0.496 (+/-0.001) for {'C': 0.05248074602497723, 'kernel': 'rbf', 'gamma': 0.016687835113653904}
0.496 (+/-0.001) for {'C': 0.05248074602497723, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.496 (+/-0.001) for {'C': 0.05248074602497723, 'kernel': 'rbf', 'gamma': 0.016811249744769604}
0.496 (+/-0.001) for {'C': 0.05248074602497723, 'kernel': 'rbf', 'gamma': 0.01687329890625804}
0.496 (+/-0.001) for {'C': 0.05248074602497723, 'kernel': 'rbf', 'gamma': 0.01693557708691519}
0.496 (+/-0.001) for {'C': 0.05248074602497723, 'kernel': 'rbf', 'gamma': 0.016998085132034966}
0.496 (+/-0.001) for {'C': 0.05248074602497723, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.496 (+/-0.001) for {'C': 0.5248074602497723, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.496 (+/-0.001) for {'C': 0.5248074602497723, 'kernel': 'rbf', 'gamma': 0.01650440985652279}
0.496 (+/-0.001) for {'C': 0.5248074602497723, 'kernel': 'rbf', 'gamma': 0.01656532649318018}
0.496 (+/-0.001) for {'C': 0.5248074602497723, 'kernel': 'rbf', 'gamma': 0.016626467968935365}
0.496 (+/-0.001) for {'C': 0.5248074602497723, 'kernel': 'rbf', 'gamma': 0.016687835113653904}
0.496 (+/-0.001) for {'C': 0.5248074602497723, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.496 (+/-0.001) for {'C': 0.5248074602497723, 'kernel': 'rbf', 'gamma': 0.016811249744769604}
0.496 (+/-0.001) for {'C': 0.5248074602497723, 'kernel': 'rbf', 'gamma': 0.01687329890625804}
0.496 (+/-0.001) for {'C': 0.5248074602497723, 'kernel': 'rbf', 'gamma': 0.01693557708691519}
0.496 (+/-0.001) for {'C': 0.5248074602497723, 'kernel': 'rbf', 'gamma': 0.016998085132034966}
0.496 (+/-0.001) for {'C': 0.5248074602497723, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.747 (+/-0.502) for {'C': 5.248074602497723, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.747 (+/-0.502) for {'C': 5.248074602497723, 'kernel': 'rbf', 'gamma': 0.01650440985652279}
0.747 (+/-0.502) for {'C': 5.248074602497723, 'kernel': 'rbf', 'gamma': 0.01656532649318018}
0.747 (+/-0.502) for {'C': 5.248074602497723, 'kernel': 'rbf', 'gamma': 0.016626467968935365}
0.747 (+/-0.502) for {'C': 5.248074602497723, 'kernel': 'rbf', 'gamma': 0.016687835113653904}
0.747 (+/-0.502) for {'C': 5.248074602497723, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.747 (+/-0.502) for {'C': 5.248074602497723, 'kernel': 'rbf', 'gamma': 0.016811249744769604}
0.747 (+/-0.502) for {'C': 5.248074602497723, 'kernel': 'rbf', 'gamma': 0.01687329890625804}
0.747 (+/-0.502) for {'C': 5.248074602497723, 'kernel': 'rbf', 'gamma': 0.01693557708691519}
0.747 (+/-0.502) for {'C': 5.248074602497723, 'kernel': 'rbf', 'gamma': 0.016998085132034966}
0.747 (+/-0.502) for {'C': 5.248074602497723, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.747 (+/-0.449) for {'C': 52.48074602497723, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.747 (+/-0.449) for {'C': 52.48074602497723, 'kernel': 'rbf', 'gamma': 0.01650440985652279}
0.747 (+/-0.449) for {'C': 52.48074602497723, 'kernel': 'rbf', 'gamma': 0.01656532649318018}
0.747 (+/-0.449) for {'C': 52.48074602497723, 'kernel': 'rbf', 'gamma': 0.016626467968935365}
0.747 (+/-0.449) for {'C': 52.48074602497723, 'kernel': 'rbf', 'gamma': 0.016687835113653904}
0.747 (+/-0.449) for {'C': 52.48074602497723, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.747 (+/-0.449) for {'C': 52.48074602497723, 'kernel': 'rbf', 'gamma': 0.016811249744769604}
0.747 (+/-0.449) for {'C': 52.48074602497723, 'kernel': 'rbf', 'gamma': 0.01687329890625804}
0.747 (+/-0.449) for {'C': 52.48074602497723, 'kernel': 'rbf', 'gamma': 0.01693557708691519}
0.747 (+/-0.449) for {'C': 52.48074602497723, 'kernel': 'rbf', 'gamma': 0.016998085132034966}
0.747 (+/-0.449) for {'C': 52.48074602497723, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.697 (+/-0.375) for {'C': 524.8074602497722, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.697 (+/-0.375) for {'C': 524.8074602497722, 'kernel': 'rbf', 'gamma': 0.01650440985652279}
0.697 (+/-0.375) for {'C': 524.8074602497722, 'kernel': 'rbf', 'gamma': 0.01656532649318018}
0.697 (+/-0.375) for {'C': 524.8074602497722, 'kernel': 'rbf', 'gamma': 0.016626467968935365}
0.697 (+/-0.375) for {'C': 524.8074602497722, 'kernel': 'rbf', 'gamma': 0.016687835113653904}
0.697 (+/-0.375) for {'C': 524.8074602497722, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.697 (+/-0.375) for {'C': 524.8074602497722, 'kernel': 'rbf', 'gamma': 0.016811249744769604}
0.697 (+/-0.375) for {'C': 524.8074602497722, 'kernel': 'rbf', 'gamma': 0.01687329890625804}
0.697 (+/-0.375) for {'C': 524.8074602497722, 'kernel': 'rbf', 'gamma': 0.01693557708691519}
0.697 (+/-0.375) for {'C': 524.8074602497722, 'kernel': 'rbf', 'gamma': 0.016998085132034966}
0.697 (+/-0.375) for {'C': 524.8074602497722, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.632 (+/-0.311) for {'C': 5248.074602497723, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.629 (+/-0.312) for {'C': 5248.074602497723, 'kernel': 'rbf', 'gamma': 0.01650440985652279}
0.629 (+/-0.312) for {'C': 5248.074602497723, 'kernel': 'rbf', 'gamma': 0.01656532649318018}
0.629 (+/-0.312) for {'C': 5248.074602497723, 'kernel': 'rbf', 'gamma': 0.016626467968935365}
0.629 (+/-0.312) for {'C': 5248.074602497723, 'kernel': 'rbf', 'gamma': 0.016687835113653904}
0.629 (+/-0.312) for {'C': 5248.074602497723, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.629 (+/-0.312) for {'C': 5248.074602497723, 'kernel': 'rbf', 'gamma': 0.016811249744769604}
0.629 (+/-0.312) for {'C': 5248.074602497723, 'kernel': 'rbf', 'gamma': 0.01687329890625804}
0.629 (+/-0.312) for {'C': 5248.074602497723, 'kernel': 'rbf', 'gamma': 0.01693557708691519}
0.629 (+/-0.312) for {'C': 5248.074602497723, 'kernel': 'rbf', 'gamma': 0.016998085132034966}
0.629 (+/-0.312) for {'C': 5248.074602497723, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.614 (+/-0.327) for {'C': 52480.74602497723, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.614 (+/-0.327) for {'C': 52480.74602497723, 'kernel': 'rbf', 'gamma': 0.01650440985652279}
0.614 (+/-0.327) for {'C': 52480.74602497723, 'kernel': 'rbf', 'gamma': 0.01656532649318018}
0.614 (+/-0.327) for {'C': 52480.74602497723, 'kernel': 'rbf', 'gamma': 0.016626467968935365}
0.614 (+/-0.327) for {'C': 52480.74602497723, 'kernel': 'rbf', 'gamma': 0.016687835113653904}
0.614 (+/-0.327) for {'C': 52480.74602497723, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.614 (+/-0.327) for {'C': 52480.74602497723, 'kernel': 'rbf', 'gamma': 0.016811249744769604}
0.614 (+/-0.327) for {'C': 52480.74602497723, 'kernel': 'rbf', 'gamma': 0.01687329890625804}
0.614 (+/-0.327) for {'C': 52480.74602497723, 'kernel': 'rbf', 'gamma': 0.01693557708691519}
0.614 (+/-0.327) for {'C': 52480.74602497723, 'kernel': 'rbf', 'gamma': 0.016998085132034966}
0.614 (+/-0.327) for {'C': 52480.74602497723, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.614 (+/-0.327) for {'C': 524807.4602497723, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.614 (+/-0.327) for {'C': 524807.4602497723, 'kernel': 'rbf', 'gamma': 0.01650440985652279}
0.614 (+/-0.327) for {'C': 524807.4602497723, 'kernel': 'rbf', 'gamma': 0.01656532649318018}
0.614 (+/-0.327) for {'C': 524807.4602497723, 'kernel': 'rbf', 'gamma': 0.016626467968935365}
0.614 (+/-0.327) for {'C': 524807.4602497723, 'kernel': 'rbf', 'gamma': 0.016687835113653904}
0.614 (+/-0.327) for {'C': 524807.4602497723, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.614 (+/-0.327) for {'C': 524807.4602497723, 'kernel': 'rbf', 'gamma': 0.016811249744769604}
0.614 (+/-0.327) for {'C': 524807.4602497723, 'kernel': 'rbf', 'gamma': 0.01687329890625804}
0.614 (+/-0.327) for {'C': 524807.4602497723, 'kernel': 'rbf', 'gamma': 0.01693557708691519}
0.614 (+/-0.327) for {'C': 524807.4602497723, 'kernel': 'rbf', 'gamma': 0.016998085132034966}
0.614 (+/-0.327) for {'C': 524807.4602497723, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.614 (+/-0.327) for {'C': 5248074.602497723, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.614 (+/-0.327) for {'C': 5248074.602497723, 'kernel': 'rbf', 'gamma': 0.01650440985652279}
0.614 (+/-0.327) for {'C': 5248074.602497723, 'kernel': 'rbf', 'gamma': 0.01656532649318018}
0.614 (+/-0.327) for {'C': 5248074.602497723, 'kernel': 'rbf', 'gamma': 0.016626467968935365}
0.614 (+/-0.327) for {'C': 5248074.602497723, 'kernel': 'rbf', 'gamma': 0.016687835113653904}
0.614 (+/-0.327) for {'C': 5248074.602497723, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.614 (+/-0.327) for {'C': 5248074.602497723, 'kernel': 'rbf', 'gamma': 0.016811249744769604}
0.614 (+/-0.327) for {'C': 5248074.602497723, 'kernel': 'rbf', 'gamma': 0.01687329890625804}
0.614 (+/-0.327) for {'C': 5248074.602497723, 'kernel': 'rbf', 'gamma': 0.01693557708691519}
0.614 (+/-0.327) for {'C': 5248074.602497723, 'kernel': 'rbf', 'gamma': 0.016998085132034966}
0.614 (+/-0.327) for {'C': 5248074.602497723, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.706 (+/-0.383) for {'C': 10.0, 'kernel': 'linear'}
0.643 (+/-0.320) for {'C': 100.0, 'kernel': 'linear'}
0.653 (+/-0.293) for {'C': 1000.0, 'kernel': 'linear'}
0.619 (+/-0.322) for {'C': 10000.0, 'kernel': 'linear'}
0.619 (+/-0.322) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.25      0.17      0.20         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.99       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 0.0005248074602497723, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.500 (+/-0.000) for {'C': 0.0005248074602497723, 'kernel': 'rbf', 'gamma': 0.01650440985652279}
0.500 (+/-0.000) for {'C': 0.0005248074602497723, 'kernel': 'rbf', 'gamma': 0.01656532649318018}
0.500 (+/-0.000) for {'C': 0.0005248074602497723, 'kernel': 'rbf', 'gamma': 0.016626467968935365}
0.500 (+/-0.000) for {'C': 0.0005248074602497723, 'kernel': 'rbf', 'gamma': 0.016687835113653904}
0.500 (+/-0.000) for {'C': 0.0005248074602497723, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.500 (+/-0.000) for {'C': 0.0005248074602497723, 'kernel': 'rbf', 'gamma': 0.016811249744769604}
0.500 (+/-0.000) for {'C': 0.0005248074602497723, 'kernel': 'rbf', 'gamma': 0.01687329890625804}
0.500 (+/-0.000) for {'C': 0.0005248074602497723, 'kernel': 'rbf', 'gamma': 0.01693557708691519}
0.500 (+/-0.000) for {'C': 0.0005248074602497723, 'kernel': 'rbf', 'gamma': 0.016998085132034966}
0.500 (+/-0.000) for {'C': 0.0005248074602497723, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.500 (+/-0.000) for {'C': 0.005248074602497723, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.500 (+/-0.000) for {'C': 0.005248074602497723, 'kernel': 'rbf', 'gamma': 0.01650440985652279}
0.500 (+/-0.000) for {'C': 0.005248074602497723, 'kernel': 'rbf', 'gamma': 0.01656532649318018}
0.500 (+/-0.000) for {'C': 0.005248074602497723, 'kernel': 'rbf', 'gamma': 0.016626467968935365}
0.500 (+/-0.000) for {'C': 0.005248074602497723, 'kernel': 'rbf', 'gamma': 0.016687835113653904}
0.500 (+/-0.000) for {'C': 0.005248074602497723, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.500 (+/-0.000) for {'C': 0.005248074602497723, 'kernel': 'rbf', 'gamma': 0.016811249744769604}
0.500 (+/-0.000) for {'C': 0.005248074602497723, 'kernel': 'rbf', 'gamma': 0.01687329890625804}
0.500 (+/-0.000) for {'C': 0.005248074602497723, 'kernel': 'rbf', 'gamma': 0.01693557708691519}
0.500 (+/-0.000) for {'C': 0.005248074602497723, 'kernel': 'rbf', 'gamma': 0.016998085132034966}
0.500 (+/-0.000) for {'C': 0.005248074602497723, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.500 (+/-0.000) for {'C': 0.05248074602497723, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.500 (+/-0.000) for {'C': 0.05248074602497723, 'kernel': 'rbf', 'gamma': 0.01650440985652279}
0.500 (+/-0.000) for {'C': 0.05248074602497723, 'kernel': 'rbf', 'gamma': 0.01656532649318018}
0.500 (+/-0.000) for {'C': 0.05248074602497723, 'kernel': 'rbf', 'gamma': 0.016626467968935365}
0.500 (+/-0.000) for {'C': 0.05248074602497723, 'kernel': 'rbf', 'gamma': 0.016687835113653904}
0.500 (+/-0.000) for {'C': 0.05248074602497723, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.500 (+/-0.000) for {'C': 0.05248074602497723, 'kernel': 'rbf', 'gamma': 0.016811249744769604}
0.500 (+/-0.000) for {'C': 0.05248074602497723, 'kernel': 'rbf', 'gamma': 0.01687329890625804}
0.500 (+/-0.000) for {'C': 0.05248074602497723, 'kernel': 'rbf', 'gamma': 0.01693557708691519}
0.500 (+/-0.000) for {'C': 0.05248074602497723, 'kernel': 'rbf', 'gamma': 0.016998085132034966}
0.500 (+/-0.000) for {'C': 0.05248074602497723, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.500 (+/-0.000) for {'C': 0.5248074602497723, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.500 (+/-0.000) for {'C': 0.5248074602497723, 'kernel': 'rbf', 'gamma': 0.01650440985652279}
0.500 (+/-0.000) for {'C': 0.5248074602497723, 'kernel': 'rbf', 'gamma': 0.01656532649318018}
0.500 (+/-0.000) for {'C': 0.5248074602497723, 'kernel': 'rbf', 'gamma': 0.016626467968935365}
0.500 (+/-0.000) for {'C': 0.5248074602497723, 'kernel': 'rbf', 'gamma': 0.016687835113653904}
0.500 (+/-0.000) for {'C': 0.5248074602497723, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.500 (+/-0.000) for {'C': 0.5248074602497723, 'kernel': 'rbf', 'gamma': 0.016811249744769604}
0.500 (+/-0.000) for {'C': 0.5248074602497723, 'kernel': 'rbf', 'gamma': 0.01687329890625804}
0.500 (+/-0.000) for {'C': 0.5248074602497723, 'kernel': 'rbf', 'gamma': 0.01693557708691519}
0.500 (+/-0.000) for {'C': 0.5248074602497723, 'kernel': 'rbf', 'gamma': 0.016998085132034966}
0.500 (+/-0.000) for {'C': 0.5248074602497723, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.617 (+/-0.238) for {'C': 5.248074602497723, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.617 (+/-0.238) for {'C': 5.248074602497723, 'kernel': 'rbf', 'gamma': 0.01650440985652279}
0.617 (+/-0.238) for {'C': 5.248074602497723, 'kernel': 'rbf', 'gamma': 0.01656532649318018}
0.617 (+/-0.238) for {'C': 5.248074602497723, 'kernel': 'rbf', 'gamma': 0.016626467968935365}
0.617 (+/-0.238) for {'C': 5.248074602497723, 'kernel': 'rbf', 'gamma': 0.016687835113653904}
0.617 (+/-0.238) for {'C': 5.248074602497723, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.617 (+/-0.238) for {'C': 5.248074602497723, 'kernel': 'rbf', 'gamma': 0.016811249744769604}
0.617 (+/-0.238) for {'C': 5.248074602497723, 'kernel': 'rbf', 'gamma': 0.01687329890625804}
0.617 (+/-0.238) for {'C': 5.248074602497723, 'kernel': 'rbf', 'gamma': 0.01693557708691519}
0.617 (+/-0.238) for {'C': 5.248074602497723, 'kernel': 'rbf', 'gamma': 0.016998085132034966}
0.617 (+/-0.238) for {'C': 5.248074602497723, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.641 (+/-0.236) for {'C': 52.48074602497723, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.641 (+/-0.236) for {'C': 52.48074602497723, 'kernel': 'rbf', 'gamma': 0.01650440985652279}
0.641 (+/-0.236) for {'C': 52.48074602497723, 'kernel': 'rbf', 'gamma': 0.01656532649318018}
0.641 (+/-0.236) for {'C': 52.48074602497723, 'kernel': 'rbf', 'gamma': 0.016626467968935365}
0.641 (+/-0.236) for {'C': 52.48074602497723, 'kernel': 'rbf', 'gamma': 0.016687835113653904}
0.641 (+/-0.236) for {'C': 52.48074602497723, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.641 (+/-0.236) for {'C': 52.48074602497723, 'kernel': 'rbf', 'gamma': 0.016811249744769604}
0.641 (+/-0.236) for {'C': 52.48074602497723, 'kernel': 'rbf', 'gamma': 0.01687329890625804}
0.641 (+/-0.236) for {'C': 52.48074602497723, 'kernel': 'rbf', 'gamma': 0.01693557708691519}
0.641 (+/-0.236) for {'C': 52.48074602497723, 'kernel': 'rbf', 'gamma': 0.016998085132034966}
0.641 (+/-0.236) for {'C': 52.48074602497723, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.641 (+/-0.236) for {'C': 524.8074602497722, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.641 (+/-0.236) for {'C': 524.8074602497722, 'kernel': 'rbf', 'gamma': 0.01650440985652279}
0.641 (+/-0.236) for {'C': 524.8074602497722, 'kernel': 'rbf', 'gamma': 0.01656532649318018}
0.641 (+/-0.236) for {'C': 524.8074602497722, 'kernel': 'rbf', 'gamma': 0.016626467968935365}
0.641 (+/-0.236) for {'C': 524.8074602497722, 'kernel': 'rbf', 'gamma': 0.016687835113653904}
0.641 (+/-0.236) for {'C': 524.8074602497722, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.641 (+/-0.236) for {'C': 524.8074602497722, 'kernel': 'rbf', 'gamma': 0.016811249744769604}
0.641 (+/-0.236) for {'C': 524.8074602497722, 'kernel': 'rbf', 'gamma': 0.01687329890625804}
0.641 (+/-0.236) for {'C': 524.8074602497722, 'kernel': 'rbf', 'gamma': 0.01693557708691519}
0.641 (+/-0.236) for {'C': 524.8074602497722, 'kernel': 'rbf', 'gamma': 0.016998085132034966}
0.641 (+/-0.236) for {'C': 524.8074602497722, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.637 (+/-0.237) for {'C': 5248.074602497723, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.637 (+/-0.237) for {'C': 5248.074602497723, 'kernel': 'rbf', 'gamma': 0.01650440985652279}
0.637 (+/-0.237) for {'C': 5248.074602497723, 'kernel': 'rbf', 'gamma': 0.01656532649318018}
0.637 (+/-0.237) for {'C': 5248.074602497723, 'kernel': 'rbf', 'gamma': 0.016626467968935365}
0.637 (+/-0.237) for {'C': 5248.074602497723, 'kernel': 'rbf', 'gamma': 0.016687835113653904}
0.637 (+/-0.237) for {'C': 5248.074602497723, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.637 (+/-0.237) for {'C': 5248.074602497723, 'kernel': 'rbf', 'gamma': 0.016811249744769604}
0.637 (+/-0.237) for {'C': 5248.074602497723, 'kernel': 'rbf', 'gamma': 0.01687329890625804}
0.637 (+/-0.237) for {'C': 5248.074602497723, 'kernel': 'rbf', 'gamma': 0.01693557708691519}
0.637 (+/-0.237) for {'C': 5248.074602497723, 'kernel': 'rbf', 'gamma': 0.016998085132034966}
0.637 (+/-0.237) for {'C': 5248.074602497723, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.588 (+/-0.232) for {'C': 52480.74602497723, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.588 (+/-0.232) for {'C': 52480.74602497723, 'kernel': 'rbf', 'gamma': 0.01650440985652279}
0.588 (+/-0.232) for {'C': 52480.74602497723, 'kernel': 'rbf', 'gamma': 0.01656532649318018}
0.588 (+/-0.232) for {'C': 52480.74602497723, 'kernel': 'rbf', 'gamma': 0.016626467968935365}
0.588 (+/-0.232) for {'C': 52480.74602497723, 'kernel': 'rbf', 'gamma': 0.016687835113653904}
0.588 (+/-0.232) for {'C': 52480.74602497723, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.588 (+/-0.232) for {'C': 52480.74602497723, 'kernel': 'rbf', 'gamma': 0.016811249744769604}
0.588 (+/-0.232) for {'C': 52480.74602497723, 'kernel': 'rbf', 'gamma': 0.01687329890625804}
0.588 (+/-0.232) for {'C': 52480.74602497723, 'kernel': 'rbf', 'gamma': 0.01693557708691519}
0.588 (+/-0.232) for {'C': 52480.74602497723, 'kernel': 'rbf', 'gamma': 0.016998085132034966}
0.588 (+/-0.232) for {'C': 52480.74602497723, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.587 (+/-0.234) for {'C': 524807.4602497723, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.587 (+/-0.234) for {'C': 524807.4602497723, 'kernel': 'rbf', 'gamma': 0.01650440985652279}
0.587 (+/-0.234) for {'C': 524807.4602497723, 'kernel': 'rbf', 'gamma': 0.01656532649318018}
0.587 (+/-0.234) for {'C': 524807.4602497723, 'kernel': 'rbf', 'gamma': 0.016626467968935365}
0.587 (+/-0.234) for {'C': 524807.4602497723, 'kernel': 'rbf', 'gamma': 0.016687835113653904}
0.587 (+/-0.234) for {'C': 524807.4602497723, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.587 (+/-0.234) for {'C': 524807.4602497723, 'kernel': 'rbf', 'gamma': 0.016811249744769604}
0.587 (+/-0.234) for {'C': 524807.4602497723, 'kernel': 'rbf', 'gamma': 0.01687329890625804}
0.587 (+/-0.234) for {'C': 524807.4602497723, 'kernel': 'rbf', 'gamma': 0.01693557708691519}
0.587 (+/-0.234) for {'C': 524807.4602497723, 'kernel': 'rbf', 'gamma': 0.016998085132034966}
0.587 (+/-0.234) for {'C': 524807.4602497723, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.587 (+/-0.234) for {'C': 5248074.602497723, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.587 (+/-0.234) for {'C': 5248074.602497723, 'kernel': 'rbf', 'gamma': 0.01650440985652279}
0.587 (+/-0.234) for {'C': 5248074.602497723, 'kernel': 'rbf', 'gamma': 0.01656532649318018}
0.587 (+/-0.234) for {'C': 5248074.602497723, 'kernel': 'rbf', 'gamma': 0.016626467968935365}
0.587 (+/-0.234) for {'C': 5248074.602497723, 'kernel': 'rbf', 'gamma': 0.016687835113653904}
0.587 (+/-0.234) for {'C': 5248074.602497723, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.587 (+/-0.234) for {'C': 5248074.602497723, 'kernel': 'rbf', 'gamma': 0.016811249744769604}
0.587 (+/-0.234) for {'C': 5248074.602497723, 'kernel': 'rbf', 'gamma': 0.01687329890625804}
0.587 (+/-0.234) for {'C': 5248074.602497723, 'kernel': 'rbf', 'gamma': 0.01693557708691519}
0.587 (+/-0.234) for {'C': 5248074.602497723, 'kernel': 'rbf', 'gamma': 0.016998085132034966}
0.587 (+/-0.234) for {'C': 5248074.602497723, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.666 (+/-0.315) for {'C': 10.0, 'kernel': 'linear'}
0.637 (+/-0.237) for {'C': 100.0, 'kernel': 'linear'}
0.662 (+/-0.227) for {'C': 1000.0, 'kernel': 'linear'}
0.611 (+/-0.241) for {'C': 10000.0, 'kernel': 'linear'}
0.611 (+/-0.240) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.17      0.17      0.17         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 10.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6658653846153846
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 0.0005248074602497723, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.496 (+/-0.001) for {'C': 0.0005248074602497723, 'kernel': 'rbf', 'gamma': 0.01650440985652279}
0.496 (+/-0.001) for {'C': 0.0005248074602497723, 'kernel': 'rbf', 'gamma': 0.01656532649318018}
0.496 (+/-0.001) for {'C': 0.0005248074602497723, 'kernel': 'rbf', 'gamma': 0.016626467968935365}
0.496 (+/-0.001) for {'C': 0.0005248074602497723, 'kernel': 'rbf', 'gamma': 0.016687835113653904}
0.496 (+/-0.001) for {'C': 0.0005248074602497723, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.496 (+/-0.001) for {'C': 0.0005248074602497723, 'kernel': 'rbf', 'gamma': 0.016811249744769604}
0.496 (+/-0.001) for {'C': 0.0005248074602497723, 'kernel': 'rbf', 'gamma': 0.01687329890625804}
0.496 (+/-0.001) for {'C': 0.0005248074602497723, 'kernel': 'rbf', 'gamma': 0.01693557708691519}
0.496 (+/-0.001) for {'C': 0.0005248074602497723, 'kernel': 'rbf', 'gamma': 0.016998085132034966}
0.496 (+/-0.001) for {'C': 0.0005248074602497723, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.496 (+/-0.001) for {'C': 0.005248074602497723, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.496 (+/-0.001) for {'C': 0.005248074602497723, 'kernel': 'rbf', 'gamma': 0.01650440985652279}
0.496 (+/-0.001) for {'C': 0.005248074602497723, 'kernel': 'rbf', 'gamma': 0.01656532649318018}
0.496 (+/-0.001) for {'C': 0.005248074602497723, 'kernel': 'rbf', 'gamma': 0.016626467968935365}
0.496 (+/-0.001) for {'C': 0.005248074602497723, 'kernel': 'rbf', 'gamma': 0.016687835113653904}
0.496 (+/-0.001) for {'C': 0.005248074602497723, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.496 (+/-0.001) for {'C': 0.005248074602497723, 'kernel': 'rbf', 'gamma': 0.016811249744769604}
0.496 (+/-0.001) for {'C': 0.005248074602497723, 'kernel': 'rbf', 'gamma': 0.01687329890625804}
0.496 (+/-0.001) for {'C': 0.005248074602497723, 'kernel': 'rbf', 'gamma': 0.01693557708691519}
0.496 (+/-0.001) for {'C': 0.005248074602497723, 'kernel': 'rbf', 'gamma': 0.016998085132034966}
0.496 (+/-0.001) for {'C': 0.005248074602497723, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.496 (+/-0.001) for {'C': 0.05248074602497723, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.496 (+/-0.001) for {'C': 0.05248074602497723, 'kernel': 'rbf', 'gamma': 0.01650440985652279}
0.496 (+/-0.001) for {'C': 0.05248074602497723, 'kernel': 'rbf', 'gamma': 0.01656532649318018}
0.496 (+/-0.001) for {'C': 0.05248074602497723, 'kernel': 'rbf', 'gamma': 0.016626467968935365}
0.496 (+/-0.001) for {'C': 0.05248074602497723, 'kernel': 'rbf', 'gamma': 0.016687835113653904}
0.496 (+/-0.001) for {'C': 0.05248074602497723, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.496 (+/-0.001) for {'C': 0.05248074602497723, 'kernel': 'rbf', 'gamma': 0.016811249744769604}
0.496 (+/-0.001) for {'C': 0.05248074602497723, 'kernel': 'rbf', 'gamma': 0.01687329890625804}
0.496 (+/-0.001) for {'C': 0.05248074602497723, 'kernel': 'rbf', 'gamma': 0.01693557708691519}
0.496 (+/-0.001) for {'C': 0.05248074602497723, 'kernel': 'rbf', 'gamma': 0.016998085132034966}
0.496 (+/-0.001) for {'C': 0.05248074602497723, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.747 (+/-0.502) for {'C': 0.5248074602497723, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.747 (+/-0.502) for {'C': 0.5248074602497723, 'kernel': 'rbf', 'gamma': 0.01650440985652279}
0.747 (+/-0.502) for {'C': 0.5248074602497723, 'kernel': 'rbf', 'gamma': 0.01656532649318018}
0.747 (+/-0.502) for {'C': 0.5248074602497723, 'kernel': 'rbf', 'gamma': 0.016626467968935365}
0.747 (+/-0.502) for {'C': 0.5248074602497723, 'kernel': 'rbf', 'gamma': 0.016687835113653904}
0.747 (+/-0.502) for {'C': 0.5248074602497723, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.747 (+/-0.502) for {'C': 0.5248074602497723, 'kernel': 'rbf', 'gamma': 0.016811249744769604}
0.747 (+/-0.502) for {'C': 0.5248074602497723, 'kernel': 'rbf', 'gamma': 0.01687329890625804}
0.747 (+/-0.502) for {'C': 0.5248074602497723, 'kernel': 'rbf', 'gamma': 0.01693557708691519}
0.747 (+/-0.502) for {'C': 0.5248074602497723, 'kernel': 'rbf', 'gamma': 0.016998085132034966}
0.747 (+/-0.502) for {'C': 0.5248074602497723, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.747 (+/-0.502) for {'C': 5.248074602497723, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.747 (+/-0.502) for {'C': 5.248074602497723, 'kernel': 'rbf', 'gamma': 0.01650440985652279}
0.747 (+/-0.502) for {'C': 5.248074602497723, 'kernel': 'rbf', 'gamma': 0.01656532649318018}
0.747 (+/-0.502) for {'C': 5.248074602497723, 'kernel': 'rbf', 'gamma': 0.016626467968935365}
0.747 (+/-0.502) for {'C': 5.248074602497723, 'kernel': 'rbf', 'gamma': 0.016687835113653904}
0.747 (+/-0.502) for {'C': 5.248074602497723, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.747 (+/-0.502) for {'C': 5.248074602497723, 'kernel': 'rbf', 'gamma': 0.016811249744769604}
0.747 (+/-0.502) for {'C': 5.248074602497723, 'kernel': 'rbf', 'gamma': 0.01687329890625804}
0.747 (+/-0.502) for {'C': 5.248074602497723, 'kernel': 'rbf', 'gamma': 0.01693557708691519}
0.747 (+/-0.502) for {'C': 5.248074602497723, 'kernel': 'rbf', 'gamma': 0.016998085132034966}
0.747 (+/-0.502) for {'C': 5.248074602497723, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.727 (+/-0.397) for {'C': 52.48074602497723, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.735 (+/-0.402) for {'C': 52.48074602497723, 'kernel': 'rbf', 'gamma': 0.01650440985652279}
0.735 (+/-0.402) for {'C': 52.48074602497723, 'kernel': 'rbf', 'gamma': 0.01656532649318018}
0.735 (+/-0.402) for {'C': 52.48074602497723, 'kernel': 'rbf', 'gamma': 0.016626467968935365}
0.735 (+/-0.402) for {'C': 52.48074602497723, 'kernel': 'rbf', 'gamma': 0.016687835113653904}
0.735 (+/-0.402) for {'C': 52.48074602497723, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.735 (+/-0.402) for {'C': 52.48074602497723, 'kernel': 'rbf', 'gamma': 0.016811249744769604}
0.735 (+/-0.402) for {'C': 52.48074602497723, 'kernel': 'rbf', 'gamma': 0.01687329890625804}
0.735 (+/-0.402) for {'C': 52.48074602497723, 'kernel': 'rbf', 'gamma': 0.01693557708691519}
0.735 (+/-0.402) for {'C': 52.48074602497723, 'kernel': 'rbf', 'gamma': 0.016998085132034966}
0.735 (+/-0.402) for {'C': 52.48074602497723, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.658 (+/-0.387) for {'C': 524.8074602497722, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.658 (+/-0.387) for {'C': 524.8074602497722, 'kernel': 'rbf', 'gamma': 0.01650440985652279}
0.658 (+/-0.387) for {'C': 524.8074602497722, 'kernel': 'rbf', 'gamma': 0.01656532649318018}
0.658 (+/-0.387) for {'C': 524.8074602497722, 'kernel': 'rbf', 'gamma': 0.016626467968935365}
0.658 (+/-0.387) for {'C': 524.8074602497722, 'kernel': 'rbf', 'gamma': 0.016687835113653904}
0.658 (+/-0.387) for {'C': 524.8074602497722, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.658 (+/-0.387) for {'C': 524.8074602497722, 'kernel': 'rbf', 'gamma': 0.016811249744769604}
0.658 (+/-0.387) for {'C': 524.8074602497722, 'kernel': 'rbf', 'gamma': 0.01687329890625804}
0.658 (+/-0.387) for {'C': 524.8074602497722, 'kernel': 'rbf', 'gamma': 0.01693557708691519}
0.658 (+/-0.387) for {'C': 524.8074602497722, 'kernel': 'rbf', 'gamma': 0.016998085132034966}
0.658 (+/-0.387) for {'C': 524.8074602497722, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.629 (+/-0.312) for {'C': 5248.074602497723, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.629 (+/-0.312) for {'C': 5248.074602497723, 'kernel': 'rbf', 'gamma': 0.01650440985652279}
0.629 (+/-0.312) for {'C': 5248.074602497723, 'kernel': 'rbf', 'gamma': 0.01656532649318018}
0.629 (+/-0.312) for {'C': 5248.074602497723, 'kernel': 'rbf', 'gamma': 0.016626467968935365}
0.629 (+/-0.312) for {'C': 5248.074602497723, 'kernel': 'rbf', 'gamma': 0.016687835113653904}
0.629 (+/-0.312) for {'C': 5248.074602497723, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.629 (+/-0.312) for {'C': 5248.074602497723, 'kernel': 'rbf', 'gamma': 0.016811249744769604}
0.629 (+/-0.312) for {'C': 5248.074602497723, 'kernel': 'rbf', 'gamma': 0.01687329890625804}
0.629 (+/-0.312) for {'C': 5248.074602497723, 'kernel': 'rbf', 'gamma': 0.01693557708691519}
0.629 (+/-0.312) for {'C': 5248.074602497723, 'kernel': 'rbf', 'gamma': 0.016998085132034966}
0.629 (+/-0.312) for {'C': 5248.074602497723, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.614 (+/-0.327) for {'C': 52480.74602497723, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.614 (+/-0.327) for {'C': 52480.74602497723, 'kernel': 'rbf', 'gamma': 0.01650440985652279}
0.614 (+/-0.327) for {'C': 52480.74602497723, 'kernel': 'rbf', 'gamma': 0.01656532649318018}
0.614 (+/-0.327) for {'C': 52480.74602497723, 'kernel': 'rbf', 'gamma': 0.016626467968935365}
0.614 (+/-0.327) for {'C': 52480.74602497723, 'kernel': 'rbf', 'gamma': 0.016687835113653904}
0.614 (+/-0.327) for {'C': 52480.74602497723, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.614 (+/-0.327) for {'C': 52480.74602497723, 'kernel': 'rbf', 'gamma': 0.016811249744769604}
0.614 (+/-0.327) for {'C': 52480.74602497723, 'kernel': 'rbf', 'gamma': 0.01687329890625804}
0.614 (+/-0.327) for {'C': 52480.74602497723, 'kernel': 'rbf', 'gamma': 0.01693557708691519}
0.614 (+/-0.327) for {'C': 52480.74602497723, 'kernel': 'rbf', 'gamma': 0.016998085132034966}
0.614 (+/-0.327) for {'C': 52480.74602497723, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.614 (+/-0.327) for {'C': 524807.4602497723, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.614 (+/-0.327) for {'C': 524807.4602497723, 'kernel': 'rbf', 'gamma': 0.01650440985652279}
0.614 (+/-0.327) for {'C': 524807.4602497723, 'kernel': 'rbf', 'gamma': 0.01656532649318018}
0.614 (+/-0.327) for {'C': 524807.4602497723, 'kernel': 'rbf', 'gamma': 0.016626467968935365}
0.614 (+/-0.327) for {'C': 524807.4602497723, 'kernel': 'rbf', 'gamma': 0.016687835113653904}
0.614 (+/-0.327) for {'C': 524807.4602497723, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.614 (+/-0.327) for {'C': 524807.4602497723, 'kernel': 'rbf', 'gamma': 0.016811249744769604}
0.614 (+/-0.327) for {'C': 524807.4602497723, 'kernel': 'rbf', 'gamma': 0.01687329890625804}
0.614 (+/-0.327) for {'C': 524807.4602497723, 'kernel': 'rbf', 'gamma': 0.01693557708691519}
0.614 (+/-0.327) for {'C': 524807.4602497723, 'kernel': 'rbf', 'gamma': 0.016998085132034966}
0.614 (+/-0.327) for {'C': 524807.4602497723, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.614 (+/-0.327) for {'C': 5248074.602497723, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.614 (+/-0.327) for {'C': 5248074.602497723, 'kernel': 'rbf', 'gamma': 0.01650440985652279}
0.614 (+/-0.327) for {'C': 5248074.602497723, 'kernel': 'rbf', 'gamma': 0.01656532649318018}
0.614 (+/-0.327) for {'C': 5248074.602497723, 'kernel': 'rbf', 'gamma': 0.016626467968935365}
0.614 (+/-0.327) for {'C': 5248074.602497723, 'kernel': 'rbf', 'gamma': 0.016687835113653904}
0.614 (+/-0.327) for {'C': 5248074.602497723, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.614 (+/-0.327) for {'C': 5248074.602497723, 'kernel': 'rbf', 'gamma': 0.016811249744769604}
0.614 (+/-0.327) for {'C': 5248074.602497723, 'kernel': 'rbf', 'gamma': 0.01687329890625804}
0.614 (+/-0.327) for {'C': 5248074.602497723, 'kernel': 'rbf', 'gamma': 0.01693557708691519}
0.614 (+/-0.327) for {'C': 5248074.602497723, 'kernel': 'rbf', 'gamma': 0.016998085132034966}
0.614 (+/-0.327) for {'C': 5248074.602497723, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.673 (+/-0.377) for {'C': 10.0, 'kernel': 'linear'}
0.631 (+/-0.312) for {'C': 100.0, 'kernel': 'linear'}
0.653 (+/-0.293) for {'C': 1000.0, 'kernel': 'linear'}
0.619 (+/-0.322) for {'C': 10000.0, 'kernel': 'linear'}
0.619 (+/-0.322) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.20      0.17      0.18         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.99      0.99       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 0.0005248074602497723, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.500 (+/-0.000) for {'C': 0.0005248074602497723, 'kernel': 'rbf', 'gamma': 0.01650440985652279}
0.500 (+/-0.000) for {'C': 0.0005248074602497723, 'kernel': 'rbf', 'gamma': 0.01656532649318018}
0.500 (+/-0.000) for {'C': 0.0005248074602497723, 'kernel': 'rbf', 'gamma': 0.016626467968935365}
0.500 (+/-0.000) for {'C': 0.0005248074602497723, 'kernel': 'rbf', 'gamma': 0.016687835113653904}
0.500 (+/-0.000) for {'C': 0.0005248074602497723, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.500 (+/-0.000) for {'C': 0.0005248074602497723, 'kernel': 'rbf', 'gamma': 0.016811249744769604}
0.500 (+/-0.000) for {'C': 0.0005248074602497723, 'kernel': 'rbf', 'gamma': 0.01687329890625804}
0.500 (+/-0.000) for {'C': 0.0005248074602497723, 'kernel': 'rbf', 'gamma': 0.01693557708691519}
0.500 (+/-0.000) for {'C': 0.0005248074602497723, 'kernel': 'rbf', 'gamma': 0.016998085132034966}
0.500 (+/-0.000) for {'C': 0.0005248074602497723, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.500 (+/-0.000) for {'C': 0.005248074602497723, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.500 (+/-0.000) for {'C': 0.005248074602497723, 'kernel': 'rbf', 'gamma': 0.01650440985652279}
0.500 (+/-0.000) for {'C': 0.005248074602497723, 'kernel': 'rbf', 'gamma': 0.01656532649318018}
0.500 (+/-0.000) for {'C': 0.005248074602497723, 'kernel': 'rbf', 'gamma': 0.016626467968935365}
0.500 (+/-0.000) for {'C': 0.005248074602497723, 'kernel': 'rbf', 'gamma': 0.016687835113653904}
0.500 (+/-0.000) for {'C': 0.005248074602497723, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.500 (+/-0.000) for {'C': 0.005248074602497723, 'kernel': 'rbf', 'gamma': 0.016811249744769604}
0.500 (+/-0.000) for {'C': 0.005248074602497723, 'kernel': 'rbf', 'gamma': 0.01687329890625804}
0.500 (+/-0.000) for {'C': 0.005248074602497723, 'kernel': 'rbf', 'gamma': 0.01693557708691519}
0.500 (+/-0.000) for {'C': 0.005248074602497723, 'kernel': 'rbf', 'gamma': 0.016998085132034966}
0.500 (+/-0.000) for {'C': 0.005248074602497723, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.500 (+/-0.000) for {'C': 0.05248074602497723, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.500 (+/-0.000) for {'C': 0.05248074602497723, 'kernel': 'rbf', 'gamma': 0.01650440985652279}
0.500 (+/-0.000) for {'C': 0.05248074602497723, 'kernel': 'rbf', 'gamma': 0.01656532649318018}
0.500 (+/-0.000) for {'C': 0.05248074602497723, 'kernel': 'rbf', 'gamma': 0.016626467968935365}
0.500 (+/-0.000) for {'C': 0.05248074602497723, 'kernel': 'rbf', 'gamma': 0.016687835113653904}
0.500 (+/-0.000) for {'C': 0.05248074602497723, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.500 (+/-0.000) for {'C': 0.05248074602497723, 'kernel': 'rbf', 'gamma': 0.016811249744769604}
0.500 (+/-0.000) for {'C': 0.05248074602497723, 'kernel': 'rbf', 'gamma': 0.01687329890625804}
0.500 (+/-0.000) for {'C': 0.05248074602497723, 'kernel': 'rbf', 'gamma': 0.01693557708691519}
0.500 (+/-0.000) for {'C': 0.05248074602497723, 'kernel': 'rbf', 'gamma': 0.016998085132034966}
0.500 (+/-0.000) for {'C': 0.05248074602497723, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.617 (+/-0.238) for {'C': 0.5248074602497723, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.617 (+/-0.238) for {'C': 0.5248074602497723, 'kernel': 'rbf', 'gamma': 0.01650440985652279}
0.617 (+/-0.238) for {'C': 0.5248074602497723, 'kernel': 'rbf', 'gamma': 0.01656532649318018}
0.617 (+/-0.238) for {'C': 0.5248074602497723, 'kernel': 'rbf', 'gamma': 0.016626467968935365}
0.617 (+/-0.238) for {'C': 0.5248074602497723, 'kernel': 'rbf', 'gamma': 0.016687835113653904}
0.617 (+/-0.238) for {'C': 0.5248074602497723, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.617 (+/-0.238) for {'C': 0.5248074602497723, 'kernel': 'rbf', 'gamma': 0.016811249744769604}
0.617 (+/-0.238) for {'C': 0.5248074602497723, 'kernel': 'rbf', 'gamma': 0.01687329890625804}
0.617 (+/-0.238) for {'C': 0.5248074602497723, 'kernel': 'rbf', 'gamma': 0.01693557708691519}
0.617 (+/-0.238) for {'C': 0.5248074602497723, 'kernel': 'rbf', 'gamma': 0.016998085132034966}
0.617 (+/-0.238) for {'C': 0.5248074602497723, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.617 (+/-0.238) for {'C': 5.248074602497723, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.617 (+/-0.238) for {'C': 5.248074602497723, 'kernel': 'rbf', 'gamma': 0.01650440985652279}
0.617 (+/-0.238) for {'C': 5.248074602497723, 'kernel': 'rbf', 'gamma': 0.01656532649318018}
0.617 (+/-0.238) for {'C': 5.248074602497723, 'kernel': 'rbf', 'gamma': 0.016626467968935365}
0.617 (+/-0.238) for {'C': 5.248074602497723, 'kernel': 'rbf', 'gamma': 0.016687835113653904}
0.617 (+/-0.238) for {'C': 5.248074602497723, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.617 (+/-0.238) for {'C': 5.248074602497723, 'kernel': 'rbf', 'gamma': 0.016811249744769604}
0.617 (+/-0.238) for {'C': 5.248074602497723, 'kernel': 'rbf', 'gamma': 0.01687329890625804}
0.617 (+/-0.238) for {'C': 5.248074602497723, 'kernel': 'rbf', 'gamma': 0.01693557708691519}
0.617 (+/-0.238) for {'C': 5.248074602497723, 'kernel': 'rbf', 'gamma': 0.016998085132034966}
0.617 (+/-0.238) for {'C': 5.248074602497723, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.682 (+/-0.294) for {'C': 52.48074602497723, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.682 (+/-0.295) for {'C': 52.48074602497723, 'kernel': 'rbf', 'gamma': 0.01650440985652279}
0.682 (+/-0.295) for {'C': 52.48074602497723, 'kernel': 'rbf', 'gamma': 0.01656532649318018}
0.682 (+/-0.295) for {'C': 52.48074602497723, 'kernel': 'rbf', 'gamma': 0.016626467968935365}
0.682 (+/-0.295) for {'C': 52.48074602497723, 'kernel': 'rbf', 'gamma': 0.016687835113653904}
0.682 (+/-0.295) for {'C': 52.48074602497723, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.682 (+/-0.295) for {'C': 52.48074602497723, 'kernel': 'rbf', 'gamma': 0.016811249744769604}
0.682 (+/-0.295) for {'C': 52.48074602497723, 'kernel': 'rbf', 'gamma': 0.01687329890625804}
0.682 (+/-0.295) for {'C': 52.48074602497723, 'kernel': 'rbf', 'gamma': 0.01693557708691519}
0.682 (+/-0.295) for {'C': 52.48074602497723, 'kernel': 'rbf', 'gamma': 0.016998085132034966}
0.682 (+/-0.295) for {'C': 52.48074602497723, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.658 (+/-0.314) for {'C': 524.8074602497722, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.658 (+/-0.314) for {'C': 524.8074602497722, 'kernel': 'rbf', 'gamma': 0.01650440985652279}
0.658 (+/-0.314) for {'C': 524.8074602497722, 'kernel': 'rbf', 'gamma': 0.01656532649318018}
0.658 (+/-0.314) for {'C': 524.8074602497722, 'kernel': 'rbf', 'gamma': 0.016626467968935365}
0.658 (+/-0.314) for {'C': 524.8074602497722, 'kernel': 'rbf', 'gamma': 0.016687835113653904}
0.658 (+/-0.314) for {'C': 524.8074602497722, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.658 (+/-0.314) for {'C': 524.8074602497722, 'kernel': 'rbf', 'gamma': 0.016811249744769604}
0.658 (+/-0.315) for {'C': 524.8074602497722, 'kernel': 'rbf', 'gamma': 0.01687329890625804}
0.658 (+/-0.315) for {'C': 524.8074602497722, 'kernel': 'rbf', 'gamma': 0.01693557708691519}
0.658 (+/-0.315) for {'C': 524.8074602497722, 'kernel': 'rbf', 'gamma': 0.016998085132034966}
0.658 (+/-0.314) for {'C': 524.8074602497722, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.637 (+/-0.237) for {'C': 5248.074602497723, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.637 (+/-0.237) for {'C': 5248.074602497723, 'kernel': 'rbf', 'gamma': 0.01650440985652279}
0.637 (+/-0.237) for {'C': 5248.074602497723, 'kernel': 'rbf', 'gamma': 0.01656532649318018}
0.637 (+/-0.237) for {'C': 5248.074602497723, 'kernel': 'rbf', 'gamma': 0.016626467968935365}
0.637 (+/-0.237) for {'C': 5248.074602497723, 'kernel': 'rbf', 'gamma': 0.016687835113653904}
0.637 (+/-0.237) for {'C': 5248.074602497723, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.637 (+/-0.237) for {'C': 5248.074602497723, 'kernel': 'rbf', 'gamma': 0.016811249744769604}
0.637 (+/-0.237) for {'C': 5248.074602497723, 'kernel': 'rbf', 'gamma': 0.01687329890625804}
0.637 (+/-0.237) for {'C': 5248.074602497723, 'kernel': 'rbf', 'gamma': 0.01693557708691519}
0.637 (+/-0.237) for {'C': 5248.074602497723, 'kernel': 'rbf', 'gamma': 0.016998085132034966}
0.637 (+/-0.237) for {'C': 5248.074602497723, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.588 (+/-0.232) for {'C': 52480.74602497723, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.588 (+/-0.232) for {'C': 52480.74602497723, 'kernel': 'rbf', 'gamma': 0.01650440985652279}
0.588 (+/-0.232) for {'C': 52480.74602497723, 'kernel': 'rbf', 'gamma': 0.01656532649318018}
0.588 (+/-0.232) for {'C': 52480.74602497723, 'kernel': 'rbf', 'gamma': 0.016626467968935365}
0.588 (+/-0.232) for {'C': 52480.74602497723, 'kernel': 'rbf', 'gamma': 0.016687835113653904}
0.588 (+/-0.232) for {'C': 52480.74602497723, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.588 (+/-0.232) for {'C': 52480.74602497723, 'kernel': 'rbf', 'gamma': 0.016811249744769604}
0.588 (+/-0.232) for {'C': 52480.74602497723, 'kernel': 'rbf', 'gamma': 0.01687329890625804}
0.588 (+/-0.232) for {'C': 52480.74602497723, 'kernel': 'rbf', 'gamma': 0.01693557708691519}
0.588 (+/-0.232) for {'C': 52480.74602497723, 'kernel': 'rbf', 'gamma': 0.016998085132034966}
0.588 (+/-0.232) for {'C': 52480.74602497723, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.587 (+/-0.234) for {'C': 524807.4602497723, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.587 (+/-0.234) for {'C': 524807.4602497723, 'kernel': 'rbf', 'gamma': 0.01650440985652279}
0.587 (+/-0.234) for {'C': 524807.4602497723, 'kernel': 'rbf', 'gamma': 0.01656532649318018}
0.587 (+/-0.234) for {'C': 524807.4602497723, 'kernel': 'rbf', 'gamma': 0.016626467968935365}
0.587 (+/-0.234) for {'C': 524807.4602497723, 'kernel': 'rbf', 'gamma': 0.016687835113653904}
0.587 (+/-0.234) for {'C': 524807.4602497723, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.587 (+/-0.234) for {'C': 524807.4602497723, 'kernel': 'rbf', 'gamma': 0.016811249744769604}
0.587 (+/-0.234) for {'C': 524807.4602497723, 'kernel': 'rbf', 'gamma': 0.01687329890625804}
0.587 (+/-0.234) for {'C': 524807.4602497723, 'kernel': 'rbf', 'gamma': 0.01693557708691519}
0.587 (+/-0.234) for {'C': 524807.4602497723, 'kernel': 'rbf', 'gamma': 0.016998085132034966}
0.587 (+/-0.234) for {'C': 524807.4602497723, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.587 (+/-0.234) for {'C': 5248074.602497723, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.587 (+/-0.234) for {'C': 5248074.602497723, 'kernel': 'rbf', 'gamma': 0.01650440985652279}
0.587 (+/-0.234) for {'C': 5248074.602497723, 'kernel': 'rbf', 'gamma': 0.01656532649318018}
0.587 (+/-0.234) for {'C': 5248074.602497723, 'kernel': 'rbf', 'gamma': 0.016626467968935365}
0.587 (+/-0.234) for {'C': 5248074.602497723, 'kernel': 'rbf', 'gamma': 0.016687835113653904}
0.587 (+/-0.234) for {'C': 5248074.602497723, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.587 (+/-0.234) for {'C': 5248074.602497723, 'kernel': 'rbf', 'gamma': 0.016811249744769604}
0.587 (+/-0.234) for {'C': 5248074.602497723, 'kernel': 'rbf', 'gamma': 0.01687329890625804}
0.587 (+/-0.234) for {'C': 5248074.602497723, 'kernel': 'rbf', 'gamma': 0.01693557708691519}
0.587 (+/-0.234) for {'C': 5248074.602497723, 'kernel': 'rbf', 'gamma': 0.016998085132034966}
0.587 (+/-0.234) for {'C': 5248074.602497723, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.663 (+/-0.315) for {'C': 10.0, 'kernel': 'linear'}
0.661 (+/-0.315) for {'C': 100.0, 'kernel': 'linear'}
0.662 (+/-0.227) for {'C': 1000.0, 'kernel': 'linear'}
0.611 (+/-0.241) for {'C': 10000.0, 'kernel': 'linear'}
0.611 (+/-0.240) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.17      0.17      0.17         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 52.48074602497723, 'kernel': 'rbf', 'gamma': 0.01650440985652279}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6822115384615385
第2轮loop中，tunemodel函数得到的最优参数是： ([{'C': array([  5.2480746 ,   8.31763771,  13.18256739,  20.89296131,
        33.11311215,  52.48074602,  83.17637711, 131.82567386,
       208.92961309, 331.13112148, 524.80746025]), 'kernel': ['rbf'], 'gamma': array([0.01644372, 0.01645584, 0.01646797, 0.01648011, 0.01649225,
       0.01650441, 0.01651658, 0.01652875, 0.01654093, 0.01655313,
       0.01656533])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}], {'C': 52.48074602497723, 'kernel': 'rbf', 'gamma': 0.01650440985652279}, 0.6822115384615385)
这是第2次迭代微调C和gamma。
第2次迭代，得到delta： [3.55271368e-14 2.45018904e-04]
SVC模型clf_op_iter的参数是： {'kernel': 'rbf', 'decision_function_shape': 'ovr', 'class_weight': {-1: 15}, 'tol': 0.001, 'gamma': 0.01650440985652279, 'random_state': 0, 'cache_size': 200, 'verbose': False, 'degree': 3, 'C': 52.48074602497723, 'probability': False, 'shrinking': True, 'coef0': 0.0, 'max_iter': -1}

训练模型SVC对训练样本的预测准确率： 0.9468462083628633

测试集中，预测为舞弊样本的有： (array([   8,   10,   11,   12,   13,   14,   19,   20,   21,   22,   24,
         25,   30,   31,   32,   33,   34,   35,   36,   37,   38,   39,
         40,   41,   42,   43,   44,   47,   48,   49,   50,   51,   52,
         53,   54,   58,   59,   60,   64,   65,   66,   67,   68,   71,
         72,   73,   74,   75,   76,   77,   78,   79,   88,   90,   91,
         92,   93,   98,  101,  107,  108,  109,  110,  111,  112,  113,
        114,  115,  116,  117,  118,  119,  121,  127,  129,  135,  136,
        142,  143,  144,  145,  146,  150,  151,  153,  156,  157,  158,
        163,  164,  165,  166,  176,  177,  180,  181,  182,  183,  184,
        185,  186,  187,  192,  193,  197,  199,  200,  201,  202,  204,
        207,  208,  212,  213,  214,  215,  216,  219,  225,  231,  232,
        233,  234,  235,  236,  237,  238,  239,  242,  243,  244,  245,
        246,  247,  251,  252,  253,  254,  255,  256,  257,  258,  259,
        260,  261,  262,  264,  268,  269,  270,  275,  276,  277,  278,
        279,  280,  281,  283,  284,  285,  286,  287,  288,  299,  300,
        301,  302,  306,  313,  316,  317,  319,  322,  323,  324,  325,
        330,  331,  332,  333,  334,  338,  339,  340,  346,  347,  348,
        349,  351,  352,  353,  354,  356,  358,  359,  360,  361,  362,
        363,  364,  365,  366,  367,  368,  369,  370,  379,  383,  384,
        385,  386,  388,  391,  392,  393,  394,  395,  396,  398,  399,
        403,  404,  410,  414,  419,  420,  421,  422,  423,  426,  427,
        429,  430,  431,  432,  433,  434,  438,  439,  440,  441,  442,
        443,  444,  447,  449,  454,  455,  456,  457,  461,  463,  465,
        466,  467,  468,  469,  471,  472,  473,  474,  475,  476,  477,
        478,  479,  481,  482,  483,  484,  485,  486,  487,  488,  489,
        490,  492,  496,  498,  499,  500,  501,  502,  503,  509,  513,
        514,  515,  516,  518,  519,  520,  521,  529,  530,  532,  534,
        535,  540,  541,  543,  544,  545,  546,  547,  549,  555,  557,
        558,  562,  563,  564,  565,  566,  567,  568,  571,  572,  573,
        582,  583,  584,  586,  587,  589,  590,  593,  595,  600,  601,
        604,  605,  611,  612,  613,  614,  615,  616,  617,  618,  619,
        624,  629,  630,  633,  634,  635,  641,  642,  643,  651,  653,
        656,  657,  658,  661,  662,  664,  665,  666,  667,  668,  669,
        674,  675,  676,  677,  680,  684,  685,  686,  687,  688,  691,
        692,  693,  694,  695,  696,  697,  699,  701,  702,  706,  716,
        717,  718,  720,  721,  722,  723,  724,  729,  735,  736,  742,
        743,  752,  753,  754,  756,  758,  759,  760,  761,  769,  774,
        775,  776,  777,  778,  786,  787,  788,  789,  790,  791,  792,
        793,  794,  795,  796,  797,  807,  813,  814,  815,  816,  817,
        819,  820,  825,  826,  827,  828,  829,  830,  840,  841,  843,
        844,  845,  848,  851,  852,  853,  854,  855,  856,  857,  859,
        862,  864,  872,  874,  875,  876,  877,  878,  879,  880,  881,
        882,  883,  884,  885,  897,  898,  902,  903,  904,  905,  906,
        907,  910,  911,  912,  913,  914,  916,  923,  928,  929,  930,
        931,  932,  933,  934,  935,  948,  949,  950,  951,  952,  953,
        954,  955,  956,  959,  960,  961,  963,  964,  965,  966,  967,
        971,  976,  977,  978,  979,  980,  981,  982,  983,  984,  985,
        998, 1000, 1002, 1003, 1004, 1005, 1006, 1008, 1009, 1012, 1013,
       1014, 1015, 1016, 1017, 1020, 1021, 1022, 1030, 1032, 1033, 1034,
       1037, 1040, 1041, 1048, 1049, 1050, 1051, 1056, 1057, 1060, 1061,
       1064, 1065, 1067, 1068, 1071, 1073, 1074, 1080, 1081, 1082, 1085,
       1086, 1089, 1090, 1092, 1094, 1095, 1096, 1097, 1098, 1099, 1100,
       1101, 1106, 1107, 1112, 1113, 1114, 1119, 1121, 1122, 1123, 1124,
       1127, 1129, 1131, 1132, 1134, 1135, 1137, 1140, 1141, 1142, 1144,
       1148, 1152, 1154, 1156, 1158, 1160, 1163, 1164, 1167, 1168, 1171,
       1175, 1180, 1181, 1183, 1188, 1191, 1196, 1205, 1211, 1215, 1217,
       1218, 1219, 1222, 1227, 1230, 1231, 1232, 1233, 1234, 1235, 1236,
       1237, 1238, 1239, 1240, 1241, 1242, 1243, 1244, 1246, 1247, 1248,
       1249, 1250, 1251, 1252, 1254, 1255, 1256], dtype=int64),) 
测试集中，实际为舞弊样本的有： (array([1246, 1247, 1248, 1249, 1250, 1251, 1252, 1253, 1254, 1255, 1256],
      dtype=int64),) 
预测的舞弊样本数目： 645

训练模型SVC对测试样本的预测准确率： 0.5393338058114813
以上是第30次特征筛选。
第30次特征筛选，AUC值是： 0.6997300452356633
X_train_iter_svc.shape is: (1257, 22)
X_test_iter_svc.shape is: (1257, 22)
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.647 (+/-0.401) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.747 (+/-0.449) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.17      0.17      0.17         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.589 (+/-0.232) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.641 (+/-0.236) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.17      0.17      0.17         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6413461538461539
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.637 (+/-0.307) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.20      0.17      0.18         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.99      0.99       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.637 (+/-0.239) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.20      0.17      0.18         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.99      0.99       629

本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6663461538461538
RBF的粗grid search最佳超参： {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001} RBF的粗grid search最高分值： 0.6413461538461539
linear的粗grid search最佳超参： {'C': 1.0, 'kernel': 'linear'} linear的粗grid search最高分值： 0.6663461538461538
粗grid search得到的parameter是：
 [{'C': array([1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03, 1.e+04, 1.e+05,
       1.e+06, 1.e+07, 1.e+08]), 'kernel': ['rbf'], 'gamma': array([1.e-08, 1.e-07, 1.e-06, 1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01,
       1.e+00, 1.e+01, 1.e+02])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.647 (+/-0.333) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.449) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.654 (+/-0.392) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.449) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.375) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.647 (+/-0.401) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.748 (+/-0.449) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.375) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.630 (+/-0.311) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.647 (+/-0.401) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.747 (+/-0.502) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.748 (+/-0.449) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.706 (+/-0.383) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.629 (+/-0.312) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.647 (+/-0.401) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.747 (+/-0.502) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.748 (+/-0.449) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.706 (+/-0.383) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.630 (+/-0.311) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.621 (+/-0.320) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.647 (+/-0.401) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.649 (+/-0.778) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.772 (+/-0.473) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.668 (+/-0.290) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.637 (+/-0.310) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.637 (+/-0.307) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.614 (+/-0.327) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.647 (+/-0.401) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.699 (+/-0.797) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.764 (+/-0.478) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.659 (+/-0.313) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.629 (+/-0.312) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.633 (+/-0.310) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.614 (+/-0.327) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.647 (+/-0.401) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.706 (+/-0.383) for {'C': 10.0, 'kernel': 'linear'}
0.643 (+/-0.320) for {'C': 100.0, 'kernel': 'linear'}
0.637 (+/-0.307) for {'C': 1000.0, 'kernel': 'linear'}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'linear'}
0.620 (+/-0.321) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.006) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.641 (+/-0.236) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.239) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.641 (+/-0.236) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.641 (+/-0.236) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.587 (+/-0.233) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.589 (+/-0.232) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.316) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.641 (+/-0.236) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.232) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.589 (+/-0.232) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.617 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.666 (+/-0.316) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.316) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.636 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.588 (+/-0.232) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.232) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.589 (+/-0.232) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.617 (+/-0.238) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.666 (+/-0.316) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.665 (+/-0.317) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.637 (+/-0.238) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.612 (+/-0.241) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.586 (+/-0.235) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.232) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.589 (+/-0.232) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.617 (+/-0.238) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.642 (+/-0.236) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.681 (+/-0.294) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.661 (+/-0.319) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.638 (+/-0.238) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.586 (+/-0.236) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.586 (+/-0.235) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.232) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.589 (+/-0.232) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.642 (+/-0.236) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.641 (+/-0.236) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.664 (+/-0.316) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.660 (+/-0.315) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.636 (+/-0.240) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.586 (+/-0.236) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.586 (+/-0.235) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.232) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.589 (+/-0.232) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.666 (+/-0.315) for {'C': 10.0, 'kernel': 'linear'}
0.637 (+/-0.237) for {'C': 100.0, 'kernel': 'linear'}
0.637 (+/-0.239) for {'C': 1000.0, 'kernel': 'linear'}
0.585 (+/-0.236) for {'C': 10000.0, 'kernel': 'linear'}
0.611 (+/-0.242) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.11      0.17      0.13         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6812489694121527
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.673 (+/-0.330) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.627 (+/-0.330) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.714 (+/-0.359) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.609 (+/-0.313) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.654 (+/-0.392) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.714 (+/-0.359) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.658 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.647 (+/-0.401) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.714 (+/-0.359) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.658 (+/-0.388) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.630 (+/-0.311) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.647 (+/-0.401) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.747 (+/-0.502) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.797 (+/-0.492) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.677 (+/-0.374) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.658 (+/-0.387) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.629 (+/-0.312) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.647 (+/-0.401) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.024) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.747 (+/-0.502) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.660 (+/-0.290) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.658 (+/-0.388) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.630 (+/-0.311) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.621 (+/-0.320) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.647 (+/-0.401) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.699 (+/-0.797) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.710 (+/-0.420) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.637 (+/-0.291) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.630 (+/-0.310) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.637 (+/-0.307) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.614 (+/-0.327) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.647 (+/-0.401) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.650 (+/-0.777) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.662 (+/-0.373) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.625 (+/-0.315) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.628 (+/-0.312) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.633 (+/-0.310) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.614 (+/-0.327) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.647 (+/-0.401) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.674 (+/-0.375) for {'C': 10.0, 'kernel': 'linear'}
0.631 (+/-0.312) for {'C': 100.0, 'kernel': 'linear'}
0.637 (+/-0.307) for {'C': 1000.0, 'kernel': 'linear'}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'linear'}
0.620 (+/-0.321) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.006) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.666 (+/-0.315) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.611 (+/-0.242) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.682 (+/-0.295) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.608 (+/-0.244) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.239) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.682 (+/-0.295) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.658 (+/-0.314) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.587 (+/-0.233) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.589 (+/-0.232) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.682 (+/-0.295) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.658 (+/-0.314) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.232) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.589 (+/-0.232) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.617 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.642 (+/-0.236) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.665 (+/-0.314) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.658 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.636 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.588 (+/-0.232) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.232) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.589 (+/-0.232) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.523 (+/-0.138) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.617 (+/-0.238) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.681 (+/-0.293) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.658 (+/-0.315) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.637 (+/-0.238) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.612 (+/-0.241) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.586 (+/-0.235) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.232) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.589 (+/-0.232) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.642 (+/-0.236) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.641 (+/-0.235) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.677 (+/-0.295) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.660 (+/-0.317) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.638 (+/-0.238) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.586 (+/-0.236) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.586 (+/-0.235) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.232) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.589 (+/-0.232) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.651 (+/-0.258) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.640 (+/-0.233) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.658 (+/-0.315) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.660 (+/-0.314) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.636 (+/-0.240) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.586 (+/-0.236) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.586 (+/-0.235) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.232) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.589 (+/-0.232) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.663 (+/-0.316) for {'C': 10.0, 'kernel': 'linear'}
0.661 (+/-0.315) for {'C': 100.0, 'kernel': 'linear'}
0.637 (+/-0.239) for {'C': 1000.0, 'kernel': 'linear'}
0.585 (+/-0.236) for {'C': 10000.0, 'kernel': 'linear'}
0.611 (+/-0.242) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.17      0.17      0.17         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6822115384615385
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.747 (+/-0.502) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.747 (+/-0.502) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.747 (+/-0.502) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.747 (+/-0.502) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.747 (+/-0.502) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.747 (+/-0.502) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.747 (+/-0.502) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.747 (+/-0.502) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.697 (+/-0.437) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.747 (+/-0.502) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.747 (+/-0.502) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.747 (+/-0.502) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.747 (+/-0.502) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.747 (+/-0.502) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.747 (+/-0.502) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.747 (+/-0.502) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.747 (+/-0.449) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.714 (+/-0.418) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.747 (+/-0.502) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.747 (+/-0.502) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.747 (+/-0.502) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.747 (+/-0.502) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.747 (+/-0.502) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.747 (+/-0.502) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.747 (+/-0.449) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.723 (+/-0.423) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.664 (+/-0.388) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.747 (+/-0.502) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.747 (+/-0.502) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.747 (+/-0.502) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.747 (+/-0.502) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.747 (+/-0.502) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.747 (+/-0.449) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.723 (+/-0.423) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.664 (+/-0.388) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.631 (+/-0.319) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.747 (+/-0.502) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.747 (+/-0.502) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.747 (+/-0.502) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.747 (+/-0.502) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.747 (+/-0.449) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.723 (+/-0.423) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.698 (+/-0.383) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.664 (+/-0.388) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.664 (+/-0.388) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.747 (+/-0.502) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.747 (+/-0.502) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.747 (+/-0.502) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.747 (+/-0.449) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.723 (+/-0.423) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.723 (+/-0.423) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.664 (+/-0.388) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.672 (+/-0.392) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.631 (+/-0.319) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.747 (+/-0.502) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.722 (+/-0.473) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.747 (+/-0.449) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.723 (+/-0.423) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.723 (+/-0.423) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.689 (+/-0.374) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.672 (+/-0.392) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.664 (+/-0.388) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.631 (+/-0.319) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.722 (+/-0.473) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.747 (+/-0.449) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.723 (+/-0.423) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.723 (+/-0.423) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.698 (+/-0.383) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.697 (+/-0.375) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.672 (+/-0.392) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.614 (+/-0.327) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.614 (+/-0.327) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.001}
0.722 (+/-0.473) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.747 (+/-0.449) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.723 (+/-0.423) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.723 (+/-0.423) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.698 (+/-0.383) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.697 (+/-0.375) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.672 (+/-0.392) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.622 (+/-0.336) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.614 (+/-0.327) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.614 (+/-0.327) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.449) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.723 (+/-0.423) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.723 (+/-0.423) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.698 (+/-0.383) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.697 (+/-0.375) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.672 (+/-0.392) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.657 (+/-0.390) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.621 (+/-0.320) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.614 (+/-0.327) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.614 (+/-0.327) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.449) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.723 (+/-0.423) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.723 (+/-0.423) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.698 (+/-0.383) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.706 (+/-0.383) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.697 (+/-0.375) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.657 (+/-0.390) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.621 (+/-0.320) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.619 (+/-0.322) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.706 (+/-0.383) for {'C': 10.0, 'kernel': 'linear'}
0.643 (+/-0.320) for {'C': 100.0, 'kernel': 'linear'}
0.637 (+/-0.307) for {'C': 1000.0, 'kernel': 'linear'}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'linear'}
0.620 (+/-0.321) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.25      0.17      0.20         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.99       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.617 (+/-0.238) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.617 (+/-0.238) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.617 (+/-0.238) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.617 (+/-0.238) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.617 (+/-0.238) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.617 (+/-0.238) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.617 (+/-0.238) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.617 (+/-0.238) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.616 (+/-0.237) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.617 (+/-0.238) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.617 (+/-0.238) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.617 (+/-0.238) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.617 (+/-0.238) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.617 (+/-0.238) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.617 (+/-0.238) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.617 (+/-0.238) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.641 (+/-0.236) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.641 (+/-0.235) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.617 (+/-0.238) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.617 (+/-0.238) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.617 (+/-0.238) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.617 (+/-0.238) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.617 (+/-0.238) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.617 (+/-0.238) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.641 (+/-0.236) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.666 (+/-0.315) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.616 (+/-0.237) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.617 (+/-0.238) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.617 (+/-0.238) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.617 (+/-0.238) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.617 (+/-0.238) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.617 (+/-0.238) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.641 (+/-0.236) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.666 (+/-0.315) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.616 (+/-0.237) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.616 (+/-0.236) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.617 (+/-0.238) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.617 (+/-0.238) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.617 (+/-0.238) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.617 (+/-0.238) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.641 (+/-0.236) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.666 (+/-0.315) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.666 (+/-0.315) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.616 (+/-0.237) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.616 (+/-0.237) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.617 (+/-0.238) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.617 (+/-0.238) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.617 (+/-0.238) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.641 (+/-0.236) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.666 (+/-0.315) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.666 (+/-0.315) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.616 (+/-0.237) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.616 (+/-0.238) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.615 (+/-0.237) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.617 (+/-0.238) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.617 (+/-0.238) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.641 (+/-0.236) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.666 (+/-0.315) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.666 (+/-0.315) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.641 (+/-0.235) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.616 (+/-0.238) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.616 (+/-0.238) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.615 (+/-0.237) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.617 (+/-0.238) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.641 (+/-0.236) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.666 (+/-0.315) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.666 (+/-0.315) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.666 (+/-0.315) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.641 (+/-0.236) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.616 (+/-0.238) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.590 (+/-0.229) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.588 (+/-0.233) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.641 (+/-0.236) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.666 (+/-0.315) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.666 (+/-0.315) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.666 (+/-0.315) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.641 (+/-0.236) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.616 (+/-0.238) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.590 (+/-0.230) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.588 (+/-0.232) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.587 (+/-0.234) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.001}
0.641 (+/-0.236) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.666 (+/-0.315) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.666 (+/-0.315) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.666 (+/-0.315) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.641 (+/-0.236) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.616 (+/-0.238) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.615 (+/-0.238) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.613 (+/-0.238) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.587 (+/-0.234) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.587 (+/-0.234) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.1}
0.641 (+/-0.236) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.315) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.666 (+/-0.315) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.666 (+/-0.315) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.666 (+/-0.315) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.641 (+/-0.236) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.615 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.613 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.612 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.587 (+/-0.234) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.587 (+/-0.233) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.666 (+/-0.315) for {'C': 10.0, 'kernel': 'linear'}
0.637 (+/-0.237) for {'C': 100.0, 'kernel': 'linear'}
0.637 (+/-0.239) for {'C': 1000.0, 'kernel': 'linear'}
0.585 (+/-0.236) for {'C': 10000.0, 'kernel': 'linear'}
0.611 (+/-0.242) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.20      0.17      0.18         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.99      0.99       629

本轮grid search结果，得到最好的参数选择是： {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.666025641025641
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.747 (+/-0.502) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.747 (+/-0.502) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.747 (+/-0.502) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.747 (+/-0.502) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.747 (+/-0.502) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.747 (+/-0.502) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.797 (+/-0.492) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.748 (+/-0.449) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.689 (+/-0.382) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.673 (+/-0.330) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.747 (+/-0.502) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.747 (+/-0.502) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.747 (+/-0.502) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.747 (+/-0.502) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.797 (+/-0.492) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.748 (+/-0.449) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.698 (+/-0.383) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.664 (+/-0.326) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.673 (+/-0.330) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.747 (+/-0.502) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.747 (+/-0.502) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.747 (+/-0.502) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.797 (+/-0.492) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.748 (+/-0.449) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.706 (+/-0.383) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.664 (+/-0.326) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.673 (+/-0.330) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.651 (+/-0.308) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.747 (+/-0.502) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.747 (+/-0.502) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.797 (+/-0.492) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.748 (+/-0.449) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.706 (+/-0.383) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.664 (+/-0.326) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.673 (+/-0.330) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.651 (+/-0.308) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.626 (+/-0.313) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.747 (+/-0.502) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.797 (+/-0.492) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.748 (+/-0.449) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.706 (+/-0.383) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.714 (+/-0.359) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.673 (+/-0.330) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.659 (+/-0.313) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.659 (+/-0.385) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.650 (+/-0.385) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.797 (+/-0.492) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.748 (+/-0.449) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.706 (+/-0.383) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.714 (+/-0.359) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.673 (+/-0.330) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.689 (+/-0.374) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.669 (+/-0.373) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.663 (+/-0.383) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.609 (+/-0.313) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.001}
0.797 (+/-0.492) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.748 (+/-0.449) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.706 (+/-0.383) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.714 (+/-0.359) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.673 (+/-0.330) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.689 (+/-0.374) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.669 (+/-0.373) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.663 (+/-0.383) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.647 (+/-0.387) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.618 (+/-0.322) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.1}
0.797 (+/-0.492) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.001}
0.748 (+/-0.449) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.706 (+/-0.383) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.714 (+/-0.359) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.673 (+/-0.330) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.689 (+/-0.374) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.669 (+/-0.373) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.663 (+/-0.383) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.658 (+/-0.387) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.622 (+/-0.319) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.614 (+/-0.327) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.1}
0.748 (+/-0.449) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.001}
0.706 (+/-0.383) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.714 (+/-0.359) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.673 (+/-0.330) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.689 (+/-0.374) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.669 (+/-0.373) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.663 (+/-0.383) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.658 (+/-0.388) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.630 (+/-0.328) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.618 (+/-0.322) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.614 (+/-0.327) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.1}
0.706 (+/-0.383) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.001}
0.714 (+/-0.359) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.673 (+/-0.330) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.689 (+/-0.374) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.669 (+/-0.373) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.663 (+/-0.383) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.658 (+/-0.388) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.660 (+/-0.385) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.622 (+/-0.319) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.614 (+/-0.327) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.614 (+/-0.327) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.1}
0.714 (+/-0.359) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.673 (+/-0.330) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.689 (+/-0.374) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.669 (+/-0.373) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.663 (+/-0.383) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.658 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.660 (+/-0.385) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.626 (+/-0.313) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.625 (+/-0.314) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.674 (+/-0.375) for {'C': 10.0, 'kernel': 'linear'}
0.631 (+/-0.312) for {'C': 100.0, 'kernel': 'linear'}
0.637 (+/-0.307) for {'C': 1000.0, 'kernel': 'linear'}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'linear'}
0.620 (+/-0.321) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.617 (+/-0.238) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.617 (+/-0.238) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.617 (+/-0.238) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.617 (+/-0.238) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.617 (+/-0.238) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.617 (+/-0.238) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.642 (+/-0.236) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.666 (+/-0.316) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.666 (+/-0.315) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.666 (+/-0.315) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.617 (+/-0.238) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.617 (+/-0.238) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.617 (+/-0.238) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.617 (+/-0.238) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.642 (+/-0.236) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.666 (+/-0.316) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.666 (+/-0.315) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.666 (+/-0.315) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.665 (+/-0.315) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.617 (+/-0.238) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.617 (+/-0.238) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.617 (+/-0.238) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.642 (+/-0.236) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.666 (+/-0.316) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.666 (+/-0.315) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.666 (+/-0.315) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.665 (+/-0.315) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.665 (+/-0.314) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.617 (+/-0.238) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.617 (+/-0.238) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.642 (+/-0.236) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.666 (+/-0.316) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.666 (+/-0.315) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.666 (+/-0.315) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.665 (+/-0.315) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.665 (+/-0.314) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.639 (+/-0.325) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.617 (+/-0.238) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.642 (+/-0.236) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.666 (+/-0.316) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.666 (+/-0.315) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.682 (+/-0.295) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.665 (+/-0.315) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.665 (+/-0.314) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.639 (+/-0.325) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.636 (+/-0.327) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.642 (+/-0.236) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.666 (+/-0.316) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.666 (+/-0.315) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.682 (+/-0.295) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.665 (+/-0.315) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.665 (+/-0.315) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.664 (+/-0.315) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.661 (+/-0.315) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.608 (+/-0.244) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.001}
0.642 (+/-0.236) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.666 (+/-0.316) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.666 (+/-0.315) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.682 (+/-0.295) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.665 (+/-0.315) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.665 (+/-0.315) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.664 (+/-0.315) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.661 (+/-0.315) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.633 (+/-0.328) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.609 (+/-0.243) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.1}
0.642 (+/-0.236) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.316) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.666 (+/-0.315) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.682 (+/-0.295) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.665 (+/-0.315) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.665 (+/-0.315) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.664 (+/-0.315) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.661 (+/-0.315) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.658 (+/-0.314) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.634 (+/-0.326) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.585 (+/-0.237) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.1}
0.666 (+/-0.316) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.315) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.682 (+/-0.295) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.665 (+/-0.315) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.665 (+/-0.315) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.664 (+/-0.315) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.661 (+/-0.315) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.658 (+/-0.314) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.635 (+/-0.325) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.610 (+/-0.242) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.586 (+/-0.235) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.1}
0.666 (+/-0.315) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.001}
0.682 (+/-0.295) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.665 (+/-0.315) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.665 (+/-0.315) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.664 (+/-0.315) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.661 (+/-0.315) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.658 (+/-0.314) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.660 (+/-0.314) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.635 (+/-0.324) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.586 (+/-0.236) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.586 (+/-0.235) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.1}
0.682 (+/-0.295) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.665 (+/-0.315) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.665 (+/-0.315) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.664 (+/-0.315) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.661 (+/-0.315) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.658 (+/-0.314) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.660 (+/-0.314) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.660 (+/-0.312) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.636 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.586 (+/-0.235) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.587 (+/-0.233) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.663 (+/-0.316) for {'C': 10.0, 'kernel': 'linear'}
0.661 (+/-0.315) for {'C': 100.0, 'kernel': 'linear'}
0.637 (+/-0.239) for {'C': 1000.0, 'kernel': 'linear'}
0.585 (+/-0.236) for {'C': 10000.0, 'kernel': 'linear'}
0.611 (+/-0.242) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.17      0.17      0.17         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6822115384615385
循环迭代之前，delta is: [3.69042656e+01 5.84893192e-03]
执行tunemodel()函数前，使用的grid search parameter是：
 [{'C': array([ 39.81071706,  43.65158322,  47.86300923,  52.48074602,
        57.54399373,  63.09573445,  69.18309709,  75.8577575 ,
        83.17637711,  91.20108394, 100.        ]), 'kernel': ['rbf'], 'gamma': array([0.01      , 0.01096478, 0.01202264, 0.01318257, 0.0144544 ,
       0.01584893, 0.01737801, 0.01905461, 0.02089296, 0.02290868,
       0.02511886])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.747 (+/-0.502) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.747 (+/-0.502) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.747 (+/-0.502) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.747 (+/-0.502) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.747 (+/-0.502) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.747 (+/-0.502) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.722 (+/-0.473) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.747 (+/-0.449) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.747 (+/-0.449) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.747 (+/-0.449) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.747 (+/-0.449) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.747 (+/-0.502) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.747 (+/-0.502) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.747 (+/-0.502) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.747 (+/-0.502) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.747 (+/-0.502) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.722 (+/-0.473) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.747 (+/-0.449) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.747 (+/-0.449) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.747 (+/-0.449) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.747 (+/-0.449) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.747 (+/-0.449) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.747 (+/-0.502) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.747 (+/-0.502) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.747 (+/-0.502) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.747 (+/-0.502) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.772 (+/-0.473) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.747 (+/-0.449) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.747 (+/-0.449) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.747 (+/-0.449) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.747 (+/-0.449) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.747 (+/-0.449) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.748 (+/-0.449) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.747 (+/-0.502) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.747 (+/-0.502) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.747 (+/-0.502) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.772 (+/-0.473) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.747 (+/-0.449) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.747 (+/-0.449) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.747 (+/-0.449) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.747 (+/-0.449) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.747 (+/-0.449) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.748 (+/-0.449) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.748 (+/-0.449) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.747 (+/-0.502) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.747 (+/-0.502) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.772 (+/-0.473) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.747 (+/-0.449) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.747 (+/-0.449) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.747 (+/-0.449) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.747 (+/-0.449) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.747 (+/-0.449) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.748 (+/-0.449) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.748 (+/-0.449) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.748 (+/-0.449) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.747 (+/-0.502) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.772 (+/-0.473) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.747 (+/-0.449) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.747 (+/-0.449) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.747 (+/-0.449) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.747 (+/-0.449) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.747 (+/-0.449) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.748 (+/-0.449) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.748 (+/-0.449) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.748 (+/-0.449) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.723 (+/-0.423) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.772 (+/-0.473) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.747 (+/-0.449) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.747 (+/-0.449) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.747 (+/-0.449) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.747 (+/-0.449) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.747 (+/-0.449) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.748 (+/-0.449) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.748 (+/-0.449) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.748 (+/-0.449) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.723 (+/-0.423) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.723 (+/-0.423) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.747 (+/-0.449) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.747 (+/-0.449) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.747 (+/-0.449) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.747 (+/-0.449) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.747 (+/-0.449) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.748 (+/-0.449) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.748 (+/-0.449) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.748 (+/-0.449) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.723 (+/-0.423) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.723 (+/-0.423) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.698 (+/-0.383) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.747 (+/-0.449) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.747 (+/-0.449) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.747 (+/-0.449) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.748 (+/-0.449) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.748 (+/-0.449) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.748 (+/-0.449) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.748 (+/-0.449) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.723 (+/-0.423) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.723 (+/-0.423) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.698 (+/-0.383) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.698 (+/-0.383) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.747 (+/-0.449) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.747 (+/-0.449) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.748 (+/-0.449) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.748 (+/-0.449) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.748 (+/-0.449) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.748 (+/-0.449) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.723 (+/-0.423) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.723 (+/-0.423) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.698 (+/-0.383) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.698 (+/-0.383) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.698 (+/-0.383) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.747 (+/-0.449) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.748 (+/-0.449) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.748 (+/-0.449) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.748 (+/-0.449) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.748 (+/-0.449) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.723 (+/-0.423) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.723 (+/-0.423) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.698 (+/-0.383) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.698 (+/-0.383) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.698 (+/-0.383) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.723 (+/-0.423) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.706 (+/-0.383) for {'C': 10.0, 'kernel': 'linear'}
0.643 (+/-0.320) for {'C': 100.0, 'kernel': 'linear'}
0.637 (+/-0.307) for {'C': 1000.0, 'kernel': 'linear'}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'linear'}
0.620 (+/-0.321) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.617 (+/-0.238) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.617 (+/-0.238) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.617 (+/-0.238) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.617 (+/-0.238) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.617 (+/-0.238) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.617 (+/-0.238) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.617 (+/-0.238) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.641 (+/-0.236) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.641 (+/-0.236) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.641 (+/-0.236) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.641 (+/-0.236) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.617 (+/-0.238) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.617 (+/-0.238) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.617 (+/-0.238) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.617 (+/-0.238) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.617 (+/-0.238) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.617 (+/-0.238) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.641 (+/-0.236) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.641 (+/-0.236) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.641 (+/-0.236) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.641 (+/-0.236) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.641 (+/-0.236) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.617 (+/-0.238) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.617 (+/-0.238) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.617 (+/-0.238) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.617 (+/-0.238) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.642 (+/-0.236) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.641 (+/-0.236) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.641 (+/-0.236) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.641 (+/-0.236) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.641 (+/-0.236) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.641 (+/-0.236) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.666 (+/-0.316) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.617 (+/-0.238) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.617 (+/-0.238) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.617 (+/-0.238) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.642 (+/-0.236) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.641 (+/-0.236) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.641 (+/-0.236) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.641 (+/-0.236) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.641 (+/-0.236) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.641 (+/-0.236) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.666 (+/-0.316) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.666 (+/-0.316) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.617 (+/-0.238) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.617 (+/-0.238) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.642 (+/-0.236) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.641 (+/-0.236) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.641 (+/-0.236) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.641 (+/-0.236) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.641 (+/-0.236) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.641 (+/-0.236) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.666 (+/-0.316) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.666 (+/-0.316) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.666 (+/-0.316) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.617 (+/-0.238) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.642 (+/-0.236) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.641 (+/-0.236) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.641 (+/-0.236) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.641 (+/-0.236) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.641 (+/-0.236) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.641 (+/-0.236) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.666 (+/-0.316) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.666 (+/-0.316) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.666 (+/-0.316) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.666 (+/-0.315) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.642 (+/-0.236) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.641 (+/-0.236) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.641 (+/-0.236) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.641 (+/-0.236) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.641 (+/-0.236) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.641 (+/-0.236) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.666 (+/-0.316) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.666 (+/-0.316) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.666 (+/-0.316) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.666 (+/-0.315) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.666 (+/-0.315) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.641 (+/-0.236) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.641 (+/-0.236) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.641 (+/-0.236) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.641 (+/-0.236) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.641 (+/-0.236) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.666 (+/-0.316) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.666 (+/-0.316) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.666 (+/-0.316) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.666 (+/-0.315) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.666 (+/-0.315) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.666 (+/-0.315) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.641 (+/-0.236) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.641 (+/-0.236) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.641 (+/-0.236) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.666 (+/-0.316) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.666 (+/-0.316) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.666 (+/-0.316) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.666 (+/-0.316) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.666 (+/-0.315) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.666 (+/-0.315) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.666 (+/-0.315) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.666 (+/-0.315) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.641 (+/-0.236) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.641 (+/-0.236) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.666 (+/-0.316) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.666 (+/-0.316) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.666 (+/-0.316) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.666 (+/-0.316) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.666 (+/-0.315) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.666 (+/-0.315) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.666 (+/-0.315) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.666 (+/-0.315) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.666 (+/-0.315) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.641 (+/-0.236) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.666 (+/-0.316) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.666 (+/-0.316) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.666 (+/-0.316) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.666 (+/-0.316) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.666 (+/-0.315) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.666 (+/-0.315) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.666 (+/-0.315) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.666 (+/-0.315) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.666 (+/-0.315) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.666 (+/-0.315) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.666 (+/-0.315) for {'C': 10.0, 'kernel': 'linear'}
0.637 (+/-0.237) for {'C': 100.0, 'kernel': 'linear'}
0.637 (+/-0.239) for {'C': 1000.0, 'kernel': 'linear'}
0.585 (+/-0.236) for {'C': 10000.0, 'kernel': 'linear'}
0.611 (+/-0.242) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.20      0.17      0.18         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.99      0.99       629

本轮grid search结果，得到最好的参数选择是： {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6663461538461538
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.748 (+/-0.449) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.748 (+/-0.449) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.748 (+/-0.449) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.748 (+/-0.449) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.748 (+/-0.449) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.706 (+/-0.383) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.685 (+/-0.384) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.677 (+/-0.374) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.727 (+/-0.397) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.735 (+/-0.402) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.664 (+/-0.326) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.748 (+/-0.449) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.748 (+/-0.449) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.748 (+/-0.449) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.748 (+/-0.449) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.706 (+/-0.383) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.685 (+/-0.384) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.677 (+/-0.374) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.727 (+/-0.397) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.735 (+/-0.402) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.664 (+/-0.326) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.664 (+/-0.326) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.748 (+/-0.449) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.748 (+/-0.449) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.748 (+/-0.449) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.706 (+/-0.383) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.685 (+/-0.384) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.677 (+/-0.374) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.727 (+/-0.397) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.735 (+/-0.402) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.664 (+/-0.326) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.664 (+/-0.326) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.673 (+/-0.330) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.748 (+/-0.449) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.748 (+/-0.449) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.706 (+/-0.383) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.685 (+/-0.384) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.677 (+/-0.374) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.727 (+/-0.397) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.710 (+/-0.363) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.714 (+/-0.359) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.664 (+/-0.326) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.673 (+/-0.330) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.673 (+/-0.330) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.748 (+/-0.449) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.706 (+/-0.383) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.685 (+/-0.384) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.677 (+/-0.374) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.727 (+/-0.397) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.710 (+/-0.363) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.714 (+/-0.359) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.664 (+/-0.326) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.673 (+/-0.330) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.673 (+/-0.330) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.673 (+/-0.330) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.706 (+/-0.383) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.685 (+/-0.384) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.677 (+/-0.374) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.727 (+/-0.397) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.710 (+/-0.363) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.714 (+/-0.359) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.714 (+/-0.359) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.673 (+/-0.330) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.673 (+/-0.330) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.673 (+/-0.330) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.673 (+/-0.330) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.685 (+/-0.384) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.677 (+/-0.374) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.727 (+/-0.397) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.710 (+/-0.363) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.714 (+/-0.359) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.714 (+/-0.359) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.673 (+/-0.330) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.673 (+/-0.330) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.673 (+/-0.330) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.673 (+/-0.330) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.673 (+/-0.330) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.677 (+/-0.374) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.727 (+/-0.397) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.710 (+/-0.363) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.714 (+/-0.359) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.714 (+/-0.359) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.673 (+/-0.330) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.673 (+/-0.330) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.673 (+/-0.330) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.673 (+/-0.330) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.673 (+/-0.330) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.673 (+/-0.330) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.727 (+/-0.397) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.710 (+/-0.363) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.714 (+/-0.359) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.714 (+/-0.359) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.673 (+/-0.330) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.673 (+/-0.330) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.673 (+/-0.330) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.673 (+/-0.330) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.673 (+/-0.330) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.673 (+/-0.330) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.664 (+/-0.317) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.710 (+/-0.363) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.714 (+/-0.359) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.714 (+/-0.359) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.673 (+/-0.330) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.673 (+/-0.330) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.673 (+/-0.330) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.673 (+/-0.330) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.673 (+/-0.330) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.673 (+/-0.330) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.664 (+/-0.317) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.664 (+/-0.317) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.714 (+/-0.359) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.714 (+/-0.359) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.673 (+/-0.330) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.673 (+/-0.330) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.673 (+/-0.330) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.673 (+/-0.330) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.673 (+/-0.330) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.673 (+/-0.330) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.664 (+/-0.317) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.664 (+/-0.317) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.689 (+/-0.374) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.674 (+/-0.375) for {'C': 10.0, 'kernel': 'linear'}
0.631 (+/-0.312) for {'C': 100.0, 'kernel': 'linear'}
0.637 (+/-0.307) for {'C': 1000.0, 'kernel': 'linear'}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'linear'}
0.620 (+/-0.321) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.20      0.17      0.18         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.99      0.99       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.666 (+/-0.316) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.666 (+/-0.316) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.666 (+/-0.316) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.666 (+/-0.316) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.666 (+/-0.316) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.666 (+/-0.315) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.666 (+/-0.315) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.665 (+/-0.314) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.682 (+/-0.294) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.682 (+/-0.295) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.666 (+/-0.315) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.666 (+/-0.316) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.666 (+/-0.316) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.666 (+/-0.316) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.666 (+/-0.316) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.666 (+/-0.315) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.666 (+/-0.315) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.665 (+/-0.314) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.682 (+/-0.294) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.682 (+/-0.295) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.666 (+/-0.315) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.666 (+/-0.315) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.666 (+/-0.316) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.666 (+/-0.316) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.666 (+/-0.316) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.666 (+/-0.315) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.666 (+/-0.315) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.665 (+/-0.314) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.682 (+/-0.294) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.682 (+/-0.295) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.666 (+/-0.315) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.666 (+/-0.315) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.666 (+/-0.315) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.666 (+/-0.316) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.666 (+/-0.316) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.666 (+/-0.315) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.666 (+/-0.315) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.665 (+/-0.314) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.682 (+/-0.294) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.682 (+/-0.295) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.682 (+/-0.295) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.666 (+/-0.315) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.666 (+/-0.315) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.666 (+/-0.315) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.666 (+/-0.316) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.666 (+/-0.315) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.666 (+/-0.315) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.665 (+/-0.314) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.682 (+/-0.294) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.682 (+/-0.295) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.682 (+/-0.295) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.666 (+/-0.315) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.666 (+/-0.315) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.666 (+/-0.315) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.666 (+/-0.315) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.666 (+/-0.315) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.666 (+/-0.315) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.665 (+/-0.314) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.682 (+/-0.294) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.682 (+/-0.295) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.682 (+/-0.295) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.682 (+/-0.295) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.666 (+/-0.315) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.666 (+/-0.315) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.666 (+/-0.315) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.665 (+/-0.315) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.666 (+/-0.315) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.665 (+/-0.314) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.682 (+/-0.294) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.682 (+/-0.295) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.682 (+/-0.295) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.682 (+/-0.295) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.666 (+/-0.315) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.666 (+/-0.315) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.666 (+/-0.315) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.665 (+/-0.315) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.665 (+/-0.315) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.665 (+/-0.314) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.682 (+/-0.294) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.682 (+/-0.295) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.682 (+/-0.295) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.682 (+/-0.295) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.666 (+/-0.315) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.666 (+/-0.315) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.666 (+/-0.315) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.665 (+/-0.315) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.665 (+/-0.315) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.665 (+/-0.315) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.682 (+/-0.294) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.682 (+/-0.295) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.682 (+/-0.295) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.682 (+/-0.295) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.666 (+/-0.315) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.666 (+/-0.315) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.666 (+/-0.315) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.665 (+/-0.315) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.665 (+/-0.315) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.665 (+/-0.315) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.665 (+/-0.315) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.682 (+/-0.295) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.682 (+/-0.295) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.682 (+/-0.295) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.666 (+/-0.315) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.666 (+/-0.315) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.666 (+/-0.315) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.665 (+/-0.315) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.665 (+/-0.315) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.665 (+/-0.315) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.665 (+/-0.315) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.665 (+/-0.315) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.682 (+/-0.295) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.682 (+/-0.295) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.666 (+/-0.315) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.666 (+/-0.315) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.666 (+/-0.315) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.665 (+/-0.315) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.665 (+/-0.315) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.665 (+/-0.315) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.665 (+/-0.315) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.665 (+/-0.315) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.665 (+/-0.315) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.663 (+/-0.316) for {'C': 10.0, 'kernel': 'linear'}
0.661 (+/-0.315) for {'C': 100.0, 'kernel': 'linear'}
0.637 (+/-0.239) for {'C': 1000.0, 'kernel': 'linear'}
0.585 (+/-0.236) for {'C': 10000.0, 'kernel': 'linear'}
0.611 (+/-0.242) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.17      0.17      0.17         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6822115384615385
发现最优参数C为原先的最大/最小值，直接重新设置超参。
第0轮loop中，tunemodel函数得到的最优参数是： ([{'C': array([3.98107171e-04, 3.98107171e-03, 3.98107171e-02, 3.98107171e-01,
       3.98107171e+00, 3.98107171e+01, 3.98107171e+02, 3.98107171e+03,
       3.98107171e+04, 3.98107171e+05, 3.98107171e+06]), 'kernel': ['rbf'], 'gamma': array([0.02089296, 0.02128139, 0.02167704, 0.02208005, 0.02249055,
       0.02290868, 0.02333458, 0.0237684 , 0.02421029, 0.02466039,
       0.02511886])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}], {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.02290867652767774}, 0.6822115384615385)
这是第0次迭代微调C和gamma。
第0次迭代，得到delta： [2.32850174e+01 7.05974460e-03]
执行tunemodel()函数前，使用的grid search parameter是：
 [{'C': array([3.98107171e-04, 3.98107171e-03, 3.98107171e-02, 3.98107171e-01,
       3.98107171e+00, 3.98107171e+01, 3.98107171e+02, 3.98107171e+03,
       3.98107171e+04, 3.98107171e+05, 3.98107171e+06]), 'kernel': ['rbf'], 'gamma': array([0.02089296, 0.02128139, 0.02167704, 0.02208005, 0.02249055,
       0.02290868, 0.02333458, 0.0237684 , 0.02421029, 0.02466039,
       0.02511886])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 0.0003981071705534969, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.496 (+/-0.001) for {'C': 0.0003981071705534969, 'kernel': 'rbf', 'gamma': 0.021281390459827135}
0.496 (+/-0.001) for {'C': 0.0003981071705534969, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.496 (+/-0.001) for {'C': 0.0003981071705534969, 'kernel': 'rbf', 'gamma': 0.022080047330189013}
0.496 (+/-0.001) for {'C': 0.0003981071705534969, 'kernel': 'rbf', 'gamma': 0.022490546058357815}
0.496 (+/-0.001) for {'C': 0.0003981071705534969, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.496 (+/-0.001) for {'C': 0.0003981071705534969, 'kernel': 'rbf', 'gamma': 0.023334580622810044}
0.496 (+/-0.001) for {'C': 0.0003981071705534969, 'kernel': 'rbf', 'gamma': 0.023768402866248775}
0.496 (+/-0.001) for {'C': 0.0003981071705534969, 'kernel': 'rbf', 'gamma': 0.024210290467361794}
0.496 (+/-0.001) for {'C': 0.0003981071705534969, 'kernel': 'rbf', 'gamma': 0.0246603933723434}
0.496 (+/-0.001) for {'C': 0.0003981071705534969, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.496 (+/-0.001) for {'C': 0.003981071705534969, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.496 (+/-0.001) for {'C': 0.003981071705534969, 'kernel': 'rbf', 'gamma': 0.021281390459827135}
0.496 (+/-0.001) for {'C': 0.003981071705534969, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.496 (+/-0.001) for {'C': 0.003981071705534969, 'kernel': 'rbf', 'gamma': 0.022080047330189013}
0.496 (+/-0.001) for {'C': 0.003981071705534969, 'kernel': 'rbf', 'gamma': 0.022490546058357815}
0.496 (+/-0.001) for {'C': 0.003981071705534969, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.496 (+/-0.001) for {'C': 0.003981071705534969, 'kernel': 'rbf', 'gamma': 0.023334580622810044}
0.496 (+/-0.001) for {'C': 0.003981071705534969, 'kernel': 'rbf', 'gamma': 0.023768402866248775}
0.496 (+/-0.001) for {'C': 0.003981071705534969, 'kernel': 'rbf', 'gamma': 0.024210290467361794}
0.496 (+/-0.001) for {'C': 0.003981071705534969, 'kernel': 'rbf', 'gamma': 0.0246603933723434}
0.496 (+/-0.001) for {'C': 0.003981071705534969, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.496 (+/-0.001) for {'C': 0.03981071705534969, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.496 (+/-0.001) for {'C': 0.03981071705534969, 'kernel': 'rbf', 'gamma': 0.021281390459827135}
0.496 (+/-0.001) for {'C': 0.03981071705534969, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.496 (+/-0.001) for {'C': 0.03981071705534969, 'kernel': 'rbf', 'gamma': 0.022080047330189013}
0.496 (+/-0.001) for {'C': 0.03981071705534969, 'kernel': 'rbf', 'gamma': 0.022490546058357815}
0.496 (+/-0.001) for {'C': 0.03981071705534969, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.496 (+/-0.001) for {'C': 0.03981071705534969, 'kernel': 'rbf', 'gamma': 0.023334580622810044}
0.496 (+/-0.001) for {'C': 0.03981071705534969, 'kernel': 'rbf', 'gamma': 0.023768402866248775}
0.496 (+/-0.001) for {'C': 0.03981071705534969, 'kernel': 'rbf', 'gamma': 0.024210290467361794}
0.496 (+/-0.001) for {'C': 0.03981071705534969, 'kernel': 'rbf', 'gamma': 0.0246603933723434}
0.496 (+/-0.001) for {'C': 0.03981071705534969, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.496 (+/-0.001) for {'C': 0.3981071705534969, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.496 (+/-0.001) for {'C': 0.3981071705534969, 'kernel': 'rbf', 'gamma': 0.021281390459827135}
0.496 (+/-0.001) for {'C': 0.3981071705534969, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.496 (+/-0.001) for {'C': 0.3981071705534969, 'kernel': 'rbf', 'gamma': 0.022080047330189013}
0.496 (+/-0.001) for {'C': 0.3981071705534969, 'kernel': 'rbf', 'gamma': 0.022490546058357815}
0.496 (+/-0.001) for {'C': 0.3981071705534969, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.496 (+/-0.001) for {'C': 0.3981071705534969, 'kernel': 'rbf', 'gamma': 0.023334580622810044}
0.496 (+/-0.001) for {'C': 0.3981071705534969, 'kernel': 'rbf', 'gamma': 0.023768402866248775}
0.496 (+/-0.001) for {'C': 0.3981071705534969, 'kernel': 'rbf', 'gamma': 0.024210290467361794}
0.496 (+/-0.001) for {'C': 0.3981071705534969, 'kernel': 'rbf', 'gamma': 0.0246603933723434}
0.496 (+/-0.001) for {'C': 0.3981071705534969, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.747 (+/-0.502) for {'C': 3.981071705534969, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.747 (+/-0.502) for {'C': 3.981071705534969, 'kernel': 'rbf', 'gamma': 0.021281390459827135}
0.747 (+/-0.502) for {'C': 3.981071705534969, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.747 (+/-0.502) for {'C': 3.981071705534969, 'kernel': 'rbf', 'gamma': 0.022080047330189013}
0.747 (+/-0.502) for {'C': 3.981071705534969, 'kernel': 'rbf', 'gamma': 0.022490546058357815}
0.747 (+/-0.502) for {'C': 3.981071705534969, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.747 (+/-0.502) for {'C': 3.981071705534969, 'kernel': 'rbf', 'gamma': 0.023334580622810044}
0.747 (+/-0.502) for {'C': 3.981071705534969, 'kernel': 'rbf', 'gamma': 0.023768402866248775}
0.747 (+/-0.502) for {'C': 3.981071705534969, 'kernel': 'rbf', 'gamma': 0.024210290467361794}
0.747 (+/-0.502) for {'C': 3.981071705534969, 'kernel': 'rbf', 'gamma': 0.0246603933723434}
0.747 (+/-0.502) for {'C': 3.981071705534969, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.747 (+/-0.449) for {'C': 39.81071705534969, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.747 (+/-0.449) for {'C': 39.81071705534969, 'kernel': 'rbf', 'gamma': 0.021281390459827135}
0.747 (+/-0.449) for {'C': 39.81071705534969, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.747 (+/-0.449) for {'C': 39.81071705534969, 'kernel': 'rbf', 'gamma': 0.022080047330189013}
0.747 (+/-0.449) for {'C': 39.81071705534969, 'kernel': 'rbf', 'gamma': 0.022490546058357815}
0.747 (+/-0.449) for {'C': 39.81071705534969, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.747 (+/-0.449) for {'C': 39.81071705534969, 'kernel': 'rbf', 'gamma': 0.023334580622810044}
0.747 (+/-0.449) for {'C': 39.81071705534969, 'kernel': 'rbf', 'gamma': 0.023768402866248775}
0.747 (+/-0.449) for {'C': 39.81071705534969, 'kernel': 'rbf', 'gamma': 0.024210290467361794}
0.747 (+/-0.449) for {'C': 39.81071705534969, 'kernel': 'rbf', 'gamma': 0.0246603933723434}
0.747 (+/-0.449) for {'C': 39.81071705534969, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.722 (+/-0.417) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.722 (+/-0.417) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.021281390459827135}
0.722 (+/-0.417) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.672 (+/-0.392) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.022080047330189013}
0.672 (+/-0.392) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.022490546058357815}
0.672 (+/-0.392) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.672 (+/-0.392) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.023334580622810044}
0.672 (+/-0.392) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.023768402866248775}
0.672 (+/-0.392) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.024210290467361794}
0.672 (+/-0.392) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.0246603933723434}
0.672 (+/-0.392) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.632 (+/-0.311) for {'C': 3981.071705534969, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.629 (+/-0.312) for {'C': 3981.071705534969, 'kernel': 'rbf', 'gamma': 0.021281390459827135}
0.629 (+/-0.312) for {'C': 3981.071705534969, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.629 (+/-0.312) for {'C': 3981.071705534969, 'kernel': 'rbf', 'gamma': 0.022080047330189013}
0.629 (+/-0.312) for {'C': 3981.071705534969, 'kernel': 'rbf', 'gamma': 0.022490546058357815}
0.630 (+/-0.311) for {'C': 3981.071705534969, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.630 (+/-0.311) for {'C': 3981.071705534969, 'kernel': 'rbf', 'gamma': 0.023334580622810044}
0.630 (+/-0.311) for {'C': 3981.071705534969, 'kernel': 'rbf', 'gamma': 0.023768402866248775}
0.619 (+/-0.321) for {'C': 3981.071705534969, 'kernel': 'rbf', 'gamma': 0.024210290467361794}
0.619 (+/-0.321) for {'C': 3981.071705534969, 'kernel': 'rbf', 'gamma': 0.0246603933723434}
0.619 (+/-0.321) for {'C': 3981.071705534969, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.614 (+/-0.327) for {'C': 39810.71705534969, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.614 (+/-0.327) for {'C': 39810.71705534969, 'kernel': 'rbf', 'gamma': 0.021281390459827135}
0.614 (+/-0.327) for {'C': 39810.71705534969, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.614 (+/-0.327) for {'C': 39810.71705534969, 'kernel': 'rbf', 'gamma': 0.022080047330189013}
0.614 (+/-0.327) for {'C': 39810.71705534969, 'kernel': 'rbf', 'gamma': 0.022490546058357815}
0.614 (+/-0.327) for {'C': 39810.71705534969, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.614 (+/-0.327) for {'C': 39810.71705534969, 'kernel': 'rbf', 'gamma': 0.023334580622810044}
0.614 (+/-0.327) for {'C': 39810.71705534969, 'kernel': 'rbf', 'gamma': 0.023768402866248775}
0.614 (+/-0.327) for {'C': 39810.71705534969, 'kernel': 'rbf', 'gamma': 0.024210290467361794}
0.614 (+/-0.327) for {'C': 39810.71705534969, 'kernel': 'rbf', 'gamma': 0.0246603933723434}
0.614 (+/-0.327) for {'C': 39810.71705534969, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.614 (+/-0.327) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.614 (+/-0.327) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 0.021281390459827135}
0.614 (+/-0.327) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.614 (+/-0.327) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 0.022080047330189013}
0.614 (+/-0.327) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 0.022490546058357815}
0.614 (+/-0.327) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.614 (+/-0.327) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 0.023334580622810044}
0.614 (+/-0.327) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 0.023768402866248775}
0.614 (+/-0.327) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 0.024210290467361794}
0.614 (+/-0.327) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 0.0246603933723434}
0.614 (+/-0.327) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.614 (+/-0.327) for {'C': 3981071.7055349695, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.614 (+/-0.327) for {'C': 3981071.7055349695, 'kernel': 'rbf', 'gamma': 0.021281390459827135}
0.614 (+/-0.327) for {'C': 3981071.7055349695, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.614 (+/-0.327) for {'C': 3981071.7055349695, 'kernel': 'rbf', 'gamma': 0.022080047330189013}
0.614 (+/-0.327) for {'C': 3981071.7055349695, 'kernel': 'rbf', 'gamma': 0.022490546058357815}
0.614 (+/-0.327) for {'C': 3981071.7055349695, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.614 (+/-0.327) for {'C': 3981071.7055349695, 'kernel': 'rbf', 'gamma': 0.023334580622810044}
0.614 (+/-0.327) for {'C': 3981071.7055349695, 'kernel': 'rbf', 'gamma': 0.023768402866248775}
0.614 (+/-0.327) for {'C': 3981071.7055349695, 'kernel': 'rbf', 'gamma': 0.024210290467361794}
0.614 (+/-0.327) for {'C': 3981071.7055349695, 'kernel': 'rbf', 'gamma': 0.0246603933723434}
0.614 (+/-0.327) for {'C': 3981071.7055349695, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.706 (+/-0.383) for {'C': 10.0, 'kernel': 'linear'}
0.643 (+/-0.320) for {'C': 100.0, 'kernel': 'linear'}
0.637 (+/-0.307) for {'C': 1000.0, 'kernel': 'linear'}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'linear'}
0.620 (+/-0.321) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.25      0.17      0.20         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.99       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 0.0003981071705534969, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.500 (+/-0.000) for {'C': 0.0003981071705534969, 'kernel': 'rbf', 'gamma': 0.021281390459827135}
0.500 (+/-0.000) for {'C': 0.0003981071705534969, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.500 (+/-0.000) for {'C': 0.0003981071705534969, 'kernel': 'rbf', 'gamma': 0.022080047330189013}
0.500 (+/-0.000) for {'C': 0.0003981071705534969, 'kernel': 'rbf', 'gamma': 0.022490546058357815}
0.500 (+/-0.000) for {'C': 0.0003981071705534969, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.500 (+/-0.000) for {'C': 0.0003981071705534969, 'kernel': 'rbf', 'gamma': 0.023334580622810044}
0.500 (+/-0.000) for {'C': 0.0003981071705534969, 'kernel': 'rbf', 'gamma': 0.023768402866248775}
0.500 (+/-0.000) for {'C': 0.0003981071705534969, 'kernel': 'rbf', 'gamma': 0.024210290467361794}
0.500 (+/-0.000) for {'C': 0.0003981071705534969, 'kernel': 'rbf', 'gamma': 0.0246603933723434}
0.500 (+/-0.000) for {'C': 0.0003981071705534969, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.500 (+/-0.000) for {'C': 0.003981071705534969, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.500 (+/-0.000) for {'C': 0.003981071705534969, 'kernel': 'rbf', 'gamma': 0.021281390459827135}
0.500 (+/-0.000) for {'C': 0.003981071705534969, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.500 (+/-0.000) for {'C': 0.003981071705534969, 'kernel': 'rbf', 'gamma': 0.022080047330189013}
0.500 (+/-0.000) for {'C': 0.003981071705534969, 'kernel': 'rbf', 'gamma': 0.022490546058357815}
0.500 (+/-0.000) for {'C': 0.003981071705534969, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.500 (+/-0.000) for {'C': 0.003981071705534969, 'kernel': 'rbf', 'gamma': 0.023334580622810044}
0.500 (+/-0.000) for {'C': 0.003981071705534969, 'kernel': 'rbf', 'gamma': 0.023768402866248775}
0.500 (+/-0.000) for {'C': 0.003981071705534969, 'kernel': 'rbf', 'gamma': 0.024210290467361794}
0.500 (+/-0.000) for {'C': 0.003981071705534969, 'kernel': 'rbf', 'gamma': 0.0246603933723434}
0.500 (+/-0.000) for {'C': 0.003981071705534969, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.500 (+/-0.000) for {'C': 0.03981071705534969, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.500 (+/-0.000) for {'C': 0.03981071705534969, 'kernel': 'rbf', 'gamma': 0.021281390459827135}
0.500 (+/-0.000) for {'C': 0.03981071705534969, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.500 (+/-0.000) for {'C': 0.03981071705534969, 'kernel': 'rbf', 'gamma': 0.022080047330189013}
0.500 (+/-0.000) for {'C': 0.03981071705534969, 'kernel': 'rbf', 'gamma': 0.022490546058357815}
0.500 (+/-0.000) for {'C': 0.03981071705534969, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.500 (+/-0.000) for {'C': 0.03981071705534969, 'kernel': 'rbf', 'gamma': 0.023334580622810044}
0.500 (+/-0.000) for {'C': 0.03981071705534969, 'kernel': 'rbf', 'gamma': 0.023768402866248775}
0.500 (+/-0.000) for {'C': 0.03981071705534969, 'kernel': 'rbf', 'gamma': 0.024210290467361794}
0.500 (+/-0.000) for {'C': 0.03981071705534969, 'kernel': 'rbf', 'gamma': 0.0246603933723434}
0.500 (+/-0.000) for {'C': 0.03981071705534969, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.500 (+/-0.000) for {'C': 0.3981071705534969, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.500 (+/-0.000) for {'C': 0.3981071705534969, 'kernel': 'rbf', 'gamma': 0.021281390459827135}
0.500 (+/-0.000) for {'C': 0.3981071705534969, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.500 (+/-0.000) for {'C': 0.3981071705534969, 'kernel': 'rbf', 'gamma': 0.022080047330189013}
0.500 (+/-0.000) for {'C': 0.3981071705534969, 'kernel': 'rbf', 'gamma': 0.022490546058357815}
0.500 (+/-0.000) for {'C': 0.3981071705534969, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.500 (+/-0.000) for {'C': 0.3981071705534969, 'kernel': 'rbf', 'gamma': 0.023334580622810044}
0.500 (+/-0.000) for {'C': 0.3981071705534969, 'kernel': 'rbf', 'gamma': 0.023768402866248775}
0.500 (+/-0.000) for {'C': 0.3981071705534969, 'kernel': 'rbf', 'gamma': 0.024210290467361794}
0.500 (+/-0.000) for {'C': 0.3981071705534969, 'kernel': 'rbf', 'gamma': 0.0246603933723434}
0.500 (+/-0.000) for {'C': 0.3981071705534969, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.617 (+/-0.238) for {'C': 3.981071705534969, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.617 (+/-0.238) for {'C': 3.981071705534969, 'kernel': 'rbf', 'gamma': 0.021281390459827135}
0.617 (+/-0.238) for {'C': 3.981071705534969, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.617 (+/-0.238) for {'C': 3.981071705534969, 'kernel': 'rbf', 'gamma': 0.022080047330189013}
0.617 (+/-0.238) for {'C': 3.981071705534969, 'kernel': 'rbf', 'gamma': 0.022490546058357815}
0.617 (+/-0.238) for {'C': 3.981071705534969, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.617 (+/-0.238) for {'C': 3.981071705534969, 'kernel': 'rbf', 'gamma': 0.023334580622810044}
0.617 (+/-0.238) for {'C': 3.981071705534969, 'kernel': 'rbf', 'gamma': 0.023768402866248775}
0.617 (+/-0.238) for {'C': 3.981071705534969, 'kernel': 'rbf', 'gamma': 0.024210290467361794}
0.617 (+/-0.238) for {'C': 3.981071705534969, 'kernel': 'rbf', 'gamma': 0.0246603933723434}
0.617 (+/-0.238) for {'C': 3.981071705534969, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.641 (+/-0.236) for {'C': 39.81071705534969, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.641 (+/-0.236) for {'C': 39.81071705534969, 'kernel': 'rbf', 'gamma': 0.021281390459827135}
0.641 (+/-0.236) for {'C': 39.81071705534969, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.641 (+/-0.236) for {'C': 39.81071705534969, 'kernel': 'rbf', 'gamma': 0.022080047330189013}
0.641 (+/-0.236) for {'C': 39.81071705534969, 'kernel': 'rbf', 'gamma': 0.022490546058357815}
0.641 (+/-0.236) for {'C': 39.81071705534969, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.641 (+/-0.236) for {'C': 39.81071705534969, 'kernel': 'rbf', 'gamma': 0.023334580622810044}
0.641 (+/-0.236) for {'C': 39.81071705534969, 'kernel': 'rbf', 'gamma': 0.023768402866248775}
0.641 (+/-0.236) for {'C': 39.81071705534969, 'kernel': 'rbf', 'gamma': 0.024210290467361794}
0.641 (+/-0.236) for {'C': 39.81071705534969, 'kernel': 'rbf', 'gamma': 0.0246603933723434}
0.641 (+/-0.236) for {'C': 39.81071705534969, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.641 (+/-0.237) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.641 (+/-0.237) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.021281390459827135}
0.641 (+/-0.237) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.616 (+/-0.238) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.022080047330189013}
0.616 (+/-0.238) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.022490546058357815}
0.616 (+/-0.238) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.616 (+/-0.238) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.023334580622810044}
0.616 (+/-0.238) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.023768402866248775}
0.616 (+/-0.238) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.024210290467361794}
0.616 (+/-0.238) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.0246603933723434}
0.616 (+/-0.238) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.637 (+/-0.238) for {'C': 3981.071705534969, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.637 (+/-0.237) for {'C': 3981.071705534969, 'kernel': 'rbf', 'gamma': 0.021281390459827135}
0.637 (+/-0.237) for {'C': 3981.071705534969, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.637 (+/-0.237) for {'C': 3981.071705534969, 'kernel': 'rbf', 'gamma': 0.022080047330189013}
0.637 (+/-0.237) for {'C': 3981.071705534969, 'kernel': 'rbf', 'gamma': 0.022490546058357815}
0.637 (+/-0.238) for {'C': 3981.071705534969, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.637 (+/-0.238) for {'C': 3981.071705534969, 'kernel': 'rbf', 'gamma': 0.023334580622810044}
0.637 (+/-0.238) for {'C': 3981.071705534969, 'kernel': 'rbf', 'gamma': 0.023768402866248775}
0.612 (+/-0.240) for {'C': 3981.071705534969, 'kernel': 'rbf', 'gamma': 0.024210290467361794}
0.612 (+/-0.240) for {'C': 3981.071705534969, 'kernel': 'rbf', 'gamma': 0.0246603933723434}
0.612 (+/-0.240) for {'C': 3981.071705534969, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.588 (+/-0.232) for {'C': 39810.71705534969, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.588 (+/-0.232) for {'C': 39810.71705534969, 'kernel': 'rbf', 'gamma': 0.021281390459827135}
0.588 (+/-0.232) for {'C': 39810.71705534969, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.588 (+/-0.232) for {'C': 39810.71705534969, 'kernel': 'rbf', 'gamma': 0.022080047330189013}
0.588 (+/-0.232) for {'C': 39810.71705534969, 'kernel': 'rbf', 'gamma': 0.022490546058357815}
0.588 (+/-0.233) for {'C': 39810.71705534969, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.588 (+/-0.233) for {'C': 39810.71705534969, 'kernel': 'rbf', 'gamma': 0.023334580622810044}
0.588 (+/-0.233) for {'C': 39810.71705534969, 'kernel': 'rbf', 'gamma': 0.023768402866248775}
0.587 (+/-0.233) for {'C': 39810.71705534969, 'kernel': 'rbf', 'gamma': 0.024210290467361794}
0.587 (+/-0.233) for {'C': 39810.71705534969, 'kernel': 'rbf', 'gamma': 0.0246603933723434}
0.587 (+/-0.234) for {'C': 39810.71705534969, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.587 (+/-0.235) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.587 (+/-0.235) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 0.021281390459827135}
0.587 (+/-0.235) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.587 (+/-0.235) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 0.022080047330189013}
0.587 (+/-0.235) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 0.022490546058357815}
0.587 (+/-0.235) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.587 (+/-0.235) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 0.023334580622810044}
0.587 (+/-0.235) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 0.023768402866248775}
0.586 (+/-0.235) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 0.024210290467361794}
0.587 (+/-0.235) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 0.0246603933723434}
0.587 (+/-0.235) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.587 (+/-0.235) for {'C': 3981071.7055349695, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.587 (+/-0.235) for {'C': 3981071.7055349695, 'kernel': 'rbf', 'gamma': 0.021281390459827135}
0.587 (+/-0.235) for {'C': 3981071.7055349695, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.587 (+/-0.235) for {'C': 3981071.7055349695, 'kernel': 'rbf', 'gamma': 0.022080047330189013}
0.587 (+/-0.235) for {'C': 3981071.7055349695, 'kernel': 'rbf', 'gamma': 0.022490546058357815}
0.587 (+/-0.235) for {'C': 3981071.7055349695, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.587 (+/-0.235) for {'C': 3981071.7055349695, 'kernel': 'rbf', 'gamma': 0.023334580622810044}
0.587 (+/-0.235) for {'C': 3981071.7055349695, 'kernel': 'rbf', 'gamma': 0.023768402866248775}
0.586 (+/-0.235) for {'C': 3981071.7055349695, 'kernel': 'rbf', 'gamma': 0.024210290467361794}
0.587 (+/-0.235) for {'C': 3981071.7055349695, 'kernel': 'rbf', 'gamma': 0.0246603933723434}
0.587 (+/-0.235) for {'C': 3981071.7055349695, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.666 (+/-0.315) for {'C': 10.0, 'kernel': 'linear'}
0.637 (+/-0.237) for {'C': 100.0, 'kernel': 'linear'}
0.637 (+/-0.239) for {'C': 1000.0, 'kernel': 'linear'}
0.585 (+/-0.236) for {'C': 10000.0, 'kernel': 'linear'}
0.611 (+/-0.242) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.17      0.17      0.17         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 10.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6658653846153846
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 0.0003981071705534969, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.496 (+/-0.001) for {'C': 0.0003981071705534969, 'kernel': 'rbf', 'gamma': 0.021281390459827135}
0.496 (+/-0.001) for {'C': 0.0003981071705534969, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.496 (+/-0.001) for {'C': 0.0003981071705534969, 'kernel': 'rbf', 'gamma': 0.022080047330189013}
0.496 (+/-0.001) for {'C': 0.0003981071705534969, 'kernel': 'rbf', 'gamma': 0.022490546058357815}
0.496 (+/-0.001) for {'C': 0.0003981071705534969, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.496 (+/-0.001) for {'C': 0.0003981071705534969, 'kernel': 'rbf', 'gamma': 0.023334580622810044}
0.496 (+/-0.001) for {'C': 0.0003981071705534969, 'kernel': 'rbf', 'gamma': 0.023768402866248775}
0.496 (+/-0.001) for {'C': 0.0003981071705534969, 'kernel': 'rbf', 'gamma': 0.024210290467361794}
0.496 (+/-0.001) for {'C': 0.0003981071705534969, 'kernel': 'rbf', 'gamma': 0.0246603933723434}
0.496 (+/-0.001) for {'C': 0.0003981071705534969, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.496 (+/-0.001) for {'C': 0.003981071705534969, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.496 (+/-0.001) for {'C': 0.003981071705534969, 'kernel': 'rbf', 'gamma': 0.021281390459827135}
0.496 (+/-0.001) for {'C': 0.003981071705534969, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.496 (+/-0.001) for {'C': 0.003981071705534969, 'kernel': 'rbf', 'gamma': 0.022080047330189013}
0.496 (+/-0.001) for {'C': 0.003981071705534969, 'kernel': 'rbf', 'gamma': 0.022490546058357815}
0.496 (+/-0.001) for {'C': 0.003981071705534969, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.496 (+/-0.001) for {'C': 0.003981071705534969, 'kernel': 'rbf', 'gamma': 0.023334580622810044}
0.496 (+/-0.001) for {'C': 0.003981071705534969, 'kernel': 'rbf', 'gamma': 0.023768402866248775}
0.496 (+/-0.001) for {'C': 0.003981071705534969, 'kernel': 'rbf', 'gamma': 0.024210290467361794}
0.496 (+/-0.001) for {'C': 0.003981071705534969, 'kernel': 'rbf', 'gamma': 0.0246603933723434}
0.496 (+/-0.001) for {'C': 0.003981071705534969, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.496 (+/-0.001) for {'C': 0.03981071705534969, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.496 (+/-0.001) for {'C': 0.03981071705534969, 'kernel': 'rbf', 'gamma': 0.021281390459827135}
0.496 (+/-0.001) for {'C': 0.03981071705534969, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.496 (+/-0.001) for {'C': 0.03981071705534969, 'kernel': 'rbf', 'gamma': 0.022080047330189013}
0.496 (+/-0.001) for {'C': 0.03981071705534969, 'kernel': 'rbf', 'gamma': 0.022490546058357815}
0.496 (+/-0.001) for {'C': 0.03981071705534969, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.496 (+/-0.001) for {'C': 0.03981071705534969, 'kernel': 'rbf', 'gamma': 0.023334580622810044}
0.496 (+/-0.001) for {'C': 0.03981071705534969, 'kernel': 'rbf', 'gamma': 0.023768402866248775}
0.496 (+/-0.001) for {'C': 0.03981071705534969, 'kernel': 'rbf', 'gamma': 0.024210290467361794}
0.496 (+/-0.001) for {'C': 0.03981071705534969, 'kernel': 'rbf', 'gamma': 0.0246603933723434}
0.496 (+/-0.001) for {'C': 0.03981071705534969, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.747 (+/-0.502) for {'C': 0.3981071705534969, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.747 (+/-0.502) for {'C': 0.3981071705534969, 'kernel': 'rbf', 'gamma': 0.021281390459827135}
0.747 (+/-0.502) for {'C': 0.3981071705534969, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.747 (+/-0.502) for {'C': 0.3981071705534969, 'kernel': 'rbf', 'gamma': 0.022080047330189013}
0.747 (+/-0.502) for {'C': 0.3981071705534969, 'kernel': 'rbf', 'gamma': 0.022490546058357815}
0.747 (+/-0.502) for {'C': 0.3981071705534969, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.747 (+/-0.502) for {'C': 0.3981071705534969, 'kernel': 'rbf', 'gamma': 0.023334580622810044}
0.747 (+/-0.502) for {'C': 0.3981071705534969, 'kernel': 'rbf', 'gamma': 0.023768402866248775}
0.747 (+/-0.502) for {'C': 0.3981071705534969, 'kernel': 'rbf', 'gamma': 0.024210290467361794}
0.747 (+/-0.502) for {'C': 0.3981071705534969, 'kernel': 'rbf', 'gamma': 0.0246603933723434}
0.747 (+/-0.502) for {'C': 0.3981071705534969, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.747 (+/-0.502) for {'C': 3.981071705534969, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.747 (+/-0.502) for {'C': 3.981071705534969, 'kernel': 'rbf', 'gamma': 0.021281390459827135}
0.747 (+/-0.502) for {'C': 3.981071705534969, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.747 (+/-0.502) for {'C': 3.981071705534969, 'kernel': 'rbf', 'gamma': 0.022080047330189013}
0.747 (+/-0.502) for {'C': 3.981071705534969, 'kernel': 'rbf', 'gamma': 0.022490546058357815}
0.747 (+/-0.502) for {'C': 3.981071705534969, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.747 (+/-0.502) for {'C': 3.981071705534969, 'kernel': 'rbf', 'gamma': 0.023334580622810044}
0.747 (+/-0.502) for {'C': 3.981071705534969, 'kernel': 'rbf', 'gamma': 0.023768402866248775}
0.747 (+/-0.502) for {'C': 3.981071705534969, 'kernel': 'rbf', 'gamma': 0.024210290467361794}
0.747 (+/-0.502) for {'C': 3.981071705534969, 'kernel': 'rbf', 'gamma': 0.0246603933723434}
0.747 (+/-0.502) for {'C': 3.981071705534969, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.727 (+/-0.397) for {'C': 39.81071705534969, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.735 (+/-0.402) for {'C': 39.81071705534969, 'kernel': 'rbf', 'gamma': 0.021281390459827135}
0.735 (+/-0.402) for {'C': 39.81071705534969, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.735 (+/-0.402) for {'C': 39.81071705534969, 'kernel': 'rbf', 'gamma': 0.022080047330189013}
0.735 (+/-0.402) for {'C': 39.81071705534969, 'kernel': 'rbf', 'gamma': 0.022490546058357815}
0.735 (+/-0.402) for {'C': 39.81071705534969, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.710 (+/-0.363) for {'C': 39.81071705534969, 'kernel': 'rbf', 'gamma': 0.023334580622810044}
0.710 (+/-0.363) for {'C': 39.81071705534969, 'kernel': 'rbf', 'gamma': 0.023768402866248775}
0.660 (+/-0.327) for {'C': 39.81071705534969, 'kernel': 'rbf', 'gamma': 0.024210290467361794}
0.660 (+/-0.327) for {'C': 39.81071705534969, 'kernel': 'rbf', 'gamma': 0.0246603933723434}
0.664 (+/-0.326) for {'C': 39.81071705534969, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.658 (+/-0.388) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.658 (+/-0.388) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.021281390459827135}
0.658 (+/-0.388) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.658 (+/-0.388) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.022080047330189013}
0.658 (+/-0.388) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.022490546058357815}
0.658 (+/-0.388) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.658 (+/-0.388) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.023334580622810044}
0.658 (+/-0.388) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.023768402866248775}
0.658 (+/-0.388) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.024210290467361794}
0.658 (+/-0.388) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.0246603933723434}
0.658 (+/-0.388) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.627 (+/-0.313) for {'C': 3981.071705534969, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.629 (+/-0.312) for {'C': 3981.071705534969, 'kernel': 'rbf', 'gamma': 0.021281390459827135}
0.629 (+/-0.312) for {'C': 3981.071705534969, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.629 (+/-0.312) for {'C': 3981.071705534969, 'kernel': 'rbf', 'gamma': 0.022080047330189013}
0.629 (+/-0.312) for {'C': 3981.071705534969, 'kernel': 'rbf', 'gamma': 0.022490546058357815}
0.630 (+/-0.311) for {'C': 3981.071705534969, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.630 (+/-0.311) for {'C': 3981.071705534969, 'kernel': 'rbf', 'gamma': 0.023334580622810044}
0.630 (+/-0.311) for {'C': 3981.071705534969, 'kernel': 'rbf', 'gamma': 0.023768402866248775}
0.619 (+/-0.321) for {'C': 3981.071705534969, 'kernel': 'rbf', 'gamma': 0.024210290467361794}
0.619 (+/-0.321) for {'C': 3981.071705534969, 'kernel': 'rbf', 'gamma': 0.0246603933723434}
0.619 (+/-0.321) for {'C': 3981.071705534969, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.614 (+/-0.327) for {'C': 39810.71705534969, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.614 (+/-0.327) for {'C': 39810.71705534969, 'kernel': 'rbf', 'gamma': 0.021281390459827135}
0.614 (+/-0.327) for {'C': 39810.71705534969, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.614 (+/-0.327) for {'C': 39810.71705534969, 'kernel': 'rbf', 'gamma': 0.022080047330189013}
0.614 (+/-0.327) for {'C': 39810.71705534969, 'kernel': 'rbf', 'gamma': 0.022490546058357815}
0.614 (+/-0.327) for {'C': 39810.71705534969, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.614 (+/-0.327) for {'C': 39810.71705534969, 'kernel': 'rbf', 'gamma': 0.023334580622810044}
0.614 (+/-0.327) for {'C': 39810.71705534969, 'kernel': 'rbf', 'gamma': 0.023768402866248775}
0.614 (+/-0.327) for {'C': 39810.71705534969, 'kernel': 'rbf', 'gamma': 0.024210290467361794}
0.614 (+/-0.327) for {'C': 39810.71705534969, 'kernel': 'rbf', 'gamma': 0.0246603933723434}
0.614 (+/-0.327) for {'C': 39810.71705534969, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.614 (+/-0.327) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.614 (+/-0.327) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 0.021281390459827135}
0.614 (+/-0.327) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.614 (+/-0.327) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 0.022080047330189013}
0.614 (+/-0.327) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 0.022490546058357815}
0.614 (+/-0.327) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.614 (+/-0.327) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 0.023334580622810044}
0.614 (+/-0.327) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 0.023768402866248775}
0.614 (+/-0.327) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 0.024210290467361794}
0.614 (+/-0.327) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 0.0246603933723434}
0.614 (+/-0.327) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.614 (+/-0.327) for {'C': 3981071.7055349695, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.614 (+/-0.327) for {'C': 3981071.7055349695, 'kernel': 'rbf', 'gamma': 0.021281390459827135}
0.614 (+/-0.327) for {'C': 3981071.7055349695, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.614 (+/-0.327) for {'C': 3981071.7055349695, 'kernel': 'rbf', 'gamma': 0.022080047330189013}
0.614 (+/-0.327) for {'C': 3981071.7055349695, 'kernel': 'rbf', 'gamma': 0.022490546058357815}
0.614 (+/-0.327) for {'C': 3981071.7055349695, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.614 (+/-0.327) for {'C': 3981071.7055349695, 'kernel': 'rbf', 'gamma': 0.023334580622810044}
0.614 (+/-0.327) for {'C': 3981071.7055349695, 'kernel': 'rbf', 'gamma': 0.023768402866248775}
0.614 (+/-0.327) for {'C': 3981071.7055349695, 'kernel': 'rbf', 'gamma': 0.024210290467361794}
0.614 (+/-0.327) for {'C': 3981071.7055349695, 'kernel': 'rbf', 'gamma': 0.0246603933723434}
0.614 (+/-0.327) for {'C': 3981071.7055349695, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.674 (+/-0.375) for {'C': 10.0, 'kernel': 'linear'}
0.631 (+/-0.312) for {'C': 100.0, 'kernel': 'linear'}
0.637 (+/-0.307) for {'C': 1000.0, 'kernel': 'linear'}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'linear'}
0.620 (+/-0.321) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.20      0.17      0.18         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.99      0.99       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 0.0003981071705534969, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.500 (+/-0.000) for {'C': 0.0003981071705534969, 'kernel': 'rbf', 'gamma': 0.021281390459827135}
0.500 (+/-0.000) for {'C': 0.0003981071705534969, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.500 (+/-0.000) for {'C': 0.0003981071705534969, 'kernel': 'rbf', 'gamma': 0.022080047330189013}
0.500 (+/-0.000) for {'C': 0.0003981071705534969, 'kernel': 'rbf', 'gamma': 0.022490546058357815}
0.500 (+/-0.000) for {'C': 0.0003981071705534969, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.500 (+/-0.000) for {'C': 0.0003981071705534969, 'kernel': 'rbf', 'gamma': 0.023334580622810044}
0.500 (+/-0.000) for {'C': 0.0003981071705534969, 'kernel': 'rbf', 'gamma': 0.023768402866248775}
0.500 (+/-0.000) for {'C': 0.0003981071705534969, 'kernel': 'rbf', 'gamma': 0.024210290467361794}
0.500 (+/-0.000) for {'C': 0.0003981071705534969, 'kernel': 'rbf', 'gamma': 0.0246603933723434}
0.500 (+/-0.000) for {'C': 0.0003981071705534969, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.500 (+/-0.000) for {'C': 0.003981071705534969, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.500 (+/-0.000) for {'C': 0.003981071705534969, 'kernel': 'rbf', 'gamma': 0.021281390459827135}
0.500 (+/-0.000) for {'C': 0.003981071705534969, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.500 (+/-0.000) for {'C': 0.003981071705534969, 'kernel': 'rbf', 'gamma': 0.022080047330189013}
0.500 (+/-0.000) for {'C': 0.003981071705534969, 'kernel': 'rbf', 'gamma': 0.022490546058357815}
0.500 (+/-0.000) for {'C': 0.003981071705534969, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.500 (+/-0.000) for {'C': 0.003981071705534969, 'kernel': 'rbf', 'gamma': 0.023334580622810044}
0.500 (+/-0.000) for {'C': 0.003981071705534969, 'kernel': 'rbf', 'gamma': 0.023768402866248775}
0.500 (+/-0.000) for {'C': 0.003981071705534969, 'kernel': 'rbf', 'gamma': 0.024210290467361794}
0.500 (+/-0.000) for {'C': 0.003981071705534969, 'kernel': 'rbf', 'gamma': 0.0246603933723434}
0.500 (+/-0.000) for {'C': 0.003981071705534969, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.500 (+/-0.000) for {'C': 0.03981071705534969, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.500 (+/-0.000) for {'C': 0.03981071705534969, 'kernel': 'rbf', 'gamma': 0.021281390459827135}
0.500 (+/-0.000) for {'C': 0.03981071705534969, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.500 (+/-0.000) for {'C': 0.03981071705534969, 'kernel': 'rbf', 'gamma': 0.022080047330189013}
0.500 (+/-0.000) for {'C': 0.03981071705534969, 'kernel': 'rbf', 'gamma': 0.022490546058357815}
0.500 (+/-0.000) for {'C': 0.03981071705534969, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.500 (+/-0.000) for {'C': 0.03981071705534969, 'kernel': 'rbf', 'gamma': 0.023334580622810044}
0.500 (+/-0.000) for {'C': 0.03981071705534969, 'kernel': 'rbf', 'gamma': 0.023768402866248775}
0.500 (+/-0.000) for {'C': 0.03981071705534969, 'kernel': 'rbf', 'gamma': 0.024210290467361794}
0.500 (+/-0.000) for {'C': 0.03981071705534969, 'kernel': 'rbf', 'gamma': 0.0246603933723434}
0.500 (+/-0.000) for {'C': 0.03981071705534969, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.617 (+/-0.238) for {'C': 0.3981071705534969, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.617 (+/-0.238) for {'C': 0.3981071705534969, 'kernel': 'rbf', 'gamma': 0.021281390459827135}
0.617 (+/-0.238) for {'C': 0.3981071705534969, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.617 (+/-0.238) for {'C': 0.3981071705534969, 'kernel': 'rbf', 'gamma': 0.022080047330189013}
0.617 (+/-0.238) for {'C': 0.3981071705534969, 'kernel': 'rbf', 'gamma': 0.022490546058357815}
0.617 (+/-0.238) for {'C': 0.3981071705534969, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.617 (+/-0.238) for {'C': 0.3981071705534969, 'kernel': 'rbf', 'gamma': 0.023334580622810044}
0.617 (+/-0.238) for {'C': 0.3981071705534969, 'kernel': 'rbf', 'gamma': 0.023768402866248775}
0.617 (+/-0.238) for {'C': 0.3981071705534969, 'kernel': 'rbf', 'gamma': 0.024210290467361794}
0.617 (+/-0.238) for {'C': 0.3981071705534969, 'kernel': 'rbf', 'gamma': 0.0246603933723434}
0.617 (+/-0.238) for {'C': 0.3981071705534969, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.617 (+/-0.238) for {'C': 3.981071705534969, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.617 (+/-0.238) for {'C': 3.981071705534969, 'kernel': 'rbf', 'gamma': 0.021281390459827135}
0.617 (+/-0.238) for {'C': 3.981071705534969, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.617 (+/-0.238) for {'C': 3.981071705534969, 'kernel': 'rbf', 'gamma': 0.022080047330189013}
0.617 (+/-0.238) for {'C': 3.981071705534969, 'kernel': 'rbf', 'gamma': 0.022490546058357815}
0.617 (+/-0.238) for {'C': 3.981071705534969, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.617 (+/-0.238) for {'C': 3.981071705534969, 'kernel': 'rbf', 'gamma': 0.023334580622810044}
0.617 (+/-0.238) for {'C': 3.981071705534969, 'kernel': 'rbf', 'gamma': 0.023768402866248775}
0.617 (+/-0.238) for {'C': 3.981071705534969, 'kernel': 'rbf', 'gamma': 0.024210290467361794}
0.617 (+/-0.238) for {'C': 3.981071705534969, 'kernel': 'rbf', 'gamma': 0.0246603933723434}
0.617 (+/-0.238) for {'C': 3.981071705534969, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.682 (+/-0.294) for {'C': 39.81071705534969, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.682 (+/-0.295) for {'C': 39.81071705534969, 'kernel': 'rbf', 'gamma': 0.021281390459827135}
0.682 (+/-0.295) for {'C': 39.81071705534969, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.682 (+/-0.295) for {'C': 39.81071705534969, 'kernel': 'rbf', 'gamma': 0.022080047330189013}
0.682 (+/-0.295) for {'C': 39.81071705534969, 'kernel': 'rbf', 'gamma': 0.022490546058357815}
0.682 (+/-0.295) for {'C': 39.81071705534969, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.682 (+/-0.295) for {'C': 39.81071705534969, 'kernel': 'rbf', 'gamma': 0.023334580622810044}
0.682 (+/-0.295) for {'C': 39.81071705534969, 'kernel': 'rbf', 'gamma': 0.023768402866248775}
0.665 (+/-0.314) for {'C': 39.81071705534969, 'kernel': 'rbf', 'gamma': 0.024210290467361794}
0.665 (+/-0.314) for {'C': 39.81071705534969, 'kernel': 'rbf', 'gamma': 0.0246603933723434}
0.666 (+/-0.315) for {'C': 39.81071705534969, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.658 (+/-0.314) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.658 (+/-0.314) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.021281390459827135}
0.658 (+/-0.314) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.658 (+/-0.315) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.022080047330189013}
0.658 (+/-0.315) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.022490546058357815}
0.658 (+/-0.315) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.658 (+/-0.314) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.023334580622810044}
0.658 (+/-0.314) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.023768402866248775}
0.658 (+/-0.314) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.024210290467361794}
0.658 (+/-0.314) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.0246603933723434}
0.658 (+/-0.314) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.636 (+/-0.237) for {'C': 3981.071705534969, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.637 (+/-0.237) for {'C': 3981.071705534969, 'kernel': 'rbf', 'gamma': 0.021281390459827135}
0.637 (+/-0.237) for {'C': 3981.071705534969, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.637 (+/-0.237) for {'C': 3981.071705534969, 'kernel': 'rbf', 'gamma': 0.022080047330189013}
0.637 (+/-0.237) for {'C': 3981.071705534969, 'kernel': 'rbf', 'gamma': 0.022490546058357815}
0.637 (+/-0.238) for {'C': 3981.071705534969, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.637 (+/-0.238) for {'C': 3981.071705534969, 'kernel': 'rbf', 'gamma': 0.023334580622810044}
0.637 (+/-0.238) for {'C': 3981.071705534969, 'kernel': 'rbf', 'gamma': 0.023768402866248775}
0.612 (+/-0.240) for {'C': 3981.071705534969, 'kernel': 'rbf', 'gamma': 0.024210290467361794}
0.612 (+/-0.240) for {'C': 3981.071705534969, 'kernel': 'rbf', 'gamma': 0.0246603933723434}
0.612 (+/-0.240) for {'C': 3981.071705534969, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.588 (+/-0.232) for {'C': 39810.71705534969, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.588 (+/-0.232) for {'C': 39810.71705534969, 'kernel': 'rbf', 'gamma': 0.021281390459827135}
0.588 (+/-0.232) for {'C': 39810.71705534969, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.588 (+/-0.232) for {'C': 39810.71705534969, 'kernel': 'rbf', 'gamma': 0.022080047330189013}
0.588 (+/-0.232) for {'C': 39810.71705534969, 'kernel': 'rbf', 'gamma': 0.022490546058357815}
0.588 (+/-0.233) for {'C': 39810.71705534969, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.588 (+/-0.233) for {'C': 39810.71705534969, 'kernel': 'rbf', 'gamma': 0.023334580622810044}
0.588 (+/-0.233) for {'C': 39810.71705534969, 'kernel': 'rbf', 'gamma': 0.023768402866248775}
0.587 (+/-0.233) for {'C': 39810.71705534969, 'kernel': 'rbf', 'gamma': 0.024210290467361794}
0.587 (+/-0.233) for {'C': 39810.71705534969, 'kernel': 'rbf', 'gamma': 0.0246603933723434}
0.587 (+/-0.234) for {'C': 39810.71705534969, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.587 (+/-0.235) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.587 (+/-0.235) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 0.021281390459827135}
0.587 (+/-0.235) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.587 (+/-0.235) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 0.022080047330189013}
0.587 (+/-0.235) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 0.022490546058357815}
0.587 (+/-0.235) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.587 (+/-0.235) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 0.023334580622810044}
0.587 (+/-0.235) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 0.023768402866248775}
0.586 (+/-0.235) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 0.024210290467361794}
0.587 (+/-0.235) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 0.0246603933723434}
0.587 (+/-0.235) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.587 (+/-0.235) for {'C': 3981071.7055349695, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.587 (+/-0.235) for {'C': 3981071.7055349695, 'kernel': 'rbf', 'gamma': 0.021281390459827135}
0.587 (+/-0.235) for {'C': 3981071.7055349695, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.587 (+/-0.235) for {'C': 3981071.7055349695, 'kernel': 'rbf', 'gamma': 0.022080047330189013}
0.587 (+/-0.235) for {'C': 3981071.7055349695, 'kernel': 'rbf', 'gamma': 0.022490546058357815}
0.587 (+/-0.235) for {'C': 3981071.7055349695, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.587 (+/-0.235) for {'C': 3981071.7055349695, 'kernel': 'rbf', 'gamma': 0.023334580622810044}
0.587 (+/-0.235) for {'C': 3981071.7055349695, 'kernel': 'rbf', 'gamma': 0.023768402866248775}
0.586 (+/-0.235) for {'C': 3981071.7055349695, 'kernel': 'rbf', 'gamma': 0.024210290467361794}
0.587 (+/-0.235) for {'C': 3981071.7055349695, 'kernel': 'rbf', 'gamma': 0.0246603933723434}
0.587 (+/-0.235) for {'C': 3981071.7055349695, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.663 (+/-0.316) for {'C': 10.0, 'kernel': 'linear'}
0.661 (+/-0.315) for {'C': 100.0, 'kernel': 'linear'}
0.637 (+/-0.239) for {'C': 1000.0, 'kernel': 'linear'}
0.585 (+/-0.236) for {'C': 10000.0, 'kernel': 'linear'}
0.611 (+/-0.242) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.17      0.17      0.17         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 39.81071705534969, 'kernel': 'rbf', 'gamma': 0.021281390459827135}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6822115384615385
第1轮loop中，tunemodel函数得到的最优参数是： ([{'C': array([  3.98107171,   6.30957344,  10.        ,  15.84893192,
        25.11886432,  39.81071706,  63.09573445, 100.        ,
       158.48931925, 251.18864315, 398.10717055]), 'kernel': ['rbf'], 'gamma': array([0.02089296, 0.02097008, 0.02104747, 0.02112516, 0.02120313,
       0.02128139, 0.02135994, 0.02143878, 0.02151791, 0.02159733,
       0.02167704])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}], {'C': 39.81071705534969, 'kernel': 'rbf', 'gamma': 0.021281390459827135}, 0.6822115384615385)
这是第1次迭代微调C和gamma。
第1次迭代，得到delta： [1.42108547e-14 1.62728607e-03]
执行tunemodel()函数前，使用的grid search parameter是：
 [{'C': array([  3.98107171,   6.30957344,  10.        ,  15.84893192,
        25.11886432,  39.81071706,  63.09573445, 100.        ,
       158.48931925, 251.18864315, 398.10717055]), 'kernel': ['rbf'], 'gamma': array([0.02089296, 0.02097008, 0.02104747, 0.02112516, 0.02120313,
       0.02128139, 0.02135994, 0.02143878, 0.02151791, 0.02159733,
       0.02167704])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.747 (+/-0.502) for {'C': 3.9810717055349696, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.747 (+/-0.502) for {'C': 3.9810717055349696, 'kernel': 'rbf', 'gamma': 0.02097007578544648}
0.747 (+/-0.502) for {'C': 3.9810717055349696, 'kernel': 'rbf', 'gamma': 0.02104747488655977}
0.747 (+/-0.502) for {'C': 3.9810717055349696, 'kernel': 'rbf', 'gamma': 0.021125159662408542}
0.747 (+/-0.502) for {'C': 3.9810717055349696, 'kernel': 'rbf', 'gamma': 0.021203131167398505}
0.747 (+/-0.502) for {'C': 3.9810717055349696, 'kernel': 'rbf', 'gamma': 0.02128139045982712}
0.747 (+/-0.502) for {'C': 3.9810717055349696, 'kernel': 'rbf', 'gamma': 0.02135993860189796}
0.747 (+/-0.502) for {'C': 3.9810717055349696, 'kernel': 'rbf', 'gamma': 0.021438776659735086}
0.747 (+/-0.502) for {'C': 3.9810717055349696, 'kernel': 'rbf', 'gamma': 0.02151790570339755}
0.747 (+/-0.502) for {'C': 3.9810717055349696, 'kernel': 'rbf', 'gamma': 0.021597326806893944}
0.747 (+/-0.502) for {'C': 3.9810717055349696, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.747 (+/-0.502) for {'C': 6.309573444801927, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.747 (+/-0.502) for {'C': 6.309573444801927, 'kernel': 'rbf', 'gamma': 0.02097007578544648}
0.747 (+/-0.502) for {'C': 6.309573444801927, 'kernel': 'rbf', 'gamma': 0.02104747488655977}
0.747 (+/-0.502) for {'C': 6.309573444801927, 'kernel': 'rbf', 'gamma': 0.021125159662408542}
0.747 (+/-0.502) for {'C': 6.309573444801927, 'kernel': 'rbf', 'gamma': 0.021203131167398505}
0.747 (+/-0.502) for {'C': 6.309573444801927, 'kernel': 'rbf', 'gamma': 0.02128139045982712}
0.747 (+/-0.502) for {'C': 6.309573444801927, 'kernel': 'rbf', 'gamma': 0.02135993860189796}
0.747 (+/-0.502) for {'C': 6.309573444801927, 'kernel': 'rbf', 'gamma': 0.021438776659735086}
0.747 (+/-0.502) for {'C': 6.309573444801927, 'kernel': 'rbf', 'gamma': 0.02151790570339755}
0.747 (+/-0.502) for {'C': 6.309573444801927, 'kernel': 'rbf', 'gamma': 0.021597326806893944}
0.747 (+/-0.502) for {'C': 6.309573444801927, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.747 (+/-0.502) for {'C': 9.99999999999999, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.747 (+/-0.502) for {'C': 9.99999999999999, 'kernel': 'rbf', 'gamma': 0.02097007578544648}
0.747 (+/-0.502) for {'C': 9.99999999999999, 'kernel': 'rbf', 'gamma': 0.02104747488655977}
0.747 (+/-0.502) for {'C': 9.99999999999999, 'kernel': 'rbf', 'gamma': 0.021125159662408542}
0.747 (+/-0.502) for {'C': 9.99999999999999, 'kernel': 'rbf', 'gamma': 0.021203131167398505}
0.747 (+/-0.502) for {'C': 9.99999999999999, 'kernel': 'rbf', 'gamma': 0.02128139045982712}
0.747 (+/-0.502) for {'C': 9.99999999999999, 'kernel': 'rbf', 'gamma': 0.02135993860189796}
0.747 (+/-0.502) for {'C': 9.99999999999999, 'kernel': 'rbf', 'gamma': 0.021438776659735086}
0.747 (+/-0.502) for {'C': 9.99999999999999, 'kernel': 'rbf', 'gamma': 0.02151790570339755}
0.747 (+/-0.502) for {'C': 9.99999999999999, 'kernel': 'rbf', 'gamma': 0.021597326806893944}
0.747 (+/-0.502) for {'C': 9.99999999999999, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.747 (+/-0.502) for {'C': 15.84893192461112, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.747 (+/-0.502) for {'C': 15.84893192461112, 'kernel': 'rbf', 'gamma': 0.02097007578544648}
0.747 (+/-0.502) for {'C': 15.84893192461112, 'kernel': 'rbf', 'gamma': 0.02104747488655977}
0.747 (+/-0.502) for {'C': 15.84893192461112, 'kernel': 'rbf', 'gamma': 0.021125159662408542}
0.747 (+/-0.502) for {'C': 15.84893192461112, 'kernel': 'rbf', 'gamma': 0.021203131167398505}
0.747 (+/-0.502) for {'C': 15.84893192461112, 'kernel': 'rbf', 'gamma': 0.02128139045982712}
0.747 (+/-0.502) for {'C': 15.84893192461112, 'kernel': 'rbf', 'gamma': 0.02135993860189796}
0.747 (+/-0.502) for {'C': 15.84893192461112, 'kernel': 'rbf', 'gamma': 0.021438776659735086}
0.747 (+/-0.502) for {'C': 15.84893192461112, 'kernel': 'rbf', 'gamma': 0.02151790570339755}
0.747 (+/-0.502) for {'C': 15.84893192461112, 'kernel': 'rbf', 'gamma': 0.021597326806893944}
0.747 (+/-0.502) for {'C': 15.84893192461112, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.747 (+/-0.502) for {'C': 25.11886431509578, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.747 (+/-0.502) for {'C': 25.11886431509578, 'kernel': 'rbf', 'gamma': 0.02097007578544648}
0.747 (+/-0.502) for {'C': 25.11886431509578, 'kernel': 'rbf', 'gamma': 0.02104747488655977}
0.747 (+/-0.502) for {'C': 25.11886431509578, 'kernel': 'rbf', 'gamma': 0.021125159662408542}
0.747 (+/-0.502) for {'C': 25.11886431509578, 'kernel': 'rbf', 'gamma': 0.021203131167398505}
0.747 (+/-0.502) for {'C': 25.11886431509578, 'kernel': 'rbf', 'gamma': 0.02128139045982712}
0.747 (+/-0.502) for {'C': 25.11886431509578, 'kernel': 'rbf', 'gamma': 0.02135993860189796}
0.747 (+/-0.502) for {'C': 25.11886431509578, 'kernel': 'rbf', 'gamma': 0.021438776659735086}
0.747 (+/-0.502) for {'C': 25.11886431509578, 'kernel': 'rbf', 'gamma': 0.02151790570339755}
0.747 (+/-0.502) for {'C': 25.11886431509578, 'kernel': 'rbf', 'gamma': 0.021597326806893944}
0.747 (+/-0.502) for {'C': 25.11886431509578, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.747 (+/-0.449) for {'C': 39.810717055349684, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.747 (+/-0.449) for {'C': 39.810717055349684, 'kernel': 'rbf', 'gamma': 0.02097007578544648}
0.747 (+/-0.449) for {'C': 39.810717055349684, 'kernel': 'rbf', 'gamma': 0.02104747488655977}
0.747 (+/-0.449) for {'C': 39.810717055349684, 'kernel': 'rbf', 'gamma': 0.021125159662408542}
0.747 (+/-0.449) for {'C': 39.810717055349684, 'kernel': 'rbf', 'gamma': 0.021203131167398505}
0.747 (+/-0.449) for {'C': 39.810717055349684, 'kernel': 'rbf', 'gamma': 0.02128139045982712}
0.747 (+/-0.449) for {'C': 39.810717055349684, 'kernel': 'rbf', 'gamma': 0.02135993860189796}
0.747 (+/-0.449) for {'C': 39.810717055349684, 'kernel': 'rbf', 'gamma': 0.021438776659735086}
0.747 (+/-0.449) for {'C': 39.810717055349684, 'kernel': 'rbf', 'gamma': 0.02151790570339755}
0.747 (+/-0.449) for {'C': 39.810717055349684, 'kernel': 'rbf', 'gamma': 0.021597326806893944}
0.747 (+/-0.449) for {'C': 39.810717055349684, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.748 (+/-0.449) for {'C': 63.09573444801924, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.748 (+/-0.449) for {'C': 63.09573444801924, 'kernel': 'rbf', 'gamma': 0.02097007578544648}
0.748 (+/-0.449) for {'C': 63.09573444801924, 'kernel': 'rbf', 'gamma': 0.02104747488655977}
0.748 (+/-0.449) for {'C': 63.09573444801924, 'kernel': 'rbf', 'gamma': 0.021125159662408542}
0.748 (+/-0.449) for {'C': 63.09573444801924, 'kernel': 'rbf', 'gamma': 0.021203131167398505}
0.748 (+/-0.449) for {'C': 63.09573444801924, 'kernel': 'rbf', 'gamma': 0.02128139045982712}
0.748 (+/-0.449) for {'C': 63.09573444801924, 'kernel': 'rbf', 'gamma': 0.02135993860189796}
0.748 (+/-0.449) for {'C': 63.09573444801924, 'kernel': 'rbf', 'gamma': 0.021438776659735086}
0.748 (+/-0.449) for {'C': 63.09573444801924, 'kernel': 'rbf', 'gamma': 0.02151790570339755}
0.748 (+/-0.449) for {'C': 63.09573444801924, 'kernel': 'rbf', 'gamma': 0.021597326806893944}
0.748 (+/-0.449) for {'C': 63.09573444801924, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.698 (+/-0.383) for {'C': 99.99999999999991, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.698 (+/-0.383) for {'C': 99.99999999999991, 'kernel': 'rbf', 'gamma': 0.02097007578544648}
0.698 (+/-0.383) for {'C': 99.99999999999991, 'kernel': 'rbf', 'gamma': 0.02104747488655977}
0.698 (+/-0.383) for {'C': 99.99999999999991, 'kernel': 'rbf', 'gamma': 0.021125159662408542}
0.698 (+/-0.383) for {'C': 99.99999999999991, 'kernel': 'rbf', 'gamma': 0.021203131167398505}
0.698 (+/-0.383) for {'C': 99.99999999999991, 'kernel': 'rbf', 'gamma': 0.02128139045982712}
0.698 (+/-0.383) for {'C': 99.99999999999991, 'kernel': 'rbf', 'gamma': 0.02135993860189796}
0.698 (+/-0.383) for {'C': 99.99999999999991, 'kernel': 'rbf', 'gamma': 0.021438776659735086}
0.698 (+/-0.383) for {'C': 99.99999999999991, 'kernel': 'rbf', 'gamma': 0.02151790570339755}
0.698 (+/-0.383) for {'C': 99.99999999999991, 'kernel': 'rbf', 'gamma': 0.021597326806893944}
0.698 (+/-0.383) for {'C': 99.99999999999991, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.698 (+/-0.383) for {'C': 158.4893192461112, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.698 (+/-0.383) for {'C': 158.4893192461112, 'kernel': 'rbf', 'gamma': 0.02097007578544648}
0.698 (+/-0.383) for {'C': 158.4893192461112, 'kernel': 'rbf', 'gamma': 0.02104747488655977}
0.698 (+/-0.383) for {'C': 158.4893192461112, 'kernel': 'rbf', 'gamma': 0.021125159662408542}
0.698 (+/-0.383) for {'C': 158.4893192461112, 'kernel': 'rbf', 'gamma': 0.021203131167398505}
0.698 (+/-0.383) for {'C': 158.4893192461112, 'kernel': 'rbf', 'gamma': 0.02128139045982712}
0.698 (+/-0.383) for {'C': 158.4893192461112, 'kernel': 'rbf', 'gamma': 0.02135993860189796}
0.698 (+/-0.383) for {'C': 158.4893192461112, 'kernel': 'rbf', 'gamma': 0.021438776659735086}
0.698 (+/-0.383) for {'C': 158.4893192461112, 'kernel': 'rbf', 'gamma': 0.02151790570339755}
0.698 (+/-0.383) for {'C': 158.4893192461112, 'kernel': 'rbf', 'gamma': 0.021597326806893944}
0.698 (+/-0.383) for {'C': 158.4893192461112, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.697 (+/-0.375) for {'C': 251.1886431509577, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.697 (+/-0.375) for {'C': 251.1886431509577, 'kernel': 'rbf', 'gamma': 0.02097007578544648}
0.697 (+/-0.375) for {'C': 251.1886431509577, 'kernel': 'rbf', 'gamma': 0.02104747488655977}
0.697 (+/-0.375) for {'C': 251.1886431509577, 'kernel': 'rbf', 'gamma': 0.021125159662408542}
0.697 (+/-0.375) for {'C': 251.1886431509577, 'kernel': 'rbf', 'gamma': 0.021203131167398505}
0.697 (+/-0.375) for {'C': 251.1886431509577, 'kernel': 'rbf', 'gamma': 0.02128139045982712}
0.697 (+/-0.375) for {'C': 251.1886431509577, 'kernel': 'rbf', 'gamma': 0.02135993860189796}
0.697 (+/-0.375) for {'C': 251.1886431509577, 'kernel': 'rbf', 'gamma': 0.021438776659735086}
0.697 (+/-0.375) for {'C': 251.1886431509577, 'kernel': 'rbf', 'gamma': 0.02151790570339755}
0.697 (+/-0.375) for {'C': 251.1886431509577, 'kernel': 'rbf', 'gamma': 0.021597326806893944}
0.697 (+/-0.375) for {'C': 251.1886431509577, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.722 (+/-0.417) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.722 (+/-0.417) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.02097007578544648}
0.722 (+/-0.417) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.02104747488655977}
0.722 (+/-0.417) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.021125159662408542}
0.722 (+/-0.417) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.021203131167398505}
0.722 (+/-0.417) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.02128139045982712}
0.722 (+/-0.417) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.02135993860189796}
0.722 (+/-0.417) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.021438776659735086}
0.722 (+/-0.417) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.02151790570339755}
0.722 (+/-0.417) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.021597326806893944}
0.722 (+/-0.417) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.706 (+/-0.383) for {'C': 10.0, 'kernel': 'linear'}
0.643 (+/-0.320) for {'C': 100.0, 'kernel': 'linear'}
0.637 (+/-0.307) for {'C': 1000.0, 'kernel': 'linear'}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'linear'}
0.620 (+/-0.321) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.20      0.17      0.18         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.99      0.99       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.617 (+/-0.238) for {'C': 3.9810717055349696, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.617 (+/-0.238) for {'C': 3.9810717055349696, 'kernel': 'rbf', 'gamma': 0.02097007578544648}
0.617 (+/-0.238) for {'C': 3.9810717055349696, 'kernel': 'rbf', 'gamma': 0.02104747488655977}
0.617 (+/-0.238) for {'C': 3.9810717055349696, 'kernel': 'rbf', 'gamma': 0.021125159662408542}
0.617 (+/-0.238) for {'C': 3.9810717055349696, 'kernel': 'rbf', 'gamma': 0.021203131167398505}
0.617 (+/-0.238) for {'C': 3.9810717055349696, 'kernel': 'rbf', 'gamma': 0.02128139045982712}
0.617 (+/-0.238) for {'C': 3.9810717055349696, 'kernel': 'rbf', 'gamma': 0.02135993860189796}
0.617 (+/-0.238) for {'C': 3.9810717055349696, 'kernel': 'rbf', 'gamma': 0.021438776659735086}
0.617 (+/-0.238) for {'C': 3.9810717055349696, 'kernel': 'rbf', 'gamma': 0.02151790570339755}
0.617 (+/-0.238) for {'C': 3.9810717055349696, 'kernel': 'rbf', 'gamma': 0.021597326806893944}
0.617 (+/-0.238) for {'C': 3.9810717055349696, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.617 (+/-0.238) for {'C': 6.309573444801927, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.617 (+/-0.238) for {'C': 6.309573444801927, 'kernel': 'rbf', 'gamma': 0.02097007578544648}
0.617 (+/-0.238) for {'C': 6.309573444801927, 'kernel': 'rbf', 'gamma': 0.02104747488655977}
0.617 (+/-0.238) for {'C': 6.309573444801927, 'kernel': 'rbf', 'gamma': 0.021125159662408542}
0.617 (+/-0.238) for {'C': 6.309573444801927, 'kernel': 'rbf', 'gamma': 0.021203131167398505}
0.617 (+/-0.238) for {'C': 6.309573444801927, 'kernel': 'rbf', 'gamma': 0.02128139045982712}
0.617 (+/-0.238) for {'C': 6.309573444801927, 'kernel': 'rbf', 'gamma': 0.02135993860189796}
0.617 (+/-0.238) for {'C': 6.309573444801927, 'kernel': 'rbf', 'gamma': 0.021438776659735086}
0.617 (+/-0.238) for {'C': 6.309573444801927, 'kernel': 'rbf', 'gamma': 0.02151790570339755}
0.617 (+/-0.238) for {'C': 6.309573444801927, 'kernel': 'rbf', 'gamma': 0.021597326806893944}
0.617 (+/-0.238) for {'C': 6.309573444801927, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.617 (+/-0.238) for {'C': 9.99999999999999, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.617 (+/-0.238) for {'C': 9.99999999999999, 'kernel': 'rbf', 'gamma': 0.02097007578544648}
0.617 (+/-0.238) for {'C': 9.99999999999999, 'kernel': 'rbf', 'gamma': 0.02104747488655977}
0.617 (+/-0.238) for {'C': 9.99999999999999, 'kernel': 'rbf', 'gamma': 0.021125159662408542}
0.617 (+/-0.238) for {'C': 9.99999999999999, 'kernel': 'rbf', 'gamma': 0.021203131167398505}
0.617 (+/-0.238) for {'C': 9.99999999999999, 'kernel': 'rbf', 'gamma': 0.02128139045982712}
0.617 (+/-0.238) for {'C': 9.99999999999999, 'kernel': 'rbf', 'gamma': 0.02135993860189796}
0.617 (+/-0.238) for {'C': 9.99999999999999, 'kernel': 'rbf', 'gamma': 0.021438776659735086}
0.617 (+/-0.238) for {'C': 9.99999999999999, 'kernel': 'rbf', 'gamma': 0.02151790570339755}
0.617 (+/-0.238) for {'C': 9.99999999999999, 'kernel': 'rbf', 'gamma': 0.021597326806893944}
0.617 (+/-0.238) for {'C': 9.99999999999999, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.617 (+/-0.238) for {'C': 15.84893192461112, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.617 (+/-0.238) for {'C': 15.84893192461112, 'kernel': 'rbf', 'gamma': 0.02097007578544648}
0.617 (+/-0.238) for {'C': 15.84893192461112, 'kernel': 'rbf', 'gamma': 0.02104747488655977}
0.617 (+/-0.238) for {'C': 15.84893192461112, 'kernel': 'rbf', 'gamma': 0.021125159662408542}
0.617 (+/-0.238) for {'C': 15.84893192461112, 'kernel': 'rbf', 'gamma': 0.021203131167398505}
0.617 (+/-0.238) for {'C': 15.84893192461112, 'kernel': 'rbf', 'gamma': 0.02128139045982712}
0.617 (+/-0.238) for {'C': 15.84893192461112, 'kernel': 'rbf', 'gamma': 0.02135993860189796}
0.617 (+/-0.238) for {'C': 15.84893192461112, 'kernel': 'rbf', 'gamma': 0.021438776659735086}
0.617 (+/-0.238) for {'C': 15.84893192461112, 'kernel': 'rbf', 'gamma': 0.02151790570339755}
0.617 (+/-0.238) for {'C': 15.84893192461112, 'kernel': 'rbf', 'gamma': 0.021597326806893944}
0.617 (+/-0.238) for {'C': 15.84893192461112, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.617 (+/-0.238) for {'C': 25.11886431509578, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.617 (+/-0.238) for {'C': 25.11886431509578, 'kernel': 'rbf', 'gamma': 0.02097007578544648}
0.617 (+/-0.238) for {'C': 25.11886431509578, 'kernel': 'rbf', 'gamma': 0.02104747488655977}
0.617 (+/-0.238) for {'C': 25.11886431509578, 'kernel': 'rbf', 'gamma': 0.021125159662408542}
0.617 (+/-0.238) for {'C': 25.11886431509578, 'kernel': 'rbf', 'gamma': 0.021203131167398505}
0.617 (+/-0.238) for {'C': 25.11886431509578, 'kernel': 'rbf', 'gamma': 0.02128139045982712}
0.617 (+/-0.238) for {'C': 25.11886431509578, 'kernel': 'rbf', 'gamma': 0.02135993860189796}
0.617 (+/-0.238) for {'C': 25.11886431509578, 'kernel': 'rbf', 'gamma': 0.021438776659735086}
0.617 (+/-0.238) for {'C': 25.11886431509578, 'kernel': 'rbf', 'gamma': 0.02151790570339755}
0.617 (+/-0.238) for {'C': 25.11886431509578, 'kernel': 'rbf', 'gamma': 0.021597326806893944}
0.617 (+/-0.238) for {'C': 25.11886431509578, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.641 (+/-0.236) for {'C': 39.810717055349684, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.641 (+/-0.236) for {'C': 39.810717055349684, 'kernel': 'rbf', 'gamma': 0.02097007578544648}
0.641 (+/-0.236) for {'C': 39.810717055349684, 'kernel': 'rbf', 'gamma': 0.02104747488655977}
0.641 (+/-0.236) for {'C': 39.810717055349684, 'kernel': 'rbf', 'gamma': 0.021125159662408542}
0.641 (+/-0.236) for {'C': 39.810717055349684, 'kernel': 'rbf', 'gamma': 0.021203131167398505}
0.641 (+/-0.236) for {'C': 39.810717055349684, 'kernel': 'rbf', 'gamma': 0.02128139045982712}
0.641 (+/-0.236) for {'C': 39.810717055349684, 'kernel': 'rbf', 'gamma': 0.02135993860189796}
0.641 (+/-0.236) for {'C': 39.810717055349684, 'kernel': 'rbf', 'gamma': 0.021438776659735086}
0.641 (+/-0.236) for {'C': 39.810717055349684, 'kernel': 'rbf', 'gamma': 0.02151790570339755}
0.641 (+/-0.236) for {'C': 39.810717055349684, 'kernel': 'rbf', 'gamma': 0.021597326806893944}
0.641 (+/-0.236) for {'C': 39.810717055349684, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.666 (+/-0.316) for {'C': 63.09573444801924, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.666 (+/-0.316) for {'C': 63.09573444801924, 'kernel': 'rbf', 'gamma': 0.02097007578544648}
0.666 (+/-0.316) for {'C': 63.09573444801924, 'kernel': 'rbf', 'gamma': 0.02104747488655977}
0.666 (+/-0.316) for {'C': 63.09573444801924, 'kernel': 'rbf', 'gamma': 0.021125159662408542}
0.666 (+/-0.316) for {'C': 63.09573444801924, 'kernel': 'rbf', 'gamma': 0.021203131167398505}
0.666 (+/-0.316) for {'C': 63.09573444801924, 'kernel': 'rbf', 'gamma': 0.02128139045982712}
0.666 (+/-0.316) for {'C': 63.09573444801924, 'kernel': 'rbf', 'gamma': 0.02135993860189796}
0.666 (+/-0.316) for {'C': 63.09573444801924, 'kernel': 'rbf', 'gamma': 0.021438776659735086}
0.666 (+/-0.316) for {'C': 63.09573444801924, 'kernel': 'rbf', 'gamma': 0.02151790570339755}
0.666 (+/-0.316) for {'C': 63.09573444801924, 'kernel': 'rbf', 'gamma': 0.021597326806893944}
0.666 (+/-0.316) for {'C': 63.09573444801924, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.666 (+/-0.315) for {'C': 99.99999999999991, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.666 (+/-0.315) for {'C': 99.99999999999991, 'kernel': 'rbf', 'gamma': 0.02097007578544648}
0.666 (+/-0.315) for {'C': 99.99999999999991, 'kernel': 'rbf', 'gamma': 0.02104747488655977}
0.666 (+/-0.315) for {'C': 99.99999999999991, 'kernel': 'rbf', 'gamma': 0.021125159662408542}
0.666 (+/-0.315) for {'C': 99.99999999999991, 'kernel': 'rbf', 'gamma': 0.021203131167398505}
0.666 (+/-0.315) for {'C': 99.99999999999991, 'kernel': 'rbf', 'gamma': 0.02128139045982712}
0.666 (+/-0.315) for {'C': 99.99999999999991, 'kernel': 'rbf', 'gamma': 0.02135993860189796}
0.666 (+/-0.315) for {'C': 99.99999999999991, 'kernel': 'rbf', 'gamma': 0.021438776659735086}
0.666 (+/-0.315) for {'C': 99.99999999999991, 'kernel': 'rbf', 'gamma': 0.02151790570339755}
0.666 (+/-0.315) for {'C': 99.99999999999991, 'kernel': 'rbf', 'gamma': 0.021597326806893944}
0.666 (+/-0.315) for {'C': 99.99999999999991, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.666 (+/-0.315) for {'C': 158.4893192461112, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.666 (+/-0.315) for {'C': 158.4893192461112, 'kernel': 'rbf', 'gamma': 0.02097007578544648}
0.666 (+/-0.315) for {'C': 158.4893192461112, 'kernel': 'rbf', 'gamma': 0.02104747488655977}
0.666 (+/-0.315) for {'C': 158.4893192461112, 'kernel': 'rbf', 'gamma': 0.021125159662408542}
0.666 (+/-0.315) for {'C': 158.4893192461112, 'kernel': 'rbf', 'gamma': 0.021203131167398505}
0.666 (+/-0.315) for {'C': 158.4893192461112, 'kernel': 'rbf', 'gamma': 0.02128139045982712}
0.666 (+/-0.315) for {'C': 158.4893192461112, 'kernel': 'rbf', 'gamma': 0.02135993860189796}
0.666 (+/-0.315) for {'C': 158.4893192461112, 'kernel': 'rbf', 'gamma': 0.021438776659735086}
0.666 (+/-0.315) for {'C': 158.4893192461112, 'kernel': 'rbf', 'gamma': 0.02151790570339755}
0.666 (+/-0.315) for {'C': 158.4893192461112, 'kernel': 'rbf', 'gamma': 0.021597326806893944}
0.666 (+/-0.315) for {'C': 158.4893192461112, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.641 (+/-0.236) for {'C': 251.1886431509577, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.641 (+/-0.236) for {'C': 251.1886431509577, 'kernel': 'rbf', 'gamma': 0.02097007578544648}
0.641 (+/-0.236) for {'C': 251.1886431509577, 'kernel': 'rbf', 'gamma': 0.02104747488655977}
0.641 (+/-0.236) for {'C': 251.1886431509577, 'kernel': 'rbf', 'gamma': 0.021125159662408542}
0.641 (+/-0.236) for {'C': 251.1886431509577, 'kernel': 'rbf', 'gamma': 0.021203131167398505}
0.641 (+/-0.236) for {'C': 251.1886431509577, 'kernel': 'rbf', 'gamma': 0.02128139045982712}
0.641 (+/-0.236) for {'C': 251.1886431509577, 'kernel': 'rbf', 'gamma': 0.02135993860189796}
0.641 (+/-0.236) for {'C': 251.1886431509577, 'kernel': 'rbf', 'gamma': 0.021438776659735086}
0.641 (+/-0.236) for {'C': 251.1886431509577, 'kernel': 'rbf', 'gamma': 0.02151790570339755}
0.641 (+/-0.236) for {'C': 251.1886431509577, 'kernel': 'rbf', 'gamma': 0.021597326806893944}
0.641 (+/-0.236) for {'C': 251.1886431509577, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.641 (+/-0.237) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.641 (+/-0.237) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.02097007578544648}
0.641 (+/-0.237) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.02104747488655977}
0.641 (+/-0.237) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.021125159662408542}
0.641 (+/-0.237) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.021203131167398505}
0.641 (+/-0.237) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.02128139045982712}
0.641 (+/-0.237) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.02135993860189796}
0.641 (+/-0.237) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.021438776659735086}
0.641 (+/-0.237) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.02151790570339755}
0.641 (+/-0.237) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.021597326806893944}
0.641 (+/-0.237) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.666 (+/-0.315) for {'C': 10.0, 'kernel': 'linear'}
0.637 (+/-0.237) for {'C': 100.0, 'kernel': 'linear'}
0.637 (+/-0.239) for {'C': 1000.0, 'kernel': 'linear'}
0.585 (+/-0.236) for {'C': 10000.0, 'kernel': 'linear'}
0.611 (+/-0.242) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.20      0.17      0.18         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.99      0.99       629

本轮grid search结果，得到最好的参数选择是： {'C': 63.09573444801924, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6663461538461538
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.747 (+/-0.502) for {'C': 3.9810717055349696, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.747 (+/-0.502) for {'C': 3.9810717055349696, 'kernel': 'rbf', 'gamma': 0.02097007578544648}
0.747 (+/-0.502) for {'C': 3.9810717055349696, 'kernel': 'rbf', 'gamma': 0.02104747488655977}
0.747 (+/-0.502) for {'C': 3.9810717055349696, 'kernel': 'rbf', 'gamma': 0.021125159662408542}
0.747 (+/-0.502) for {'C': 3.9810717055349696, 'kernel': 'rbf', 'gamma': 0.021203131167398505}
0.747 (+/-0.502) for {'C': 3.9810717055349696, 'kernel': 'rbf', 'gamma': 0.02128139045982712}
0.747 (+/-0.502) for {'C': 3.9810717055349696, 'kernel': 'rbf', 'gamma': 0.02135993860189796}
0.747 (+/-0.502) for {'C': 3.9810717055349696, 'kernel': 'rbf', 'gamma': 0.021438776659735086}
0.747 (+/-0.502) for {'C': 3.9810717055349696, 'kernel': 'rbf', 'gamma': 0.02151790570339755}
0.747 (+/-0.502) for {'C': 3.9810717055349696, 'kernel': 'rbf', 'gamma': 0.021597326806893944}
0.747 (+/-0.502) for {'C': 3.9810717055349696, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.747 (+/-0.502) for {'C': 6.309573444801927, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.747 (+/-0.502) for {'C': 6.309573444801927, 'kernel': 'rbf', 'gamma': 0.02097007578544648}
0.747 (+/-0.502) for {'C': 6.309573444801927, 'kernel': 'rbf', 'gamma': 0.02104747488655977}
0.747 (+/-0.502) for {'C': 6.309573444801927, 'kernel': 'rbf', 'gamma': 0.021125159662408542}
0.747 (+/-0.502) for {'C': 6.309573444801927, 'kernel': 'rbf', 'gamma': 0.021203131167398505}
0.747 (+/-0.502) for {'C': 6.309573444801927, 'kernel': 'rbf', 'gamma': 0.02128139045982712}
0.747 (+/-0.502) for {'C': 6.309573444801927, 'kernel': 'rbf', 'gamma': 0.02135993860189796}
0.747 (+/-0.502) for {'C': 6.309573444801927, 'kernel': 'rbf', 'gamma': 0.021438776659735086}
0.747 (+/-0.502) for {'C': 6.309573444801927, 'kernel': 'rbf', 'gamma': 0.02151790570339755}
0.747 (+/-0.502) for {'C': 6.309573444801927, 'kernel': 'rbf', 'gamma': 0.021597326806893944}
0.747 (+/-0.502) for {'C': 6.309573444801927, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.797 (+/-0.492) for {'C': 9.99999999999999, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.797 (+/-0.492) for {'C': 9.99999999999999, 'kernel': 'rbf', 'gamma': 0.02097007578544648}
0.797 (+/-0.492) for {'C': 9.99999999999999, 'kernel': 'rbf', 'gamma': 0.02104747488655977}
0.797 (+/-0.492) for {'C': 9.99999999999999, 'kernel': 'rbf', 'gamma': 0.021125159662408542}
0.797 (+/-0.492) for {'C': 9.99999999999999, 'kernel': 'rbf', 'gamma': 0.021203131167398505}
0.797 (+/-0.492) for {'C': 9.99999999999999, 'kernel': 'rbf', 'gamma': 0.02128139045982712}
0.797 (+/-0.492) for {'C': 9.99999999999999, 'kernel': 'rbf', 'gamma': 0.02135993860189796}
0.797 (+/-0.492) for {'C': 9.99999999999999, 'kernel': 'rbf', 'gamma': 0.021438776659735086}
0.797 (+/-0.492) for {'C': 9.99999999999999, 'kernel': 'rbf', 'gamma': 0.02151790570339755}
0.797 (+/-0.492) for {'C': 9.99999999999999, 'kernel': 'rbf', 'gamma': 0.021597326806893944}
0.797 (+/-0.492) for {'C': 9.99999999999999, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.748 (+/-0.449) for {'C': 15.84893192461112, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.748 (+/-0.449) for {'C': 15.84893192461112, 'kernel': 'rbf', 'gamma': 0.02097007578544648}
0.748 (+/-0.449) for {'C': 15.84893192461112, 'kernel': 'rbf', 'gamma': 0.02104747488655977}
0.748 (+/-0.449) for {'C': 15.84893192461112, 'kernel': 'rbf', 'gamma': 0.021125159662408542}
0.748 (+/-0.449) for {'C': 15.84893192461112, 'kernel': 'rbf', 'gamma': 0.021203131167398505}
0.748 (+/-0.449) for {'C': 15.84893192461112, 'kernel': 'rbf', 'gamma': 0.02128139045982712}
0.748 (+/-0.449) for {'C': 15.84893192461112, 'kernel': 'rbf', 'gamma': 0.02135993860189796}
0.748 (+/-0.449) for {'C': 15.84893192461112, 'kernel': 'rbf', 'gamma': 0.021438776659735086}
0.748 (+/-0.449) for {'C': 15.84893192461112, 'kernel': 'rbf', 'gamma': 0.02151790570339755}
0.748 (+/-0.449) for {'C': 15.84893192461112, 'kernel': 'rbf', 'gamma': 0.021597326806893944}
0.748 (+/-0.449) for {'C': 15.84893192461112, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.748 (+/-0.449) for {'C': 25.11886431509578, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.748 (+/-0.449) for {'C': 25.11886431509578, 'kernel': 'rbf', 'gamma': 0.02097007578544648}
0.748 (+/-0.449) for {'C': 25.11886431509578, 'kernel': 'rbf', 'gamma': 0.02104747488655977}
0.748 (+/-0.449) for {'C': 25.11886431509578, 'kernel': 'rbf', 'gamma': 0.021125159662408542}
0.748 (+/-0.449) for {'C': 25.11886431509578, 'kernel': 'rbf', 'gamma': 0.021203131167398505}
0.748 (+/-0.449) for {'C': 25.11886431509578, 'kernel': 'rbf', 'gamma': 0.02128139045982712}
0.748 (+/-0.449) for {'C': 25.11886431509578, 'kernel': 'rbf', 'gamma': 0.02135993860189796}
0.748 (+/-0.449) for {'C': 25.11886431509578, 'kernel': 'rbf', 'gamma': 0.021438776659735086}
0.748 (+/-0.449) for {'C': 25.11886431509578, 'kernel': 'rbf', 'gamma': 0.02151790570339755}
0.748 (+/-0.449) for {'C': 25.11886431509578, 'kernel': 'rbf', 'gamma': 0.021597326806893944}
0.748 (+/-0.449) for {'C': 25.11886431509578, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.727 (+/-0.397) for {'C': 39.810717055349684, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.727 (+/-0.397) for {'C': 39.810717055349684, 'kernel': 'rbf', 'gamma': 0.02097007578544648}
0.727 (+/-0.397) for {'C': 39.810717055349684, 'kernel': 'rbf', 'gamma': 0.02104747488655977}
0.727 (+/-0.397) for {'C': 39.810717055349684, 'kernel': 'rbf', 'gamma': 0.021125159662408542}
0.735 (+/-0.402) for {'C': 39.810717055349684, 'kernel': 'rbf', 'gamma': 0.021203131167398505}
0.735 (+/-0.402) for {'C': 39.810717055349684, 'kernel': 'rbf', 'gamma': 0.02128139045982712}
0.735 (+/-0.402) for {'C': 39.810717055349684, 'kernel': 'rbf', 'gamma': 0.02135993860189796}
0.735 (+/-0.402) for {'C': 39.810717055349684, 'kernel': 'rbf', 'gamma': 0.021438776659735086}
0.735 (+/-0.402) for {'C': 39.810717055349684, 'kernel': 'rbf', 'gamma': 0.02151790570339755}
0.735 (+/-0.402) for {'C': 39.810717055349684, 'kernel': 'rbf', 'gamma': 0.021597326806893944}
0.735 (+/-0.402) for {'C': 39.810717055349684, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.673 (+/-0.330) for {'C': 63.09573444801924, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.673 (+/-0.330) for {'C': 63.09573444801924, 'kernel': 'rbf', 'gamma': 0.02097007578544648}
0.673 (+/-0.330) for {'C': 63.09573444801924, 'kernel': 'rbf', 'gamma': 0.02104747488655977}
0.673 (+/-0.330) for {'C': 63.09573444801924, 'kernel': 'rbf', 'gamma': 0.021125159662408542}
0.673 (+/-0.330) for {'C': 63.09573444801924, 'kernel': 'rbf', 'gamma': 0.021203131167398505}
0.673 (+/-0.330) for {'C': 63.09573444801924, 'kernel': 'rbf', 'gamma': 0.02128139045982712}
0.673 (+/-0.330) for {'C': 63.09573444801924, 'kernel': 'rbf', 'gamma': 0.02135993860189796}
0.673 (+/-0.330) for {'C': 63.09573444801924, 'kernel': 'rbf', 'gamma': 0.021438776659735086}
0.673 (+/-0.330) for {'C': 63.09573444801924, 'kernel': 'rbf', 'gamma': 0.02151790570339755}
0.673 (+/-0.330) for {'C': 63.09573444801924, 'kernel': 'rbf', 'gamma': 0.021597326806893944}
0.673 (+/-0.330) for {'C': 63.09573444801924, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.664 (+/-0.317) for {'C': 99.99999999999991, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.664 (+/-0.317) for {'C': 99.99999999999991, 'kernel': 'rbf', 'gamma': 0.02097007578544648}
0.664 (+/-0.317) for {'C': 99.99999999999991, 'kernel': 'rbf', 'gamma': 0.02104747488655977}
0.664 (+/-0.317) for {'C': 99.99999999999991, 'kernel': 'rbf', 'gamma': 0.021125159662408542}
0.664 (+/-0.317) for {'C': 99.99999999999991, 'kernel': 'rbf', 'gamma': 0.021203131167398505}
0.664 (+/-0.317) for {'C': 99.99999999999991, 'kernel': 'rbf', 'gamma': 0.02128139045982712}
0.664 (+/-0.317) for {'C': 99.99999999999991, 'kernel': 'rbf', 'gamma': 0.02135993860189796}
0.664 (+/-0.317) for {'C': 99.99999999999991, 'kernel': 'rbf', 'gamma': 0.021438776659735086}
0.664 (+/-0.317) for {'C': 99.99999999999991, 'kernel': 'rbf', 'gamma': 0.02151790570339755}
0.664 (+/-0.317) for {'C': 99.99999999999991, 'kernel': 'rbf', 'gamma': 0.021597326806893944}
0.664 (+/-0.317) for {'C': 99.99999999999991, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.672 (+/-0.371) for {'C': 158.4893192461112, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.672 (+/-0.371) for {'C': 158.4893192461112, 'kernel': 'rbf', 'gamma': 0.02097007578544648}
0.672 (+/-0.371) for {'C': 158.4893192461112, 'kernel': 'rbf', 'gamma': 0.02104747488655977}
0.672 (+/-0.371) for {'C': 158.4893192461112, 'kernel': 'rbf', 'gamma': 0.021125159662408542}
0.672 (+/-0.371) for {'C': 158.4893192461112, 'kernel': 'rbf', 'gamma': 0.021203131167398505}
0.672 (+/-0.371) for {'C': 158.4893192461112, 'kernel': 'rbf', 'gamma': 0.02128139045982712}
0.672 (+/-0.371) for {'C': 158.4893192461112, 'kernel': 'rbf', 'gamma': 0.02135993860189796}
0.672 (+/-0.371) for {'C': 158.4893192461112, 'kernel': 'rbf', 'gamma': 0.021438776659735086}
0.672 (+/-0.371) for {'C': 158.4893192461112, 'kernel': 'rbf', 'gamma': 0.02151790570339755}
0.672 (+/-0.371) for {'C': 158.4893192461112, 'kernel': 'rbf', 'gamma': 0.021597326806893944}
0.672 (+/-0.371) for {'C': 158.4893192461112, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.669 (+/-0.379) for {'C': 251.1886431509577, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.669 (+/-0.379) for {'C': 251.1886431509577, 'kernel': 'rbf', 'gamma': 0.02097007578544648}
0.669 (+/-0.379) for {'C': 251.1886431509577, 'kernel': 'rbf', 'gamma': 0.02104747488655977}
0.669 (+/-0.379) for {'C': 251.1886431509577, 'kernel': 'rbf', 'gamma': 0.021125159662408542}
0.669 (+/-0.379) for {'C': 251.1886431509577, 'kernel': 'rbf', 'gamma': 0.021203131167398505}
0.666 (+/-0.380) for {'C': 251.1886431509577, 'kernel': 'rbf', 'gamma': 0.02128139045982712}
0.666 (+/-0.380) for {'C': 251.1886431509577, 'kernel': 'rbf', 'gamma': 0.02135993860189796}
0.666 (+/-0.381) for {'C': 251.1886431509577, 'kernel': 'rbf', 'gamma': 0.021438776659735086}
0.666 (+/-0.381) for {'C': 251.1886431509577, 'kernel': 'rbf', 'gamma': 0.02151790570339755}
0.666 (+/-0.381) for {'C': 251.1886431509577, 'kernel': 'rbf', 'gamma': 0.021597326806893944}
0.666 (+/-0.381) for {'C': 251.1886431509577, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.658 (+/-0.388) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.658 (+/-0.388) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.02097007578544648}
0.658 (+/-0.388) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.02104747488655977}
0.658 (+/-0.388) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.021125159662408542}
0.658 (+/-0.388) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.021203131167398505}
0.658 (+/-0.388) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.02128139045982712}
0.658 (+/-0.388) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.02135993860189796}
0.658 (+/-0.388) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.021438776659735086}
0.658 (+/-0.388) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.02151790570339755}
0.658 (+/-0.388) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.021597326806893944}
0.658 (+/-0.388) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.674 (+/-0.375) for {'C': 10.0, 'kernel': 'linear'}
0.631 (+/-0.312) for {'C': 100.0, 'kernel': 'linear'}
0.637 (+/-0.307) for {'C': 1000.0, 'kernel': 'linear'}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'linear'}
0.620 (+/-0.321) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.617 (+/-0.238) for {'C': 3.9810717055349696, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.617 (+/-0.238) for {'C': 3.9810717055349696, 'kernel': 'rbf', 'gamma': 0.02097007578544648}
0.617 (+/-0.238) for {'C': 3.9810717055349696, 'kernel': 'rbf', 'gamma': 0.02104747488655977}
0.617 (+/-0.238) for {'C': 3.9810717055349696, 'kernel': 'rbf', 'gamma': 0.021125159662408542}
0.617 (+/-0.238) for {'C': 3.9810717055349696, 'kernel': 'rbf', 'gamma': 0.021203131167398505}
0.617 (+/-0.238) for {'C': 3.9810717055349696, 'kernel': 'rbf', 'gamma': 0.02128139045982712}
0.617 (+/-0.238) for {'C': 3.9810717055349696, 'kernel': 'rbf', 'gamma': 0.02135993860189796}
0.617 (+/-0.238) for {'C': 3.9810717055349696, 'kernel': 'rbf', 'gamma': 0.021438776659735086}
0.617 (+/-0.238) for {'C': 3.9810717055349696, 'kernel': 'rbf', 'gamma': 0.02151790570339755}
0.617 (+/-0.238) for {'C': 3.9810717055349696, 'kernel': 'rbf', 'gamma': 0.021597326806893944}
0.617 (+/-0.238) for {'C': 3.9810717055349696, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.617 (+/-0.238) for {'C': 6.309573444801927, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.617 (+/-0.238) for {'C': 6.309573444801927, 'kernel': 'rbf', 'gamma': 0.02097007578544648}
0.617 (+/-0.238) for {'C': 6.309573444801927, 'kernel': 'rbf', 'gamma': 0.02104747488655977}
0.617 (+/-0.238) for {'C': 6.309573444801927, 'kernel': 'rbf', 'gamma': 0.021125159662408542}
0.617 (+/-0.238) for {'C': 6.309573444801927, 'kernel': 'rbf', 'gamma': 0.021203131167398505}
0.617 (+/-0.238) for {'C': 6.309573444801927, 'kernel': 'rbf', 'gamma': 0.02128139045982712}
0.617 (+/-0.238) for {'C': 6.309573444801927, 'kernel': 'rbf', 'gamma': 0.02135993860189796}
0.617 (+/-0.238) for {'C': 6.309573444801927, 'kernel': 'rbf', 'gamma': 0.021438776659735086}
0.617 (+/-0.238) for {'C': 6.309573444801927, 'kernel': 'rbf', 'gamma': 0.02151790570339755}
0.617 (+/-0.238) for {'C': 6.309573444801927, 'kernel': 'rbf', 'gamma': 0.021597326806893944}
0.617 (+/-0.238) for {'C': 6.309573444801927, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.642 (+/-0.236) for {'C': 9.99999999999999, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.642 (+/-0.236) for {'C': 9.99999999999999, 'kernel': 'rbf', 'gamma': 0.02097007578544648}
0.642 (+/-0.236) for {'C': 9.99999999999999, 'kernel': 'rbf', 'gamma': 0.02104747488655977}
0.642 (+/-0.236) for {'C': 9.99999999999999, 'kernel': 'rbf', 'gamma': 0.021125159662408542}
0.642 (+/-0.236) for {'C': 9.99999999999999, 'kernel': 'rbf', 'gamma': 0.021203131167398505}
0.642 (+/-0.236) for {'C': 9.99999999999999, 'kernel': 'rbf', 'gamma': 0.02128139045982712}
0.642 (+/-0.236) for {'C': 9.99999999999999, 'kernel': 'rbf', 'gamma': 0.02135993860189796}
0.642 (+/-0.236) for {'C': 9.99999999999999, 'kernel': 'rbf', 'gamma': 0.021438776659735086}
0.642 (+/-0.236) for {'C': 9.99999999999999, 'kernel': 'rbf', 'gamma': 0.02151790570339755}
0.642 (+/-0.236) for {'C': 9.99999999999999, 'kernel': 'rbf', 'gamma': 0.021597326806893944}
0.642 (+/-0.236) for {'C': 9.99999999999999, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.666 (+/-0.316) for {'C': 15.84893192461112, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.666 (+/-0.316) for {'C': 15.84893192461112, 'kernel': 'rbf', 'gamma': 0.02097007578544648}
0.666 (+/-0.316) for {'C': 15.84893192461112, 'kernel': 'rbf', 'gamma': 0.02104747488655977}
0.666 (+/-0.316) for {'C': 15.84893192461112, 'kernel': 'rbf', 'gamma': 0.021125159662408542}
0.666 (+/-0.316) for {'C': 15.84893192461112, 'kernel': 'rbf', 'gamma': 0.021203131167398505}
0.666 (+/-0.316) for {'C': 15.84893192461112, 'kernel': 'rbf', 'gamma': 0.02128139045982712}
0.666 (+/-0.316) for {'C': 15.84893192461112, 'kernel': 'rbf', 'gamma': 0.02135993860189796}
0.666 (+/-0.316) for {'C': 15.84893192461112, 'kernel': 'rbf', 'gamma': 0.021438776659735086}
0.666 (+/-0.316) for {'C': 15.84893192461112, 'kernel': 'rbf', 'gamma': 0.02151790570339755}
0.666 (+/-0.316) for {'C': 15.84893192461112, 'kernel': 'rbf', 'gamma': 0.021597326806893944}
0.666 (+/-0.316) for {'C': 15.84893192461112, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.666 (+/-0.316) for {'C': 25.11886431509578, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.666 (+/-0.316) for {'C': 25.11886431509578, 'kernel': 'rbf', 'gamma': 0.02097007578544648}
0.666 (+/-0.316) for {'C': 25.11886431509578, 'kernel': 'rbf', 'gamma': 0.02104747488655977}
0.666 (+/-0.316) for {'C': 25.11886431509578, 'kernel': 'rbf', 'gamma': 0.021125159662408542}
0.666 (+/-0.316) for {'C': 25.11886431509578, 'kernel': 'rbf', 'gamma': 0.021203131167398505}
0.666 (+/-0.316) for {'C': 25.11886431509578, 'kernel': 'rbf', 'gamma': 0.02128139045982712}
0.666 (+/-0.316) for {'C': 25.11886431509578, 'kernel': 'rbf', 'gamma': 0.02135993860189796}
0.666 (+/-0.316) for {'C': 25.11886431509578, 'kernel': 'rbf', 'gamma': 0.021438776659735086}
0.666 (+/-0.316) for {'C': 25.11886431509578, 'kernel': 'rbf', 'gamma': 0.02151790570339755}
0.666 (+/-0.316) for {'C': 25.11886431509578, 'kernel': 'rbf', 'gamma': 0.021597326806893944}
0.666 (+/-0.316) for {'C': 25.11886431509578, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.682 (+/-0.294) for {'C': 39.810717055349684, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.682 (+/-0.294) for {'C': 39.810717055349684, 'kernel': 'rbf', 'gamma': 0.02097007578544648}
0.682 (+/-0.294) for {'C': 39.810717055349684, 'kernel': 'rbf', 'gamma': 0.02104747488655977}
0.682 (+/-0.294) for {'C': 39.810717055349684, 'kernel': 'rbf', 'gamma': 0.021125159662408542}
0.682 (+/-0.295) for {'C': 39.810717055349684, 'kernel': 'rbf', 'gamma': 0.021203131167398505}
0.682 (+/-0.295) for {'C': 39.810717055349684, 'kernel': 'rbf', 'gamma': 0.02128139045982712}
0.682 (+/-0.295) for {'C': 39.810717055349684, 'kernel': 'rbf', 'gamma': 0.02135993860189796}
0.682 (+/-0.295) for {'C': 39.810717055349684, 'kernel': 'rbf', 'gamma': 0.021438776659735086}
0.682 (+/-0.295) for {'C': 39.810717055349684, 'kernel': 'rbf', 'gamma': 0.02151790570339755}
0.682 (+/-0.295) for {'C': 39.810717055349684, 'kernel': 'rbf', 'gamma': 0.021597326806893944}
0.682 (+/-0.295) for {'C': 39.810717055349684, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.666 (+/-0.315) for {'C': 63.09573444801924, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.666 (+/-0.315) for {'C': 63.09573444801924, 'kernel': 'rbf', 'gamma': 0.02097007578544648}
0.666 (+/-0.315) for {'C': 63.09573444801924, 'kernel': 'rbf', 'gamma': 0.02104747488655977}
0.666 (+/-0.315) for {'C': 63.09573444801924, 'kernel': 'rbf', 'gamma': 0.021125159662408542}
0.666 (+/-0.315) for {'C': 63.09573444801924, 'kernel': 'rbf', 'gamma': 0.021203131167398505}
0.666 (+/-0.315) for {'C': 63.09573444801924, 'kernel': 'rbf', 'gamma': 0.02128139045982712}
0.666 (+/-0.315) for {'C': 63.09573444801924, 'kernel': 'rbf', 'gamma': 0.02135993860189796}
0.666 (+/-0.315) for {'C': 63.09573444801924, 'kernel': 'rbf', 'gamma': 0.021438776659735086}
0.666 (+/-0.315) for {'C': 63.09573444801924, 'kernel': 'rbf', 'gamma': 0.02151790570339755}
0.666 (+/-0.315) for {'C': 63.09573444801924, 'kernel': 'rbf', 'gamma': 0.021597326806893944}
0.666 (+/-0.315) for {'C': 63.09573444801924, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.665 (+/-0.315) for {'C': 99.99999999999991, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.665 (+/-0.315) for {'C': 99.99999999999991, 'kernel': 'rbf', 'gamma': 0.02097007578544648}
0.665 (+/-0.315) for {'C': 99.99999999999991, 'kernel': 'rbf', 'gamma': 0.02104747488655977}
0.665 (+/-0.315) for {'C': 99.99999999999991, 'kernel': 'rbf', 'gamma': 0.021125159662408542}
0.665 (+/-0.315) for {'C': 99.99999999999991, 'kernel': 'rbf', 'gamma': 0.021203131167398505}
0.665 (+/-0.315) for {'C': 99.99999999999991, 'kernel': 'rbf', 'gamma': 0.02128139045982712}
0.665 (+/-0.315) for {'C': 99.99999999999991, 'kernel': 'rbf', 'gamma': 0.02135993860189796}
0.665 (+/-0.315) for {'C': 99.99999999999991, 'kernel': 'rbf', 'gamma': 0.021438776659735086}
0.665 (+/-0.315) for {'C': 99.99999999999991, 'kernel': 'rbf', 'gamma': 0.02151790570339755}
0.665 (+/-0.315) for {'C': 99.99999999999991, 'kernel': 'rbf', 'gamma': 0.021597326806893944}
0.665 (+/-0.315) for {'C': 99.99999999999991, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.665 (+/-0.315) for {'C': 158.4893192461112, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.665 (+/-0.315) for {'C': 158.4893192461112, 'kernel': 'rbf', 'gamma': 0.02097007578544648}
0.665 (+/-0.315) for {'C': 158.4893192461112, 'kernel': 'rbf', 'gamma': 0.02104747488655977}
0.665 (+/-0.315) for {'C': 158.4893192461112, 'kernel': 'rbf', 'gamma': 0.021125159662408542}
0.665 (+/-0.315) for {'C': 158.4893192461112, 'kernel': 'rbf', 'gamma': 0.021203131167398505}
0.665 (+/-0.315) for {'C': 158.4893192461112, 'kernel': 'rbf', 'gamma': 0.02128139045982712}
0.665 (+/-0.315) for {'C': 158.4893192461112, 'kernel': 'rbf', 'gamma': 0.02135993860189796}
0.665 (+/-0.315) for {'C': 158.4893192461112, 'kernel': 'rbf', 'gamma': 0.021438776659735086}
0.665 (+/-0.315) for {'C': 158.4893192461112, 'kernel': 'rbf', 'gamma': 0.02151790570339755}
0.665 (+/-0.315) for {'C': 158.4893192461112, 'kernel': 'rbf', 'gamma': 0.021597326806893944}
0.665 (+/-0.315) for {'C': 158.4893192461112, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.663 (+/-0.315) for {'C': 251.1886431509577, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.663 (+/-0.315) for {'C': 251.1886431509577, 'kernel': 'rbf', 'gamma': 0.02097007578544648}
0.663 (+/-0.315) for {'C': 251.1886431509577, 'kernel': 'rbf', 'gamma': 0.02104747488655977}
0.663 (+/-0.315) for {'C': 251.1886431509577, 'kernel': 'rbf', 'gamma': 0.021125159662408542}
0.663 (+/-0.315) for {'C': 251.1886431509577, 'kernel': 'rbf', 'gamma': 0.021203131167398505}
0.662 (+/-0.315) for {'C': 251.1886431509577, 'kernel': 'rbf', 'gamma': 0.02128139045982712}
0.662 (+/-0.315) for {'C': 251.1886431509577, 'kernel': 'rbf', 'gamma': 0.02135993860189796}
0.662 (+/-0.314) for {'C': 251.1886431509577, 'kernel': 'rbf', 'gamma': 0.021438776659735086}
0.662 (+/-0.314) for {'C': 251.1886431509577, 'kernel': 'rbf', 'gamma': 0.02151790570339755}
0.662 (+/-0.314) for {'C': 251.1886431509577, 'kernel': 'rbf', 'gamma': 0.021597326806893944}
0.662 (+/-0.314) for {'C': 251.1886431509577, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.658 (+/-0.314) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.658 (+/-0.314) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.02097007578544648}
0.658 (+/-0.314) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.02104747488655977}
0.658 (+/-0.314) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.021125159662408542}
0.658 (+/-0.314) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.021203131167398505}
0.658 (+/-0.314) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.02128139045982712}
0.658 (+/-0.314) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.02135993860189796}
0.658 (+/-0.314) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.021438776659735086}
0.658 (+/-0.314) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.02151790570339755}
0.658 (+/-0.314) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.021597326806893944}
0.658 (+/-0.314) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.663 (+/-0.316) for {'C': 10.0, 'kernel': 'linear'}
0.661 (+/-0.315) for {'C': 100.0, 'kernel': 'linear'}
0.637 (+/-0.239) for {'C': 1000.0, 'kernel': 'linear'}
0.585 (+/-0.236) for {'C': 10000.0, 'kernel': 'linear'}
0.611 (+/-0.242) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.17      0.17      0.17         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 39.810717055349684, 'kernel': 'rbf', 'gamma': 0.021203131167398505}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6822115384615385
第2轮loop中，tunemodel函数得到的最优参数是： ([{'C': array([25.11886432, 27.54228703, 30.1995172 , 33.11311215, 36.30780548,
       39.81071706, 43.65158322, 47.86300923, 52.48074602, 57.54399373,
       63.09573445]), 'kernel': ['rbf'], 'gamma': array([0.02112516, 0.02114073, 0.02115631, 0.02117191, 0.02118751,
       0.02120313, 0.02121876, 0.0212344 , 0.02125005, 0.02126572,
       0.02128139])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}], {'C': 39.810717055349684, 'kernel': 'rbf', 'gamma': 0.021203131167398505}, 0.6822115384615385)
这是第2次迭代微调C和gamma。
第2次迭代，得到delta： [7.10542736e-15 7.82592924e-05]
SVC模型clf_op_iter的参数是： {'kernel': 'rbf', 'decision_function_shape': 'ovr', 'class_weight': {-1: 15}, 'tol': 0.001, 'gamma': 0.021203131167398505, 'random_state': 0, 'cache_size': 200, 'verbose': False, 'degree': 3, 'C': 39.810717055349684, 'probability': False, 'shrinking': True, 'coef0': 0.0, 'max_iter': -1}

训练模型SVC对训练样本的预测准确率： 0.9468462083628633

测试集中，预测为舞弊样本的有： (array([   8,   10,   11,   12,   13,   14,   17,   18,   19,   20,   21,
         22,   23,   24,   25,   30,   31,   32,   33,   34,   35,   36,
         37,   38,   39,   40,   41,   42,   43,   44,   46,   47,   48,
         49,   50,   51,   52,   53,   54,   57,   58,   59,   60,   63,
         64,   65,   66,   67,   68,   71,   72,   73,   74,   75,   76,
         77,   78,   79,   84,   87,   88,   90,   91,   92,   93,   98,
        101,  102,  105,  106,  107,  108,  109,  110,  111,  112,  113,
        114,  115,  116,  117,  118,  119,  120,  121,  127,  128,  129,
        135,  136,  141,  142,  143,  144,  145,  146,  150,  151,  153,
        154,  155,  156,  157,  158,  162,  163,  164,  165,  166,  169,
        176,  177,  180,  181,  182,  183,  184,  185,  186,  187,  192,
        193,  195,  196,  197,  199,  200,  201,  202,  204,  205,  207,
        208,  209,  212,  213,  214,  215,  216,  217,  218,  219,  225,
        229,  230,  231,  232,  233,  234,  235,  236,  237,  238,  239,
        242,  243,  244,  245,  246,  247,  251,  252,  253,  254,  255,
        256,  257,  258,  259,  260,  261,  262,  264,  267,  268,  269,
        270,  274,  275,  276,  277,  278,  279,  280,  281,  283,  284,
        285,  286,  287,  288,  290,  295,  296,  298,  299,  300,  301,
        302,  306,  307,  308,  313,  316,  317,  318,  319,  320,  321,
        322,  323,  324,  325,  330,  331,  332,  333,  334,  338,  339,
        340,  344,  346,  347,  348,  349,  351,  352,  353,  354,  356,
        358,  359,  360,  361,  362,  363,  364,  365,  366,  367,  368,
        369,  370,  374,  375,  379,  383,  384,  385,  386,  388,  391,
        392,  393,  394,  395,  396,  398,  399,  403,  404,  410,  413,
        414,  419,  420,  421,  422,  423,  426,  427,  428,  429,  430,
        431,  432,  433,  434,  438,  439,  440,  441,  442,  443,  444,
        446,  447,  448,  449,  450,  454,  455,  456,  457,  460,  461,
        463,  465,  466,  467,  468,  469,  471,  472,  473,  474,  475,
        476,  477,  478,  479,  480,  481,  482,  483,  484,  485,  486,
        487,  488,  489,  490,  491,  492,  493,  496,  498,  499,  500,
        501,  502,  503,  508,  509,  511,  512,  513,  514,  515,  516,
        517,  518,  519,  520,  521,  529,  530,  532,  534,  535,  540,
        541,  542,  543,  544,  545,  546,  547,  549,  551,  552,  555,
        557,  558,  560,  561,  562,  563,  564,  565,  566,  567,  568,
        570,  571,  572,  573,  578,  582,  583,  584,  586,  587,  589,
        590,  592,  593,  594,  595,  599,  600,  601,  604,  605,  611,
        612,  613,  614,  615,  616,  617,  618,  619,  620,  621,  622,
        623,  624,  629,  630,  631,  633,  634,  635,  641,  642,  643,
        646,  651,  653,  655,  656,  657,  658,  659,  660,  661,  662,
        663,  664,  665,  666,  667,  668,  669,  673,  674,  675,  676,
        677,  680,  681,  682,  683,  684,  685,  686,  687,  688,  691,
        692,  693,  694,  695,  696,  697,  698,  699,  701,  702,  703,
        705,  706,  713,  714,  715,  716,  717,  718,  720,  721,  722,
        723,  724,  729,  732,  733,  734,  735,  736,  742,  743,  751,
        752,  753,  754,  756,  758,  759,  760,  761,  769,  774,  775,
        776,  777,  778,  785,  786,  787,  788,  789,  790,  791,  792,
        793,  794,  795,  796,  797,  806,  807,  812,  813,  814,  815,
        816,  817,  819,  820,  824,  825,  826,  827,  828,  829,  830,
        840,  841,  843,  844,  845,  846,  847,  848,  849,  850,  851,
        852,  853,  854,  855,  856,  857,  859,  862,  864,  872,  874,
        875,  876,  877,  878,  879,  880,  881,  882,  883,  884,  885,
        893,  897,  898,  901,  902,  903,  904,  905,  906,  907,  910,
        911,  912,  913,  914,  916,  919,  922,  923,  924,  925,  927,
        928,  929,  930,  931,  932,  933,  934,  935,  946,  947,  948,
        949,  950,  951,  952,  953,  954,  955,  956,  959,  960,  961,
        963,  964,  965,  966,  967,  969,  970,  971,  976,  977,  978,
        979,  980,  981,  982,  983,  984,  985,  992,  993,  995,  998,
       1000, 1002, 1003, 1004, 1005, 1006, 1007, 1008, 1009, 1011, 1012,
       1013, 1014, 1015, 1016, 1017, 1020, 1021, 1022, 1030, 1032, 1033,
       1034, 1035, 1036, 1037, 1039, 1040, 1041, 1047, 1048, 1049, 1050,
       1051, 1055, 1056, 1057, 1060, 1061, 1064, 1065, 1067, 1068, 1071,
       1073, 1074, 1080, 1081, 1082, 1085, 1086, 1089, 1090, 1091, 1092,
       1093, 1094, 1095, 1096, 1097, 1098, 1099, 1100, 1101, 1105, 1106,
       1107, 1112, 1113, 1114, 1117, 1119, 1121, 1122, 1123, 1124, 1125,
       1127, 1129, 1131, 1132, 1134, 1135, 1137, 1138, 1140, 1141, 1142,
       1144, 1148, 1152, 1154, 1156, 1157, 1158, 1160, 1163, 1164, 1166,
       1167, 1168, 1170, 1171, 1175, 1180, 1181, 1183, 1188, 1191, 1192,
       1196, 1200, 1205, 1208, 1211, 1214, 1215, 1216, 1217, 1218, 1219,
       1222, 1226, 1227, 1230, 1231, 1232, 1233, 1234, 1235, 1236, 1237,
       1238, 1239, 1240, 1241, 1242, 1243, 1244, 1245, 1246, 1247, 1248,
       1249, 1250, 1251, 1252, 1254, 1255, 1256], dtype=int64),) 
测试集中，实际为舞弊样本的有： (array([1246, 1247, 1248, 1249, 1250, 1251, 1252, 1253, 1254, 1255, 1256],
      dtype=int64),) 
预测的舞弊样本数目： 777

训练模型SVC对测试样本的预测准确率： 0.4457831325301205
以上是第31次特征筛选。
第31次特征筛选，AUC值是： 0.6467605428279586
X_train_iter_svc.shape is: (1257, 21)
X_test_iter_svc.shape is: (1257, 21)
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.639 (+/-0.396) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.748 (+/-0.449) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.20      0.17      0.18         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.99      0.99       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.589 (+/-0.231) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.666 (+/-0.316) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.20      0.17      0.18         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.99      0.99       629

本轮grid search结果，得到最好的参数选择是： {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6663461538461538
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.632 (+/-0.309) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.20      0.17      0.18         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.99      0.99       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.20      0.17      0.18         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.99      0.99       629

本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6663461538461538
RBF的粗grid search最佳超参： {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001} RBF的粗grid search最高分值： 0.6663461538461538
linear的粗grid search最佳超参： {'C': 1.0, 'kernel': 'linear'} linear的粗grid search最高分值： 0.6663461538461538
粗grid search得到的parameter是：
 [{'C': array([1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03, 1.e+04, 1.e+05,
       1.e+06, 1.e+07, 1.e+08]), 'kernel': ['rbf'], 'gamma': array([1.e-08, 1.e-07, 1.e-06, 1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01,
       1.e+00, 1.e+01, 1.e+02])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.748 (+/-0.449) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.639 (+/-0.326) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.646 (+/-0.388) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.748 (+/-0.449) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.317) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.396) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.748 (+/-0.449) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.317) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.627 (+/-0.313) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.396) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.747 (+/-0.502) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.748 (+/-0.449) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.673 (+/-0.330) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.633 (+/-0.312) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.396) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.797 (+/-0.492) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.747 (+/-0.449) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.693 (+/-0.374) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.635 (+/-0.312) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.631 (+/-0.310) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.396) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.649 (+/-0.778) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.597 (+/-0.401) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.698 (+/-0.352) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.629 (+/-0.312) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.614 (+/-0.327) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.614 (+/-0.327) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.396) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.649 (+/-0.778) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.597 (+/-0.401) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.677 (+/-0.374) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.625 (+/-0.314) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.614 (+/-0.327) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.614 (+/-0.327) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.396) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.706 (+/-0.383) for {'C': 10.0, 'kernel': 'linear'}
0.635 (+/-0.313) for {'C': 100.0, 'kernel': 'linear'}
0.632 (+/-0.309) for {'C': 1000.0, 'kernel': 'linear'}
0.629 (+/-0.312) for {'C': 10000.0, 'kernel': 'linear'}
0.624 (+/-0.319) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      1.00       623

avg / total       0.98      0.99      0.99       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.006) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.316) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.239) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.666 (+/-0.316) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.665 (+/-0.315) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.587 (+/-0.234) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.589 (+/-0.231) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.316) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.665 (+/-0.315) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.636 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.232) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.589 (+/-0.231) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.617 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.666 (+/-0.316) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.665 (+/-0.316) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.661 (+/-0.316) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.588 (+/-0.233) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.233) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.589 (+/-0.231) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.642 (+/-0.236) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.641 (+/-0.236) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.665 (+/-0.316) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.661 (+/-0.316) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.637 (+/-0.238) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.586 (+/-0.236) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.233) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.589 (+/-0.231) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.617 (+/-0.238) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.542 (+/-0.171) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.682 (+/-0.296) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.660 (+/-0.317) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.587 (+/-0.234) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.586 (+/-0.236) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.586 (+/-0.236) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.233) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.589 (+/-0.231) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.614 (+/-0.244) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.542 (+/-0.171) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.664 (+/-0.318) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.659 (+/-0.311) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.587 (+/-0.234) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.586 (+/-0.236) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.586 (+/-0.236) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.233) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.589 (+/-0.231) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 10.0, 'kernel': 'linear'}
0.636 (+/-0.237) for {'C': 100.0, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.636 (+/-0.240) for {'C': 10000.0, 'kernel': 'linear'}
0.610 (+/-0.245) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.17      0.17      0.17         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.681729738642922
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.656 (+/-0.384) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.639 (+/-0.306) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.619 (+/-0.322) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.307) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.323) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.646 (+/-0.388) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.662 (+/-0.298) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.624 (+/-0.316) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.396) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.662 (+/-0.298) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.624 (+/-0.316) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.627 (+/-0.313) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.396) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.747 (+/-0.502) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.797 (+/-0.492) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.662 (+/-0.373) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.624 (+/-0.316) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.633 (+/-0.312) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.396) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.797 (+/-0.492) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.654 (+/-0.293) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.656 (+/-0.389) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.635 (+/-0.312) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.631 (+/-0.310) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.396) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.699 (+/-0.797) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.599 (+/-0.399) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.636 (+/-0.292) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.626 (+/-0.314) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.614 (+/-0.327) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.614 (+/-0.327) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.396) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.599 (+/-0.745) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.597 (+/-0.400) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.611 (+/-0.305) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.625 (+/-0.314) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.614 (+/-0.327) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.614 (+/-0.327) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.396) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.666 (+/-0.379) for {'C': 10.0, 'kernel': 'linear'}
0.629 (+/-0.313) for {'C': 100.0, 'kernel': 'linear'}
0.632 (+/-0.309) for {'C': 1000.0, 'kernel': 'linear'}
0.629 (+/-0.312) for {'C': 10000.0, 'kernel': 'linear'}
0.624 (+/-0.319) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.006) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.665 (+/-0.314) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.611 (+/-0.241) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.665 (+/-0.314) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.608 (+/-0.244) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.239) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.681 (+/-0.294) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.657 (+/-0.314) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.587 (+/-0.234) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.589 (+/-0.231) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.681 (+/-0.294) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.657 (+/-0.314) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.636 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.232) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.589 (+/-0.231) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.617 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.642 (+/-0.236) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.665 (+/-0.314) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.658 (+/-0.313) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.661 (+/-0.316) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.588 (+/-0.233) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.233) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.589 (+/-0.231) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.642 (+/-0.236) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.681 (+/-0.294) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.657 (+/-0.313) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.661 (+/-0.316) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.637 (+/-0.238) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.586 (+/-0.236) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.233) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.589 (+/-0.231) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.642 (+/-0.236) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.562 (+/-0.195) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.677 (+/-0.294) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.658 (+/-0.314) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.587 (+/-0.234) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.586 (+/-0.236) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.586 (+/-0.236) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.233) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.589 (+/-0.231) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.575 (+/-0.274) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.552 (+/-0.173) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.658 (+/-0.312) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.659 (+/-0.312) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.587 (+/-0.234) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.586 (+/-0.236) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.586 (+/-0.236) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.233) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.589 (+/-0.231) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.662 (+/-0.314) for {'C': 10.0, 'kernel': 'linear'}
0.660 (+/-0.315) for {'C': 100.0, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.636 (+/-0.240) for {'C': 10000.0, 'kernel': 'linear'}
0.610 (+/-0.245) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.14      0.17      0.15         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6812494847060763
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.797 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.797 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.797 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.747 (+/-0.502) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.798 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.747 (+/-0.449) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.748 (+/-0.449) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.663 (+/-0.368) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.681 (+/-0.373) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.681 (+/-0.373) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.693 (+/-0.374) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.797 (+/-0.492) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.797 (+/-0.492) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.798 (+/-0.492) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.747 (+/-0.502) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.748 (+/-0.449) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.748 (+/-0.449) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.681 (+/-0.372) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.651 (+/-0.308) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.681 (+/-0.373) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.677 (+/-0.374) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.648 (+/-0.306) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.797 (+/-0.492) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.797 (+/-0.492) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.798 (+/-0.492) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.747 (+/-0.502) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.798 (+/-0.437) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.748 (+/-0.449) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.631 (+/-0.298) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.668 (+/-0.318) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.681 (+/-0.373) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.658 (+/-0.329) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.644 (+/-0.309) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.797 (+/-0.492) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.797 (+/-0.492) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.798 (+/-0.492) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.697 (+/-0.437) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.706 (+/-0.360) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.764 (+/-0.422) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.652 (+/-0.313) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.677 (+/-0.374) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.670 (+/-0.381) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.652 (+/-0.335) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.645 (+/-0.318) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.597 (+/-0.401) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.798 (+/-0.492) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.798 (+/-0.492) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.697 (+/-0.437) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.698 (+/-0.409) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.731 (+/-0.394) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.652 (+/-0.313) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.660 (+/-0.374) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.668 (+/-0.382) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.642 (+/-0.330) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.629 (+/-0.313) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.647 (+/-0.459) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.797 (+/-0.492) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.697 (+/-0.492) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.697 (+/-0.437) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.681 (+/-0.427) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.698 (+/-0.352) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.652 (+/-0.313) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.665 (+/-0.381) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.668 (+/-0.383) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.654 (+/-0.381) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.629 (+/-0.312) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.647 (+/-0.459) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.798 (+/-0.492) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.697 (+/-0.492) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.656 (+/-0.384) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.681 (+/-0.427) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.681 (+/-0.373) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.642 (+/-0.307) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.656 (+/-0.377) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.651 (+/-0.336) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.654 (+/-0.381) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.626 (+/-0.313) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.647 (+/-0.459) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.798 (+/-0.492) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.672 (+/-0.452) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.656 (+/-0.384) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.681 (+/-0.427) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.677 (+/-0.374) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.642 (+/-0.307) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.656 (+/-0.377) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.650 (+/-0.337) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.654 (+/-0.381) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.626 (+/-0.313) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.597 (+/-0.401) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.798 (+/-0.492) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.672 (+/-0.452) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.656 (+/-0.384) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.662 (+/-0.372) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.681 (+/-0.373) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.642 (+/-0.307) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.621 (+/-0.302) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.637 (+/-0.317) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.654 (+/-0.381) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.625 (+/-0.314) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.597 (+/-0.401) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.798 (+/-0.492) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.672 (+/-0.452) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.656 (+/-0.384) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.662 (+/-0.372) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.677 (+/-0.374) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.621 (+/-0.317) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.622 (+/-0.302) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.637 (+/-0.317) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.655 (+/-0.381) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.625 (+/-0.314) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.647 (+/-0.459) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.798 (+/-0.492) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.672 (+/-0.452) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.656 (+/-0.384) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.662 (+/-0.372) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.677 (+/-0.374) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.621 (+/-0.317) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.621 (+/-0.302) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.633 (+/-0.315) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.655 (+/-0.381) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.625 (+/-0.314) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.706 (+/-0.383) for {'C': 10.0, 'kernel': 'linear'}
0.635 (+/-0.313) for {'C': 100.0, 'kernel': 'linear'}
0.632 (+/-0.309) for {'C': 1000.0, 'kernel': 'linear'}
0.629 (+/-0.312) for {'C': 10000.0, 'kernel': 'linear'}
0.624 (+/-0.319) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.25      0.17      0.20         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.99       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.642 (+/-0.236) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.642 (+/-0.236) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.642 (+/-0.236) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.617 (+/-0.238) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.667 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.641 (+/-0.236) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.666 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.665 (+/-0.313) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.665 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.665 (+/-0.317) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.665 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.642 (+/-0.236) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.642 (+/-0.236) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.667 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.617 (+/-0.238) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.666 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.666 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.665 (+/-0.315) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.665 (+/-0.315) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.665 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.665 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.663 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.642 (+/-0.236) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.642 (+/-0.236) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.667 (+/-0.316) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.617 (+/-0.238) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.683 (+/-0.296) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.666 (+/-0.316) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.664 (+/-0.312) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.665 (+/-0.315) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.665 (+/-0.317) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.664 (+/-0.317) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.662 (+/-0.318) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.642 (+/-0.236) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.642 (+/-0.236) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.667 (+/-0.316) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.616 (+/-0.237) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.682 (+/-0.295) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.683 (+/-0.296) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.664 (+/-0.313) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.664 (+/-0.313) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.663 (+/-0.317) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.662 (+/-0.318) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.661 (+/-0.320) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.542 (+/-0.171) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.667 (+/-0.316) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.667 (+/-0.316) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.616 (+/-0.237) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.681 (+/-0.295) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.682 (+/-0.296) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.664 (+/-0.314) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.664 (+/-0.312) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.663 (+/-0.317) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.661 (+/-0.318) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.659 (+/-0.317) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.567 (+/-0.208) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.642 (+/-0.236) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.617 (+/-0.327) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.616 (+/-0.238) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.656 (+/-0.311) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.682 (+/-0.296) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.663 (+/-0.315) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.663 (+/-0.311) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.661 (+/-0.319) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.660 (+/-0.319) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.660 (+/-0.317) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.567 (+/-0.208) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.667 (+/-0.316) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.617 (+/-0.327) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.616 (+/-0.237) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.656 (+/-0.311) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.665 (+/-0.317) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.663 (+/-0.315) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.663 (+/-0.311) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.661 (+/-0.318) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.659 (+/-0.320) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.659 (+/-0.315) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.567 (+/-0.208) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.667 (+/-0.316) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.617 (+/-0.326) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.615 (+/-0.237) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.656 (+/-0.311) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.665 (+/-0.317) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.663 (+/-0.314) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.663 (+/-0.312) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.660 (+/-0.319) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.659 (+/-0.320) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.660 (+/-0.312) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.542 (+/-0.171) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.667 (+/-0.316) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.617 (+/-0.326) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.615 (+/-0.238) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.673 (+/-0.327) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.665 (+/-0.318) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.663 (+/-0.315) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.662 (+/-0.312) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.659 (+/-0.318) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.659 (+/-0.320) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.659 (+/-0.311) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.542 (+/-0.171) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.667 (+/-0.316) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.617 (+/-0.326) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.615 (+/-0.238) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.672 (+/-0.328) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.664 (+/-0.318) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.637 (+/-0.324) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.662 (+/-0.313) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.659 (+/-0.318) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.660 (+/-0.320) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.660 (+/-0.311) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.567 (+/-0.208) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.667 (+/-0.316) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.617 (+/-0.326) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.615 (+/-0.238) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.672 (+/-0.328) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.664 (+/-0.318) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.637 (+/-0.324) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.662 (+/-0.312) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.659 (+/-0.318) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.660 (+/-0.320) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.659 (+/-0.311) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 10.0, 'kernel': 'linear'}
0.636 (+/-0.237) for {'C': 100.0, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.636 (+/-0.240) for {'C': 10000.0, 'kernel': 'linear'}
0.610 (+/-0.245) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.25      0.17      0.20         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.99       629

本轮grid search结果，得到最好的参数选择是： {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6830128205128205
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.797 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.798 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.848 (+/-0.460) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.798 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.735 (+/-0.402) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.654 (+/-0.293) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.628 (+/-0.299) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.616 (+/-0.288) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.621 (+/-0.302) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.629 (+/-0.312) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.656 (+/-0.389) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.797 (+/-0.492) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.798 (+/-0.492) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.798 (+/-0.492) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.689 (+/-0.374) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.679 (+/-0.372) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.637 (+/-0.307) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.611 (+/-0.289) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.623 (+/-0.302) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.618 (+/-0.305) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.624 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.624 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.647 (+/-0.459) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.798 (+/-0.492) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.789 (+/-0.443) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.668 (+/-0.371) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.648 (+/-0.313) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.683 (+/-0.353) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.605 (+/-0.292) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.635 (+/-0.321) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.625 (+/-0.316) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.623 (+/-0.316) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.625 (+/-0.314) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.622 (+/-0.404) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.781 (+/-0.474) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.758 (+/-0.429) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.641 (+/-0.307) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.639 (+/-0.297) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.688 (+/-0.357) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.624 (+/-0.315) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.657 (+/-0.388) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.623 (+/-0.317) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.624 (+/-0.316) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.625 (+/-0.314) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.601 (+/-0.397) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.773 (+/-0.473) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.705 (+/-0.426) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.619 (+/-0.299) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.639 (+/-0.287) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.651 (+/-0.294) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.622 (+/-0.317) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.647 (+/-0.385) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.622 (+/-0.318) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.617 (+/-0.306) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.624 (+/-0.315) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.599 (+/-0.399) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.768 (+/-0.475) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.704 (+/-0.427) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.618 (+/-0.299) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.614 (+/-0.289) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.636 (+/-0.292) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.622 (+/-0.318) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.655 (+/-0.390) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.622 (+/-0.318) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.613 (+/-0.304) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.626 (+/-0.314) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.598 (+/-0.400) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.764 (+/-0.477) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.686 (+/-0.426) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.617 (+/-0.300) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.623 (+/-0.299) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.619 (+/-0.304) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.613 (+/-0.309) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.646 (+/-0.387) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.622 (+/-0.318) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.613 (+/-0.304) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.626 (+/-0.314) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.598 (+/-0.400) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.764 (+/-0.477) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.650 (+/-0.380) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.617 (+/-0.300) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.623 (+/-0.299) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.612 (+/-0.304) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.613 (+/-0.309) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.646 (+/-0.387) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.622 (+/-0.318) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.613 (+/-0.304) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.626 (+/-0.314) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.598 (+/-0.400) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.764 (+/-0.477) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.647 (+/-0.382) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.613 (+/-0.298) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.625 (+/-0.297) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.617 (+/-0.305) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.613 (+/-0.309) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.613 (+/-0.309) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.622 (+/-0.318) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.613 (+/-0.304) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.625 (+/-0.314) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.598 (+/-0.400) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.764 (+/-0.477) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.690 (+/-0.431) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.610 (+/-0.298) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.620 (+/-0.298) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.612 (+/-0.304) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.592 (+/-0.311) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.613 (+/-0.309) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.622 (+/-0.318) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.613 (+/-0.304) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.625 (+/-0.314) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.597 (+/-0.400) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.798 (+/-0.492) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.697 (+/-0.420) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.610 (+/-0.298) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.617 (+/-0.290) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.611 (+/-0.305) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.592 (+/-0.311) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.613 (+/-0.309) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.622 (+/-0.318) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.613 (+/-0.304) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.625 (+/-0.314) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.666 (+/-0.379) for {'C': 10.0, 'kernel': 'linear'}
0.629 (+/-0.313) for {'C': 100.0, 'kernel': 'linear'}
0.632 (+/-0.309) for {'C': 1000.0, 'kernel': 'linear'}
0.629 (+/-0.312) for {'C': 10000.0, 'kernel': 'linear'}
0.624 (+/-0.319) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.642 (+/-0.236) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.667 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.683 (+/-0.296) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.667 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.682 (+/-0.295) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.681 (+/-0.294) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.663 (+/-0.311) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.663 (+/-0.311) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.661 (+/-0.313) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.660 (+/-0.317) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.657 (+/-0.313) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.642 (+/-0.236) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.667 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.667 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.666 (+/-0.314) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.681 (+/-0.295) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.664 (+/-0.315) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.662 (+/-0.310) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.661 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.658 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.656 (+/-0.317) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.657 (+/-0.313) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.567 (+/-0.208) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.667 (+/-0.316) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.683 (+/-0.296) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.665 (+/-0.311) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.680 (+/-0.295) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.680 (+/-0.294) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.659 (+/-0.311) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.659 (+/-0.316) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.656 (+/-0.317) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.656 (+/-0.315) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.658 (+/-0.314) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.567 (+/-0.208) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.667 (+/-0.316) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.682 (+/-0.295) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.664 (+/-0.311) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.680 (+/-0.295) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.679 (+/-0.295) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.658 (+/-0.313) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.656 (+/-0.316) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.655 (+/-0.317) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.656 (+/-0.315) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.658 (+/-0.314) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.565 (+/-0.202) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.666 (+/-0.315) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.657 (+/-0.310) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.663 (+/-0.309) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.679 (+/-0.296) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.678 (+/-0.295) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.656 (+/-0.311) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.654 (+/-0.318) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.654 (+/-0.314) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.657 (+/-0.317) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.658 (+/-0.313) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.563 (+/-0.198) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.666 (+/-0.314) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.657 (+/-0.310) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.663 (+/-0.311) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.654 (+/-0.311) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.677 (+/-0.294) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.654 (+/-0.314) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.652 (+/-0.320) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.653 (+/-0.316) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.657 (+/-0.315) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.658 (+/-0.314) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.559 (+/-0.187) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.666 (+/-0.314) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.656 (+/-0.310) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.662 (+/-0.311) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.653 (+/-0.314) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.660 (+/-0.314) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.652 (+/-0.316) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.651 (+/-0.317) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.654 (+/-0.316) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.657 (+/-0.316) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.658 (+/-0.315) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.557 (+/-0.182) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.666 (+/-0.314) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.655 (+/-0.309) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.662 (+/-0.311) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.653 (+/-0.314) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.658 (+/-0.312) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.652 (+/-0.317) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.651 (+/-0.317) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.654 (+/-0.314) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.657 (+/-0.314) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.659 (+/-0.312) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.555 (+/-0.177) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.666 (+/-0.314) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.655 (+/-0.309) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.662 (+/-0.312) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.669 (+/-0.332) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.658 (+/-0.313) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.652 (+/-0.317) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.652 (+/-0.315) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.654 (+/-0.314) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.658 (+/-0.313) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.659 (+/-0.312) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.554 (+/-0.175) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.666 (+/-0.314) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.664 (+/-0.315) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.662 (+/-0.311) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.669 (+/-0.331) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.658 (+/-0.311) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.626 (+/-0.323) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.652 (+/-0.315) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.654 (+/-0.313) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.658 (+/-0.314) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.659 (+/-0.311) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.553 (+/-0.173) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.667 (+/-0.316) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.680 (+/-0.295) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.662 (+/-0.311) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.669 (+/-0.331) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.658 (+/-0.312) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.626 (+/-0.323) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.652 (+/-0.316) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.654 (+/-0.313) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.658 (+/-0.314) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.659 (+/-0.312) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.662 (+/-0.314) for {'C': 10.0, 'kernel': 'linear'}
0.660 (+/-0.315) for {'C': 100.0, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.636 (+/-0.240) for {'C': 10000.0, 'kernel': 'linear'}
0.610 (+/-0.245) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6833333333333332
发现最优参数C为原先的最大/最小值，直接重新设置超参。
循环迭代之前，delta is: [9.00000000e+06 7.48811357e-07]
执行tunemodel()函数前，使用的grid search parameter是：
 [{'C': array([1.e+01, 1.e+02, 1.e+03, 1.e+04, 1.e+05, 1.e+06, 1.e+07, 1.e+08,
       1.e+09, 1.e+10, 1.e+11]), 'kernel': ['rbf'], 'gamma': array([1.58489319e-07, 1.73780083e-07, 1.90546072e-07, 2.08929613e-07,
       2.29086765e-07, 2.51188643e-07, 2.75422870e-07, 3.01995172e-07,
       3.31131121e-07, 3.63078055e-07, 3.98107171e-07])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.747 (+/-0.502) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.747 (+/-0.502) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.747 (+/-0.502) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.747 (+/-0.502) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.747 (+/-0.502) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.747 (+/-0.502) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.747 (+/-0.502) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.747 (+/-0.502) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.747 (+/-0.502) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.747 (+/-0.502) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.747 (+/-0.502) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.797 (+/-0.492) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.747 (+/-0.502) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.797 (+/-0.492) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.798 (+/-0.492) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.797 (+/-0.492) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.797 (+/-0.492) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.798 (+/-0.492) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.797 (+/-0.492) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.797 (+/-0.492) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.797 (+/-0.492) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.747 (+/-0.502) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.797 (+/-0.492) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.747 (+/-0.502) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.747 (+/-0.502) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.647 (+/-0.460) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.697 (+/-0.492) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.697 (+/-0.492) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.672 (+/-0.399) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.681 (+/-0.427) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.744 (+/-0.433) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.748 (+/-0.449) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.697 (+/-0.437) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.798 (+/-0.492) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.747 (+/-0.502) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.697 (+/-0.438) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.647 (+/-0.460) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.722 (+/-0.473) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.672 (+/-0.452) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.653 (+/-0.401) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.671 (+/-0.433) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.739 (+/-0.437) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.714 (+/-0.418) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.656 (+/-0.384) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.798 (+/-0.492) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.747 (+/-0.502) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.697 (+/-0.438) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.647 (+/-0.460) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.722 (+/-0.473) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.672 (+/-0.452) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.653 (+/-0.401) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.671 (+/-0.433) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.739 (+/-0.437) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.714 (+/-0.418) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.631 (+/-0.319) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.798 (+/-0.492) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.747 (+/-0.502) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.697 (+/-0.438) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.647 (+/-0.460) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.697 (+/-0.492) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.672 (+/-0.452) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.653 (+/-0.401) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.671 (+/-0.433) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.739 (+/-0.437) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.714 (+/-0.418) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.631 (+/-0.319) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.798 (+/-0.492) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.747 (+/-0.502) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.697 (+/-0.438) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.647 (+/-0.460) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.722 (+/-0.473) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.672 (+/-0.452) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.653 (+/-0.401) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.671 (+/-0.433) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.739 (+/-0.437) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.714 (+/-0.418) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.631 (+/-0.319) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.706 (+/-0.383) for {'C': 10.0, 'kernel': 'linear'}
0.635 (+/-0.313) for {'C': 100.0, 'kernel': 'linear'}
0.632 (+/-0.309) for {'C': 1000.0, 'kernel': 'linear'}
0.629 (+/-0.312) for {'C': 10000.0, 'kernel': 'linear'}
0.624 (+/-0.319) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.617 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.617 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.617 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.617 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.617 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.617 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.617 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.617 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.617 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.617 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.617 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.642 (+/-0.236) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.617 (+/-0.238) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.642 (+/-0.236) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.667 (+/-0.316) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.642 (+/-0.236) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.642 (+/-0.236) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.667 (+/-0.316) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.642 (+/-0.236) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.642 (+/-0.236) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.642 (+/-0.236) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.617 (+/-0.238) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.642 (+/-0.236) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.617 (+/-0.238) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.642 (+/-0.325) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.592 (+/-0.320) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.617 (+/-0.327) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.617 (+/-0.327) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.641 (+/-0.324) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.665 (+/-0.315) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.682 (+/-0.296) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.666 (+/-0.316) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.616 (+/-0.238) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.667 (+/-0.316) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.617 (+/-0.238) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.641 (+/-0.325) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.592 (+/-0.320) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.633 (+/-0.318) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.617 (+/-0.326) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.640 (+/-0.323) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.664 (+/-0.314) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.682 (+/-0.296) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.666 (+/-0.316) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.615 (+/-0.238) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.667 (+/-0.316) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.617 (+/-0.238) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.641 (+/-0.325) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.592 (+/-0.320) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.633 (+/-0.318) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.617 (+/-0.326) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.640 (+/-0.323) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.664 (+/-0.314) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.682 (+/-0.296) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.666 (+/-0.316) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.615 (+/-0.238) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.667 (+/-0.316) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.617 (+/-0.238) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.641 (+/-0.325) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.592 (+/-0.320) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.617 (+/-0.327) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.617 (+/-0.326) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.640 (+/-0.323) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.664 (+/-0.314) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.682 (+/-0.296) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.666 (+/-0.316) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.615 (+/-0.238) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.667 (+/-0.316) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.617 (+/-0.238) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.641 (+/-0.325) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.592 (+/-0.320) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.633 (+/-0.318) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.617 (+/-0.326) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.640 (+/-0.323) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.664 (+/-0.314) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.682 (+/-0.296) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.666 (+/-0.316) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.615 (+/-0.238) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 10.0, 'kernel': 'linear'}
0.636 (+/-0.237) for {'C': 100.0, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.636 (+/-0.240) for {'C': 10000.0, 'kernel': 'linear'}
0.610 (+/-0.245) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6822099925797676
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.747 (+/-0.502) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.747 (+/-0.502) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.747 (+/-0.502) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.797 (+/-0.492) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.797 (+/-0.492) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.797 (+/-0.492) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.747 (+/-0.502) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.747 (+/-0.502) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.747 (+/-0.502) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.747 (+/-0.502) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.798 (+/-0.492) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.798 (+/-0.492) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.798 (+/-0.492) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.798 (+/-0.492) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.798 (+/-0.492) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.848 (+/-0.460) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.798 (+/-0.492) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.798 (+/-0.492) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.798 (+/-0.492) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.797 (+/-0.492) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.798 (+/-0.492) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.768 (+/-0.475) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.748 (+/-0.449) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.723 (+/-0.461) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.664 (+/-0.449) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.689 (+/-0.436) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.704 (+/-0.427) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.669 (+/-0.365) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.652 (+/-0.374) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.659 (+/-0.368) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.608 (+/-0.309) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.618 (+/-0.299) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.798 (+/-0.492) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.723 (+/-0.417) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.723 (+/-0.461) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.666 (+/-0.441) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.664 (+/-0.389) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.697 (+/-0.420) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.658 (+/-0.373) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.644 (+/-0.381) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.625 (+/-0.305) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.587 (+/-0.313) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.610 (+/-0.298) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.798 (+/-0.492) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.723 (+/-0.417) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.721 (+/-0.463) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.666 (+/-0.441) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.660 (+/-0.389) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.690 (+/-0.431) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.653 (+/-0.374) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.643 (+/-0.382) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.629 (+/-0.304) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.586 (+/-0.313) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.611 (+/-0.299) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.764 (+/-0.477) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.723 (+/-0.417) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.721 (+/-0.463) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.657 (+/-0.451) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.660 (+/-0.389) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.665 (+/-0.383) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.653 (+/-0.374) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.643 (+/-0.382) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.629 (+/-0.304) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.586 (+/-0.313) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.611 (+/-0.299) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.798 (+/-0.492) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.723 (+/-0.417) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.721 (+/-0.463) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.657 (+/-0.451) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.660 (+/-0.389) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.672 (+/-0.372) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.656 (+/-0.372) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.643 (+/-0.382) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.629 (+/-0.304) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.586 (+/-0.313) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.611 (+/-0.299) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.666 (+/-0.379) for {'C': 10.0, 'kernel': 'linear'}
0.629 (+/-0.313) for {'C': 100.0, 'kernel': 'linear'}
0.632 (+/-0.309) for {'C': 1000.0, 'kernel': 'linear'}
0.629 (+/-0.312) for {'C': 10000.0, 'kernel': 'linear'}
0.624 (+/-0.319) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.617 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.617 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.617 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.642 (+/-0.236) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.642 (+/-0.236) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.642 (+/-0.236) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.617 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.617 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.617 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.617 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.667 (+/-0.316) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.667 (+/-0.316) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.667 (+/-0.316) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.667 (+/-0.316) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.667 (+/-0.316) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.683 (+/-0.296) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.667 (+/-0.316) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.667 (+/-0.316) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.667 (+/-0.316) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.642 (+/-0.236) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.667 (+/-0.316) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.666 (+/-0.314) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.666 (+/-0.316) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.666 (+/-0.315) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.616 (+/-0.327) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.632 (+/-0.318) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.657 (+/-0.310) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.680 (+/-0.294) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.679 (+/-0.293) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.680 (+/-0.294) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.638 (+/-0.316) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.663 (+/-0.311) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.667 (+/-0.316) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.666 (+/-0.316) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.666 (+/-0.315) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.631 (+/-0.318) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.648 (+/-0.340) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.680 (+/-0.295) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.679 (+/-0.294) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.677 (+/-0.292) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.678 (+/-0.293) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.611 (+/-0.312) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.662 (+/-0.311) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.667 (+/-0.316) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.666 (+/-0.316) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.665 (+/-0.315) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.631 (+/-0.318) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.648 (+/-0.340) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.663 (+/-0.316) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.679 (+/-0.293) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.677 (+/-0.292) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.695 (+/-0.304) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.611 (+/-0.311) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.662 (+/-0.312) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.666 (+/-0.314) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.666 (+/-0.316) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.665 (+/-0.315) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.614 (+/-0.328) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.648 (+/-0.340) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.663 (+/-0.316) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.679 (+/-0.293) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.677 (+/-0.292) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.695 (+/-0.304) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.611 (+/-0.310) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.662 (+/-0.312) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.667 (+/-0.316) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.666 (+/-0.316) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.665 (+/-0.315) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.614 (+/-0.328) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.648 (+/-0.340) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.680 (+/-0.294) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.679 (+/-0.293) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.677 (+/-0.292) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.695 (+/-0.304) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.611 (+/-0.310) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.662 (+/-0.312) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.662 (+/-0.314) for {'C': 10.0, 'kernel': 'linear'}
0.660 (+/-0.315) for {'C': 100.0, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.636 (+/-0.240) for {'C': 10000.0, 'kernel': 'linear'}
0.610 (+/-0.245) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6947079314040729
第0轮loop中，tunemodel函数得到的最优参数是： ([{'C': array([1.00000000e+08, 1.58489319e+08, 2.51188643e+08, 3.98107171e+08,
       6.30957344e+08, 1.00000000e+09, 1.58489319e+09, 2.51188643e+09,
       3.98107171e+09, 6.30957344e+09, 1.00000000e+10]), 'kernel': ['rbf'], 'gamma': array([3.01995172e-07, 3.07609681e-07, 3.13328572e-07, 3.19153786e-07,
       3.25087297e-07, 3.31131121e-07, 3.37287309e-07, 3.43557948e-07,
       3.49945167e-07, 3.56451133e-07, 3.63078055e-07])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}], {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}, 0.6947079314040729)
这是第0次迭代微调C和gamma。
第0次迭代，得到delta： [9.99000000e+08 7.99424783e-08]
执行tunemodel()函数前，使用的grid search parameter是：
 [{'C': array([1.00000000e+08, 1.58489319e+08, 2.51188643e+08, 3.98107171e+08,
       6.30957344e+08, 1.00000000e+09, 1.58489319e+09, 2.51188643e+09,
       3.98107171e+09, 6.30957344e+09, 1.00000000e+10]), 'kernel': ['rbf'], 'gamma': array([3.01995172e-07, 3.07609681e-07, 3.13328572e-07, 3.19153786e-07,
       3.25087297e-07, 3.31131121e-07, 3.37287309e-07, 3.43557948e-07,
       3.49945167e-07, 3.56451133e-07, 3.63078055e-07])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.671 (+/-0.433) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.690 (+/-0.414) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.076096814740707e-07}
0.688 (+/-0.425) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.1332857243155833e-07}
0.691 (+/-0.422) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.191537855100758e-07}
0.689 (+/-0.422) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.739 (+/-0.437) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.706 (+/-0.418) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.685 (+/-0.438) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.4355794789987457e-07}
0.681 (+/-0.373) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.499451670283571e-07}
0.723 (+/-0.417) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.564511334262439e-07}
0.714 (+/-0.418) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.671 (+/-0.433) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.689 (+/-0.415) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.076096814740707e-07}
0.688 (+/-0.425) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.1332857243155833e-07}
0.691 (+/-0.422) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.191537855100758e-07}
0.702 (+/-0.405) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.739 (+/-0.437) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.706 (+/-0.418) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.685 (+/-0.438) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.4355794789987457e-07}
0.681 (+/-0.373) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.499451670283571e-07}
0.714 (+/-0.418) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.564511334262439e-07}
0.714 (+/-0.418) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.671 (+/-0.433) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.690 (+/-0.414) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.076096814740707e-07}
0.688 (+/-0.425) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.1332857243155833e-07}
0.691 (+/-0.422) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.191537855100758e-07}
0.702 (+/-0.405) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.739 (+/-0.437) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.706 (+/-0.418) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.685 (+/-0.438) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.4355794789987457e-07}
0.681 (+/-0.373) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.499451670283571e-07}
0.714 (+/-0.418) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.564511334262439e-07}
0.714 (+/-0.418) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.671 (+/-0.433) for {'C': 398107170.5534974, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.689 (+/-0.415) for {'C': 398107170.5534974, 'kernel': 'rbf', 'gamma': 3.076096814740707e-07}
0.681 (+/-0.428) for {'C': 398107170.5534974, 'kernel': 'rbf', 'gamma': 3.1332857243155833e-07}
0.687 (+/-0.424) for {'C': 398107170.5534974, 'kernel': 'rbf', 'gamma': 3.191537855100758e-07}
0.702 (+/-0.405) for {'C': 398107170.5534974, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.739 (+/-0.437) for {'C': 398107170.5534974, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.706 (+/-0.418) for {'C': 398107170.5534974, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.685 (+/-0.438) for {'C': 398107170.5534974, 'kernel': 'rbf', 'gamma': 3.4355794789987457e-07}
0.681 (+/-0.373) for {'C': 398107170.5534974, 'kernel': 'rbf', 'gamma': 3.499451670283571e-07}
0.714 (+/-0.418) for {'C': 398107170.5534974, 'kernel': 'rbf', 'gamma': 3.564511334262439e-07}
0.714 (+/-0.418) for {'C': 398107170.5534974, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.671 (+/-0.433) for {'C': 630957344.4801937, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.689 (+/-0.415) for {'C': 630957344.4801937, 'kernel': 'rbf', 'gamma': 3.076096814740707e-07}
0.684 (+/-0.426) for {'C': 630957344.4801937, 'kernel': 'rbf', 'gamma': 3.1332857243155833e-07}
0.687 (+/-0.424) for {'C': 630957344.4801937, 'kernel': 'rbf', 'gamma': 3.191537855100758e-07}
0.702 (+/-0.405) for {'C': 630957344.4801937, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.739 (+/-0.437) for {'C': 630957344.4801937, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.706 (+/-0.418) for {'C': 630957344.4801937, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.660 (+/-0.390) for {'C': 630957344.4801937, 'kernel': 'rbf', 'gamma': 3.4355794789987457e-07}
0.681 (+/-0.373) for {'C': 630957344.4801937, 'kernel': 'rbf', 'gamma': 3.499451670283571e-07}
0.714 (+/-0.418) for {'C': 630957344.4801937, 'kernel': 'rbf', 'gamma': 3.564511334262439e-07}
0.714 (+/-0.418) for {'C': 630957344.4801937, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.671 (+/-0.433) for {'C': 1000000000.0000011, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.687 (+/-0.416) for {'C': 1000000000.0000011, 'kernel': 'rbf', 'gamma': 3.076096814740707e-07}
0.681 (+/-0.428) for {'C': 1000000000.0000011, 'kernel': 'rbf', 'gamma': 3.1332857243155833e-07}
0.687 (+/-0.424) for {'C': 1000000000.0000011, 'kernel': 'rbf', 'gamma': 3.191537855100758e-07}
0.702 (+/-0.405) for {'C': 1000000000.0000011, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.739 (+/-0.437) for {'C': 1000000000.0000011, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.706 (+/-0.418) for {'C': 1000000000.0000011, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.660 (+/-0.390) for {'C': 1000000000.0000011, 'kernel': 'rbf', 'gamma': 3.4355794789987457e-07}
0.677 (+/-0.374) for {'C': 1000000000.0000011, 'kernel': 'rbf', 'gamma': 3.499451670283571e-07}
0.714 (+/-0.418) for {'C': 1000000000.0000011, 'kernel': 'rbf', 'gamma': 3.564511334262439e-07}
0.714 (+/-0.418) for {'C': 1000000000.0000011, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.671 (+/-0.433) for {'C': 1584893192.461112, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.687 (+/-0.416) for {'C': 1584893192.461112, 'kernel': 'rbf', 'gamma': 3.076096814740707e-07}
0.684 (+/-0.426) for {'C': 1584893192.461112, 'kernel': 'rbf', 'gamma': 3.1332857243155833e-07}
0.687 (+/-0.424) for {'C': 1584893192.461112, 'kernel': 'rbf', 'gamma': 3.191537855100758e-07}
0.702 (+/-0.405) for {'C': 1584893192.461112, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.739 (+/-0.437) for {'C': 1584893192.461112, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.706 (+/-0.418) for {'C': 1584893192.461112, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.660 (+/-0.390) for {'C': 1584893192.461112, 'kernel': 'rbf', 'gamma': 3.4355794789987457e-07}
0.677 (+/-0.374) for {'C': 1584893192.461112, 'kernel': 'rbf', 'gamma': 3.499451670283571e-07}
0.714 (+/-0.418) for {'C': 1584893192.461112, 'kernel': 'rbf', 'gamma': 3.564511334262439e-07}
0.714 (+/-0.418) for {'C': 1584893192.461112, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.671 (+/-0.433) for {'C': 2511886431.5095787, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.687 (+/-0.416) for {'C': 2511886431.5095787, 'kernel': 'rbf', 'gamma': 3.076096814740707e-07}
0.684 (+/-0.426) for {'C': 2511886431.5095787, 'kernel': 'rbf', 'gamma': 3.1332857243155833e-07}
0.687 (+/-0.424) for {'C': 2511886431.5095787, 'kernel': 'rbf', 'gamma': 3.191537855100758e-07}
0.702 (+/-0.405) for {'C': 2511886431.5095787, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.739 (+/-0.437) for {'C': 2511886431.5095787, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.706 (+/-0.418) for {'C': 2511886431.5095787, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.660 (+/-0.390) for {'C': 2511886431.5095787, 'kernel': 'rbf', 'gamma': 3.4355794789987457e-07}
0.677 (+/-0.374) for {'C': 2511886431.5095787, 'kernel': 'rbf', 'gamma': 3.499451670283571e-07}
0.714 (+/-0.418) for {'C': 2511886431.5095787, 'kernel': 'rbf', 'gamma': 3.564511334262439e-07}
0.714 (+/-0.418) for {'C': 2511886431.5095787, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.671 (+/-0.433) for {'C': 3981071705.534972, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.689 (+/-0.415) for {'C': 3981071705.534972, 'kernel': 'rbf', 'gamma': 3.076096814740707e-07}
0.684 (+/-0.426) for {'C': 3981071705.534972, 'kernel': 'rbf', 'gamma': 3.1332857243155833e-07}
0.687 (+/-0.424) for {'C': 3981071705.534972, 'kernel': 'rbf', 'gamma': 3.191537855100758e-07}
0.702 (+/-0.405) for {'C': 3981071705.534972, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.739 (+/-0.437) for {'C': 3981071705.534972, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.706 (+/-0.418) for {'C': 3981071705.534972, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.660 (+/-0.390) for {'C': 3981071705.534972, 'kernel': 'rbf', 'gamma': 3.4355794789987457e-07}
0.677 (+/-0.374) for {'C': 3981071705.534972, 'kernel': 'rbf', 'gamma': 3.499451670283571e-07}
0.714 (+/-0.418) for {'C': 3981071705.534972, 'kernel': 'rbf', 'gamma': 3.564511334262439e-07}
0.714 (+/-0.418) for {'C': 3981071705.534972, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.671 (+/-0.433) for {'C': 6309573444.8019495, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.689 (+/-0.415) for {'C': 6309573444.8019495, 'kernel': 'rbf', 'gamma': 3.076096814740707e-07}
0.681 (+/-0.428) for {'C': 6309573444.8019495, 'kernel': 'rbf', 'gamma': 3.1332857243155833e-07}
0.687 (+/-0.424) for {'C': 6309573444.8019495, 'kernel': 'rbf', 'gamma': 3.191537855100758e-07}
0.702 (+/-0.405) for {'C': 6309573444.8019495, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.739 (+/-0.437) for {'C': 6309573444.8019495, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.706 (+/-0.418) for {'C': 6309573444.8019495, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.660 (+/-0.390) for {'C': 6309573444.8019495, 'kernel': 'rbf', 'gamma': 3.4355794789987457e-07}
0.677 (+/-0.374) for {'C': 6309573444.8019495, 'kernel': 'rbf', 'gamma': 3.499451670283571e-07}
0.714 (+/-0.418) for {'C': 6309573444.8019495, 'kernel': 'rbf', 'gamma': 3.564511334262439e-07}
0.714 (+/-0.418) for {'C': 6309573444.8019495, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.671 (+/-0.433) for {'C': 10000000000.000008, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.687 (+/-0.417) for {'C': 10000000000.000008, 'kernel': 'rbf', 'gamma': 3.076096814740707e-07}
0.681 (+/-0.428) for {'C': 10000000000.000008, 'kernel': 'rbf', 'gamma': 3.1332857243155833e-07}
0.687 (+/-0.424) for {'C': 10000000000.000008, 'kernel': 'rbf', 'gamma': 3.191537855100758e-07}
0.702 (+/-0.405) for {'C': 10000000000.000008, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.739 (+/-0.437) for {'C': 10000000000.000008, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.706 (+/-0.418) for {'C': 10000000000.000008, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.660 (+/-0.390) for {'C': 10000000000.000008, 'kernel': 'rbf', 'gamma': 3.4355794789987457e-07}
0.702 (+/-0.421) for {'C': 10000000000.000008, 'kernel': 'rbf', 'gamma': 3.499451670283571e-07}
0.714 (+/-0.418) for {'C': 10000000000.000008, 'kernel': 'rbf', 'gamma': 3.564511334262439e-07}
0.714 (+/-0.418) for {'C': 10000000000.000008, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.706 (+/-0.383) for {'C': 10.0, 'kernel': 'linear'}
0.635 (+/-0.313) for {'C': 100.0, 'kernel': 'linear'}
0.632 (+/-0.309) for {'C': 1000.0, 'kernel': 'linear'}
0.629 (+/-0.312) for {'C': 10000.0, 'kernel': 'linear'}
0.624 (+/-0.319) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.664 (+/-0.314) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.681 (+/-0.295) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.076096814740707e-07}
0.665 (+/-0.316) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.1332857243155833e-07}
0.665 (+/-0.316) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.191537855100758e-07}
0.665 (+/-0.316) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.682 (+/-0.296) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.665 (+/-0.316) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.640 (+/-0.326) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.4355794789987457e-07}
0.665 (+/-0.316) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.499451670283571e-07}
0.666 (+/-0.316) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.564511334262439e-07}
0.666 (+/-0.316) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.664 (+/-0.314) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.681 (+/-0.294) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.076096814740707e-07}
0.665 (+/-0.316) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.1332857243155833e-07}
0.665 (+/-0.316) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.191537855100758e-07}
0.681 (+/-0.296) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.682 (+/-0.296) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.665 (+/-0.316) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.640 (+/-0.326) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.4355794789987457e-07}
0.665 (+/-0.316) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.499451670283571e-07}
0.666 (+/-0.316) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.564511334262439e-07}
0.666 (+/-0.316) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.664 (+/-0.314) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.681 (+/-0.295) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.076096814740707e-07}
0.665 (+/-0.316) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.1332857243155833e-07}
0.665 (+/-0.316) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.191537855100758e-07}
0.681 (+/-0.296) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.682 (+/-0.296) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.665 (+/-0.316) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.640 (+/-0.326) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.4355794789987457e-07}
0.665 (+/-0.316) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.499451670283571e-07}
0.666 (+/-0.316) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.564511334262439e-07}
0.666 (+/-0.316) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.664 (+/-0.314) for {'C': 398107170.5534974, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.681 (+/-0.294) for {'C': 398107170.5534974, 'kernel': 'rbf', 'gamma': 3.076096814740707e-07}
0.664 (+/-0.315) for {'C': 398107170.5534974, 'kernel': 'rbf', 'gamma': 3.1332857243155833e-07}
0.665 (+/-0.316) for {'C': 398107170.5534974, 'kernel': 'rbf', 'gamma': 3.191537855100758e-07}
0.681 (+/-0.296) for {'C': 398107170.5534974, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.682 (+/-0.296) for {'C': 398107170.5534974, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.665 (+/-0.316) for {'C': 398107170.5534974, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.640 (+/-0.326) for {'C': 398107170.5534974, 'kernel': 'rbf', 'gamma': 3.4355794789987457e-07}
0.665 (+/-0.316) for {'C': 398107170.5534974, 'kernel': 'rbf', 'gamma': 3.499451670283571e-07}
0.666 (+/-0.316) for {'C': 398107170.5534974, 'kernel': 'rbf', 'gamma': 3.564511334262439e-07}
0.666 (+/-0.316) for {'C': 398107170.5534974, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.664 (+/-0.314) for {'C': 630957344.4801937, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.681 (+/-0.294) for {'C': 630957344.4801937, 'kernel': 'rbf', 'gamma': 3.076096814740707e-07}
0.664 (+/-0.315) for {'C': 630957344.4801937, 'kernel': 'rbf', 'gamma': 3.1332857243155833e-07}
0.665 (+/-0.316) for {'C': 630957344.4801937, 'kernel': 'rbf', 'gamma': 3.191537855100758e-07}
0.681 (+/-0.296) for {'C': 630957344.4801937, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.682 (+/-0.296) for {'C': 630957344.4801937, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.665 (+/-0.316) for {'C': 630957344.4801937, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.640 (+/-0.326) for {'C': 630957344.4801937, 'kernel': 'rbf', 'gamma': 3.4355794789987457e-07}
0.665 (+/-0.316) for {'C': 630957344.4801937, 'kernel': 'rbf', 'gamma': 3.499451670283571e-07}
0.666 (+/-0.316) for {'C': 630957344.4801937, 'kernel': 'rbf', 'gamma': 3.564511334262439e-07}
0.666 (+/-0.316) for {'C': 630957344.4801937, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.664 (+/-0.314) for {'C': 1000000000.0000011, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.681 (+/-0.294) for {'C': 1000000000.0000011, 'kernel': 'rbf', 'gamma': 3.076096814740707e-07}
0.664 (+/-0.315) for {'C': 1000000000.0000011, 'kernel': 'rbf', 'gamma': 3.1332857243155833e-07}
0.665 (+/-0.316) for {'C': 1000000000.0000011, 'kernel': 'rbf', 'gamma': 3.191537855100758e-07}
0.681 (+/-0.296) for {'C': 1000000000.0000011, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.682 (+/-0.296) for {'C': 1000000000.0000011, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.665 (+/-0.316) for {'C': 1000000000.0000011, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.640 (+/-0.326) for {'C': 1000000000.0000011, 'kernel': 'rbf', 'gamma': 3.4355794789987457e-07}
0.665 (+/-0.316) for {'C': 1000000000.0000011, 'kernel': 'rbf', 'gamma': 3.499451670283571e-07}
0.666 (+/-0.316) for {'C': 1000000000.0000011, 'kernel': 'rbf', 'gamma': 3.564511334262439e-07}
0.666 (+/-0.316) for {'C': 1000000000.0000011, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.664 (+/-0.314) for {'C': 1584893192.461112, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.681 (+/-0.294) for {'C': 1584893192.461112, 'kernel': 'rbf', 'gamma': 3.076096814740707e-07}
0.664 (+/-0.315) for {'C': 1584893192.461112, 'kernel': 'rbf', 'gamma': 3.1332857243155833e-07}
0.665 (+/-0.316) for {'C': 1584893192.461112, 'kernel': 'rbf', 'gamma': 3.191537855100758e-07}
0.681 (+/-0.296) for {'C': 1584893192.461112, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.682 (+/-0.296) for {'C': 1584893192.461112, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.665 (+/-0.316) for {'C': 1584893192.461112, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.640 (+/-0.326) for {'C': 1584893192.461112, 'kernel': 'rbf', 'gamma': 3.4355794789987457e-07}
0.665 (+/-0.316) for {'C': 1584893192.461112, 'kernel': 'rbf', 'gamma': 3.499451670283571e-07}
0.666 (+/-0.316) for {'C': 1584893192.461112, 'kernel': 'rbf', 'gamma': 3.564511334262439e-07}
0.666 (+/-0.316) for {'C': 1584893192.461112, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.664 (+/-0.314) for {'C': 2511886431.5095787, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.681 (+/-0.294) for {'C': 2511886431.5095787, 'kernel': 'rbf', 'gamma': 3.076096814740707e-07}
0.664 (+/-0.315) for {'C': 2511886431.5095787, 'kernel': 'rbf', 'gamma': 3.1332857243155833e-07}
0.665 (+/-0.316) for {'C': 2511886431.5095787, 'kernel': 'rbf', 'gamma': 3.191537855100758e-07}
0.681 (+/-0.296) for {'C': 2511886431.5095787, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.682 (+/-0.296) for {'C': 2511886431.5095787, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.665 (+/-0.316) for {'C': 2511886431.5095787, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.640 (+/-0.326) for {'C': 2511886431.5095787, 'kernel': 'rbf', 'gamma': 3.4355794789987457e-07}
0.665 (+/-0.316) for {'C': 2511886431.5095787, 'kernel': 'rbf', 'gamma': 3.499451670283571e-07}
0.666 (+/-0.316) for {'C': 2511886431.5095787, 'kernel': 'rbf', 'gamma': 3.564511334262439e-07}
0.666 (+/-0.316) for {'C': 2511886431.5095787, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.664 (+/-0.314) for {'C': 3981071705.534972, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.681 (+/-0.294) for {'C': 3981071705.534972, 'kernel': 'rbf', 'gamma': 3.076096814740707e-07}
0.664 (+/-0.315) for {'C': 3981071705.534972, 'kernel': 'rbf', 'gamma': 3.1332857243155833e-07}
0.665 (+/-0.316) for {'C': 3981071705.534972, 'kernel': 'rbf', 'gamma': 3.191537855100758e-07}
0.681 (+/-0.296) for {'C': 3981071705.534972, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.682 (+/-0.296) for {'C': 3981071705.534972, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.665 (+/-0.316) for {'C': 3981071705.534972, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.640 (+/-0.326) for {'C': 3981071705.534972, 'kernel': 'rbf', 'gamma': 3.4355794789987457e-07}
0.665 (+/-0.316) for {'C': 3981071705.534972, 'kernel': 'rbf', 'gamma': 3.499451670283571e-07}
0.666 (+/-0.316) for {'C': 3981071705.534972, 'kernel': 'rbf', 'gamma': 3.564511334262439e-07}
0.666 (+/-0.316) for {'C': 3981071705.534972, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.664 (+/-0.314) for {'C': 6309573444.8019495, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.681 (+/-0.294) for {'C': 6309573444.8019495, 'kernel': 'rbf', 'gamma': 3.076096814740707e-07}
0.664 (+/-0.315) for {'C': 6309573444.8019495, 'kernel': 'rbf', 'gamma': 3.1332857243155833e-07}
0.665 (+/-0.316) for {'C': 6309573444.8019495, 'kernel': 'rbf', 'gamma': 3.191537855100758e-07}
0.681 (+/-0.296) for {'C': 6309573444.8019495, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.682 (+/-0.296) for {'C': 6309573444.8019495, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.665 (+/-0.316) for {'C': 6309573444.8019495, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.640 (+/-0.326) for {'C': 6309573444.8019495, 'kernel': 'rbf', 'gamma': 3.4355794789987457e-07}
0.665 (+/-0.316) for {'C': 6309573444.8019495, 'kernel': 'rbf', 'gamma': 3.499451670283571e-07}
0.666 (+/-0.316) for {'C': 6309573444.8019495, 'kernel': 'rbf', 'gamma': 3.564511334262439e-07}
0.666 (+/-0.316) for {'C': 6309573444.8019495, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.664 (+/-0.314) for {'C': 10000000000.000008, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.680 (+/-0.294) for {'C': 10000000000.000008, 'kernel': 'rbf', 'gamma': 3.076096814740707e-07}
0.664 (+/-0.315) for {'C': 10000000000.000008, 'kernel': 'rbf', 'gamma': 3.1332857243155833e-07}
0.665 (+/-0.316) for {'C': 10000000000.000008, 'kernel': 'rbf', 'gamma': 3.191537855100758e-07}
0.681 (+/-0.296) for {'C': 10000000000.000008, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.682 (+/-0.296) for {'C': 10000000000.000008, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.665 (+/-0.316) for {'C': 10000000000.000008, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.640 (+/-0.326) for {'C': 10000000000.000008, 'kernel': 'rbf', 'gamma': 3.4355794789987457e-07}
0.665 (+/-0.316) for {'C': 10000000000.000008, 'kernel': 'rbf', 'gamma': 3.499451670283571e-07}
0.666 (+/-0.316) for {'C': 10000000000.000008, 'kernel': 'rbf', 'gamma': 3.564511334262439e-07}
0.666 (+/-0.316) for {'C': 10000000000.000008, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 10.0, 'kernel': 'linear'}
0.636 (+/-0.237) for {'C': 100.0, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.636 (+/-0.240) for {'C': 10000.0, 'kernel': 'linear'}
0.610 (+/-0.245) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6820497361695111
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.644 (+/-0.381) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.615 (+/-0.298) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.076096814740707e-07}
0.644 (+/-0.382) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.1332857243155833e-07}
0.630 (+/-0.325) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.191537855100758e-07}
0.620 (+/-0.309) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.625 (+/-0.305) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.617 (+/-0.292) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.610 (+/-0.296) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.4355794789987457e-07}
0.608 (+/-0.300) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.499451670283571e-07}
0.609 (+/-0.300) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.564511334262439e-07}
0.587 (+/-0.313) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.643 (+/-0.382) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.614 (+/-0.299) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.076096814740707e-07}
0.657 (+/-0.370) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.1332857243155833e-07}
0.622 (+/-0.308) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.191537855100758e-07}
0.620 (+/-0.310) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.630 (+/-0.303) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.617 (+/-0.292) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.610 (+/-0.296) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.4355794789987457e-07}
0.607 (+/-0.300) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.499451670283571e-07}
0.609 (+/-0.300) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.564511334262439e-07}
0.587 (+/-0.313) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.643 (+/-0.382) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.614 (+/-0.299) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.076096814740707e-07}
0.657 (+/-0.370) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.1332857243155833e-07}
0.621 (+/-0.309) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.191537855100758e-07}
0.619 (+/-0.310) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.630 (+/-0.303) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.617 (+/-0.292) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.610 (+/-0.296) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.4355794789987457e-07}
0.605 (+/-0.301) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.499451670283571e-07}
0.609 (+/-0.300) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.564511334262439e-07}
0.586 (+/-0.313) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.643 (+/-0.382) for {'C': 398107170.5534974, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.623 (+/-0.308) for {'C': 398107170.5534974, 'kernel': 'rbf', 'gamma': 3.076096814740707e-07}
0.657 (+/-0.370) for {'C': 398107170.5534974, 'kernel': 'rbf', 'gamma': 3.1332857243155833e-07}
0.621 (+/-0.309) for {'C': 398107170.5534974, 'kernel': 'rbf', 'gamma': 3.191537855100758e-07}
0.619 (+/-0.310) for {'C': 398107170.5534974, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.629 (+/-0.304) for {'C': 398107170.5534974, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.617 (+/-0.292) for {'C': 398107170.5534974, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.610 (+/-0.296) for {'C': 398107170.5534974, 'kernel': 'rbf', 'gamma': 3.4355794789987457e-07}
0.606 (+/-0.301) for {'C': 398107170.5534974, 'kernel': 'rbf', 'gamma': 3.499451670283571e-07}
0.609 (+/-0.300) for {'C': 398107170.5534974, 'kernel': 'rbf', 'gamma': 3.564511334262439e-07}
0.586 (+/-0.313) for {'C': 398107170.5534974, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.643 (+/-0.382) for {'C': 630957344.4801937, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.614 (+/-0.299) for {'C': 630957344.4801937, 'kernel': 'rbf', 'gamma': 3.076096814740707e-07}
0.656 (+/-0.371) for {'C': 630957344.4801937, 'kernel': 'rbf', 'gamma': 3.1332857243155833e-07}
0.621 (+/-0.309) for {'C': 630957344.4801937, 'kernel': 'rbf', 'gamma': 3.191537855100758e-07}
0.619 (+/-0.310) for {'C': 630957344.4801937, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.625 (+/-0.306) for {'C': 630957344.4801937, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.617 (+/-0.292) for {'C': 630957344.4801937, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.609 (+/-0.297) for {'C': 630957344.4801937, 'kernel': 'rbf', 'gamma': 3.4355794789987457e-07}
0.606 (+/-0.301) for {'C': 630957344.4801937, 'kernel': 'rbf', 'gamma': 3.499451670283571e-07}
0.609 (+/-0.300) for {'C': 630957344.4801937, 'kernel': 'rbf', 'gamma': 3.564511334262439e-07}
0.586 (+/-0.313) for {'C': 630957344.4801937, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.643 (+/-0.382) for {'C': 1000000000.0000011, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.614 (+/-0.299) for {'C': 1000000000.0000011, 'kernel': 'rbf', 'gamma': 3.076096814740707e-07}
0.656 (+/-0.371) for {'C': 1000000000.0000011, 'kernel': 'rbf', 'gamma': 3.1332857243155833e-07}
0.621 (+/-0.309) for {'C': 1000000000.0000011, 'kernel': 'rbf', 'gamma': 3.191537855100758e-07}
0.619 (+/-0.310) for {'C': 1000000000.0000011, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.629 (+/-0.304) for {'C': 1000000000.0000011, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.617 (+/-0.292) for {'C': 1000000000.0000011, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.609 (+/-0.297) for {'C': 1000000000.0000011, 'kernel': 'rbf', 'gamma': 3.4355794789987457e-07}
0.605 (+/-0.301) for {'C': 1000000000.0000011, 'kernel': 'rbf', 'gamma': 3.499451670283571e-07}
0.609 (+/-0.300) for {'C': 1000000000.0000011, 'kernel': 'rbf', 'gamma': 3.564511334262439e-07}
0.586 (+/-0.313) for {'C': 1000000000.0000011, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.643 (+/-0.382) for {'C': 1584893192.461112, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.614 (+/-0.299) for {'C': 1584893192.461112, 'kernel': 'rbf', 'gamma': 3.076096814740707e-07}
0.655 (+/-0.372) for {'C': 1584893192.461112, 'kernel': 'rbf', 'gamma': 3.1332857243155833e-07}
0.621 (+/-0.309) for {'C': 1584893192.461112, 'kernel': 'rbf', 'gamma': 3.191537855100758e-07}
0.619 (+/-0.310) for {'C': 1584893192.461112, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.625 (+/-0.306) for {'C': 1584893192.461112, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.614 (+/-0.292) for {'C': 1584893192.461112, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.609 (+/-0.297) for {'C': 1584893192.461112, 'kernel': 'rbf', 'gamma': 3.4355794789987457e-07}
0.606 (+/-0.301) for {'C': 1584893192.461112, 'kernel': 'rbf', 'gamma': 3.499451670283571e-07}
0.608 (+/-0.301) for {'C': 1584893192.461112, 'kernel': 'rbf', 'gamma': 3.564511334262439e-07}
0.586 (+/-0.313) for {'C': 1584893192.461112, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.643 (+/-0.382) for {'C': 2511886431.5095787, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.614 (+/-0.299) for {'C': 2511886431.5095787, 'kernel': 'rbf', 'gamma': 3.076096814740707e-07}
0.656 (+/-0.371) for {'C': 2511886431.5095787, 'kernel': 'rbf', 'gamma': 3.1332857243155833e-07}
0.621 (+/-0.309) for {'C': 2511886431.5095787, 'kernel': 'rbf', 'gamma': 3.191537855100758e-07}
0.619 (+/-0.310) for {'C': 2511886431.5095787, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.625 (+/-0.306) for {'C': 2511886431.5095787, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.614 (+/-0.292) for {'C': 2511886431.5095787, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.609 (+/-0.297) for {'C': 2511886431.5095787, 'kernel': 'rbf', 'gamma': 3.4355794789987457e-07}
0.606 (+/-0.301) for {'C': 2511886431.5095787, 'kernel': 'rbf', 'gamma': 3.499451670283571e-07}
0.608 (+/-0.301) for {'C': 2511886431.5095787, 'kernel': 'rbf', 'gamma': 3.564511334262439e-07}
0.586 (+/-0.313) for {'C': 2511886431.5095787, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.643 (+/-0.382) for {'C': 3981071705.534972, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.614 (+/-0.299) for {'C': 3981071705.534972, 'kernel': 'rbf', 'gamma': 3.076096814740707e-07}
0.655 (+/-0.372) for {'C': 3981071705.534972, 'kernel': 'rbf', 'gamma': 3.1332857243155833e-07}
0.621 (+/-0.309) for {'C': 3981071705.534972, 'kernel': 'rbf', 'gamma': 3.191537855100758e-07}
0.619 (+/-0.310) for {'C': 3981071705.534972, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.625 (+/-0.306) for {'C': 3981071705.534972, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.614 (+/-0.292) for {'C': 3981071705.534972, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.609 (+/-0.297) for {'C': 3981071705.534972, 'kernel': 'rbf', 'gamma': 3.4355794789987457e-07}
0.605 (+/-0.301) for {'C': 3981071705.534972, 'kernel': 'rbf', 'gamma': 3.499451670283571e-07}
0.608 (+/-0.301) for {'C': 3981071705.534972, 'kernel': 'rbf', 'gamma': 3.564511334262439e-07}
0.586 (+/-0.313) for {'C': 3981071705.534972, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.643 (+/-0.382) for {'C': 6309573444.8019495, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.614 (+/-0.299) for {'C': 6309573444.8019495, 'kernel': 'rbf', 'gamma': 3.076096814740707e-07}
0.655 (+/-0.372) for {'C': 6309573444.8019495, 'kernel': 'rbf', 'gamma': 3.1332857243155833e-07}
0.621 (+/-0.309) for {'C': 6309573444.8019495, 'kernel': 'rbf', 'gamma': 3.191537855100758e-07}
0.619 (+/-0.310) for {'C': 6309573444.8019495, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.629 (+/-0.304) for {'C': 6309573444.8019495, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.617 (+/-0.292) for {'C': 6309573444.8019495, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.609 (+/-0.297) for {'C': 6309573444.8019495, 'kernel': 'rbf', 'gamma': 3.4355794789987457e-07}
0.605 (+/-0.302) for {'C': 6309573444.8019495, 'kernel': 'rbf', 'gamma': 3.499451670283571e-07}
0.608 (+/-0.301) for {'C': 6309573444.8019495, 'kernel': 'rbf', 'gamma': 3.564511334262439e-07}
0.586 (+/-0.313) for {'C': 6309573444.8019495, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.643 (+/-0.382) for {'C': 10000000000.000008, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.614 (+/-0.299) for {'C': 10000000000.000008, 'kernel': 'rbf', 'gamma': 3.076096814740707e-07}
0.655 (+/-0.372) for {'C': 10000000000.000008, 'kernel': 'rbf', 'gamma': 3.1332857243155833e-07}
0.621 (+/-0.309) for {'C': 10000000000.000008, 'kernel': 'rbf', 'gamma': 3.191537855100758e-07}
0.619 (+/-0.310) for {'C': 10000000000.000008, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.625 (+/-0.306) for {'C': 10000000000.000008, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.617 (+/-0.292) for {'C': 10000000000.000008, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.609 (+/-0.297) for {'C': 10000000000.000008, 'kernel': 'rbf', 'gamma': 3.4355794789987457e-07}
0.605 (+/-0.302) for {'C': 10000000000.000008, 'kernel': 'rbf', 'gamma': 3.499451670283571e-07}
0.608 (+/-0.301) for {'C': 10000000000.000008, 'kernel': 'rbf', 'gamma': 3.564511334262439e-07}
0.586 (+/-0.313) for {'C': 10000000000.000008, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.666 (+/-0.379) for {'C': 10.0, 'kernel': 'linear'}
0.629 (+/-0.313) for {'C': 100.0, 'kernel': 'linear'}
0.632 (+/-0.309) for {'C': 1000.0, 'kernel': 'linear'}
0.629 (+/-0.312) for {'C': 10000.0, 'kernel': 'linear'}
0.624 (+/-0.319) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.20      0.17      0.18         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.99      0.99       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.677 (+/-0.292) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.677 (+/-0.291) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.076096814740707e-07}
0.663 (+/-0.314) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.1332857243155833e-07}
0.678 (+/-0.293) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.191537855100758e-07}
0.677 (+/-0.292) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.678 (+/-0.293) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.678 (+/-0.285) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.675 (+/-0.276) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.4355794789987457e-07}
0.661 (+/-0.309) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.499451670283571e-07}
0.661 (+/-0.302) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.564511334262439e-07}
0.611 (+/-0.312) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.677 (+/-0.292) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.677 (+/-0.291) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.076096814740707e-07}
0.680 (+/-0.294) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.1332857243155833e-07}
0.678 (+/-0.292) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.191537855100758e-07}
0.677 (+/-0.292) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.695 (+/-0.304) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.678 (+/-0.285) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.675 (+/-0.275) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.4355794789987457e-07}
0.661 (+/-0.308) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.499451670283571e-07}
0.661 (+/-0.302) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.564511334262439e-07}
0.611 (+/-0.312) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.677 (+/-0.292) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.677 (+/-0.291) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.076096814740707e-07}
0.680 (+/-0.294) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.1332857243155833e-07}
0.678 (+/-0.292) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.191537855100758e-07}
0.677 (+/-0.291) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.695 (+/-0.304) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.678 (+/-0.285) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.675 (+/-0.275) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.4355794789987457e-07}
0.660 (+/-0.308) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.499451670283571e-07}
0.661 (+/-0.302) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.564511334262439e-07}
0.611 (+/-0.311) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.677 (+/-0.292) for {'C': 398107170.5534974, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.677 (+/-0.291) for {'C': 398107170.5534974, 'kernel': 'rbf', 'gamma': 3.076096814740707e-07}
0.680 (+/-0.294) for {'C': 398107170.5534974, 'kernel': 'rbf', 'gamma': 3.1332857243155833e-07}
0.678 (+/-0.292) for {'C': 398107170.5534974, 'kernel': 'rbf', 'gamma': 3.191537855100758e-07}
0.677 (+/-0.291) for {'C': 398107170.5534974, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.695 (+/-0.304) for {'C': 398107170.5534974, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.678 (+/-0.285) for {'C': 398107170.5534974, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.675 (+/-0.275) for {'C': 398107170.5534974, 'kernel': 'rbf', 'gamma': 3.4355794789987457e-07}
0.660 (+/-0.308) for {'C': 398107170.5534974, 'kernel': 'rbf', 'gamma': 3.499451670283571e-07}
0.661 (+/-0.302) for {'C': 398107170.5534974, 'kernel': 'rbf', 'gamma': 3.564511334262439e-07}
0.611 (+/-0.311) for {'C': 398107170.5534974, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.677 (+/-0.292) for {'C': 630957344.4801937, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.677 (+/-0.291) for {'C': 630957344.4801937, 'kernel': 'rbf', 'gamma': 3.076096814740707e-07}
0.679 (+/-0.294) for {'C': 630957344.4801937, 'kernel': 'rbf', 'gamma': 3.1332857243155833e-07}
0.678 (+/-0.292) for {'C': 630957344.4801937, 'kernel': 'rbf', 'gamma': 3.191537855100758e-07}
0.677 (+/-0.291) for {'C': 630957344.4801937, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.678 (+/-0.292) for {'C': 630957344.4801937, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.678 (+/-0.285) for {'C': 630957344.4801937, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.675 (+/-0.275) for {'C': 630957344.4801937, 'kernel': 'rbf', 'gamma': 3.4355794789987457e-07}
0.660 (+/-0.308) for {'C': 630957344.4801937, 'kernel': 'rbf', 'gamma': 3.499451670283571e-07}
0.661 (+/-0.302) for {'C': 630957344.4801937, 'kernel': 'rbf', 'gamma': 3.564511334262439e-07}
0.611 (+/-0.311) for {'C': 630957344.4801937, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.677 (+/-0.292) for {'C': 1000000000.0000011, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.677 (+/-0.291) for {'C': 1000000000.0000011, 'kernel': 'rbf', 'gamma': 3.076096814740707e-07}
0.679 (+/-0.294) for {'C': 1000000000.0000011, 'kernel': 'rbf', 'gamma': 3.1332857243155833e-07}
0.678 (+/-0.292) for {'C': 1000000000.0000011, 'kernel': 'rbf', 'gamma': 3.191537855100758e-07}
0.677 (+/-0.291) for {'C': 1000000000.0000011, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.695 (+/-0.304) for {'C': 1000000000.0000011, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.678 (+/-0.285) for {'C': 1000000000.0000011, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.675 (+/-0.275) for {'C': 1000000000.0000011, 'kernel': 'rbf', 'gamma': 3.4355794789987457e-07}
0.660 (+/-0.308) for {'C': 1000000000.0000011, 'kernel': 'rbf', 'gamma': 3.499451670283571e-07}
0.661 (+/-0.302) for {'C': 1000000000.0000011, 'kernel': 'rbf', 'gamma': 3.564511334262439e-07}
0.611 (+/-0.311) for {'C': 1000000000.0000011, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.677 (+/-0.292) for {'C': 1584893192.461112, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.677 (+/-0.291) for {'C': 1584893192.461112, 'kernel': 'rbf', 'gamma': 3.076096814740707e-07}
0.679 (+/-0.294) for {'C': 1584893192.461112, 'kernel': 'rbf', 'gamma': 3.1332857243155833e-07}
0.678 (+/-0.292) for {'C': 1584893192.461112, 'kernel': 'rbf', 'gamma': 3.191537855100758e-07}
0.677 (+/-0.291) for {'C': 1584893192.461112, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.678 (+/-0.292) for {'C': 1584893192.461112, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.677 (+/-0.285) for {'C': 1584893192.461112, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.675 (+/-0.275) for {'C': 1584893192.461112, 'kernel': 'rbf', 'gamma': 3.4355794789987457e-07}
0.660 (+/-0.308) for {'C': 1584893192.461112, 'kernel': 'rbf', 'gamma': 3.499451670283571e-07}
0.660 (+/-0.302) for {'C': 1584893192.461112, 'kernel': 'rbf', 'gamma': 3.564511334262439e-07}
0.611 (+/-0.310) for {'C': 1584893192.461112, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.677 (+/-0.292) for {'C': 2511886431.5095787, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.677 (+/-0.291) for {'C': 2511886431.5095787, 'kernel': 'rbf', 'gamma': 3.076096814740707e-07}
0.679 (+/-0.294) for {'C': 2511886431.5095787, 'kernel': 'rbf', 'gamma': 3.1332857243155833e-07}
0.678 (+/-0.292) for {'C': 2511886431.5095787, 'kernel': 'rbf', 'gamma': 3.191537855100758e-07}
0.677 (+/-0.291) for {'C': 2511886431.5095787, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.678 (+/-0.292) for {'C': 2511886431.5095787, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.677 (+/-0.285) for {'C': 2511886431.5095787, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.675 (+/-0.275) for {'C': 2511886431.5095787, 'kernel': 'rbf', 'gamma': 3.4355794789987457e-07}
0.660 (+/-0.308) for {'C': 2511886431.5095787, 'kernel': 'rbf', 'gamma': 3.499451670283571e-07}
0.660 (+/-0.302) for {'C': 2511886431.5095787, 'kernel': 'rbf', 'gamma': 3.564511334262439e-07}
0.611 (+/-0.310) for {'C': 2511886431.5095787, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.677 (+/-0.292) for {'C': 3981071705.534972, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.677 (+/-0.291) for {'C': 3981071705.534972, 'kernel': 'rbf', 'gamma': 3.076096814740707e-07}
0.679 (+/-0.294) for {'C': 3981071705.534972, 'kernel': 'rbf', 'gamma': 3.1332857243155833e-07}
0.678 (+/-0.292) for {'C': 3981071705.534972, 'kernel': 'rbf', 'gamma': 3.191537855100758e-07}
0.677 (+/-0.291) for {'C': 3981071705.534972, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.678 (+/-0.292) for {'C': 3981071705.534972, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.677 (+/-0.285) for {'C': 3981071705.534972, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.675 (+/-0.275) for {'C': 3981071705.534972, 'kernel': 'rbf', 'gamma': 3.4355794789987457e-07}
0.660 (+/-0.308) for {'C': 3981071705.534972, 'kernel': 'rbf', 'gamma': 3.499451670283571e-07}
0.660 (+/-0.302) for {'C': 3981071705.534972, 'kernel': 'rbf', 'gamma': 3.564511334262439e-07}
0.611 (+/-0.310) for {'C': 3981071705.534972, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.677 (+/-0.292) for {'C': 6309573444.8019495, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.677 (+/-0.291) for {'C': 6309573444.8019495, 'kernel': 'rbf', 'gamma': 3.076096814740707e-07}
0.679 (+/-0.294) for {'C': 6309573444.8019495, 'kernel': 'rbf', 'gamma': 3.1332857243155833e-07}
0.678 (+/-0.292) for {'C': 6309573444.8019495, 'kernel': 'rbf', 'gamma': 3.191537855100758e-07}
0.677 (+/-0.291) for {'C': 6309573444.8019495, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.695 (+/-0.304) for {'C': 6309573444.8019495, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.678 (+/-0.285) for {'C': 6309573444.8019495, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.675 (+/-0.275) for {'C': 6309573444.8019495, 'kernel': 'rbf', 'gamma': 3.4355794789987457e-07}
0.660 (+/-0.307) for {'C': 6309573444.8019495, 'kernel': 'rbf', 'gamma': 3.499451670283571e-07}
0.660 (+/-0.302) for {'C': 6309573444.8019495, 'kernel': 'rbf', 'gamma': 3.564511334262439e-07}
0.611 (+/-0.311) for {'C': 6309573444.8019495, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.677 (+/-0.292) for {'C': 10000000000.000008, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.677 (+/-0.291) for {'C': 10000000000.000008, 'kernel': 'rbf', 'gamma': 3.076096814740707e-07}
0.679 (+/-0.294) for {'C': 10000000000.000008, 'kernel': 'rbf', 'gamma': 3.1332857243155833e-07}
0.678 (+/-0.292) for {'C': 10000000000.000008, 'kernel': 'rbf', 'gamma': 3.191537855100758e-07}
0.677 (+/-0.291) for {'C': 10000000000.000008, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.678 (+/-0.292) for {'C': 10000000000.000008, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.678 (+/-0.285) for {'C': 10000000000.000008, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.675 (+/-0.275) for {'C': 10000000000.000008, 'kernel': 'rbf', 'gamma': 3.4355794789987457e-07}
0.660 (+/-0.307) for {'C': 10000000000.000008, 'kernel': 'rbf', 'gamma': 3.499451670283571e-07}
0.660 (+/-0.302) for {'C': 10000000000.000008, 'kernel': 'rbf', 'gamma': 3.564511334262439e-07}
0.611 (+/-0.310) for {'C': 10000000000.000008, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.662 (+/-0.314) for {'C': 10.0, 'kernel': 'linear'}
0.660 (+/-0.315) for {'C': 100.0, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.636 (+/-0.240) for {'C': 10000.0, 'kernel': 'linear'}
0.610 (+/-0.245) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6948687031082529
第1轮loop中，tunemodel函数得到的最优参数是： ([{'C': array([1.00000000e+08, 1.09647820e+08, 1.20226443e+08, 1.31825674e+08,
       1.44543977e+08, 1.58489319e+08, 1.73780083e+08, 1.90546072e+08,
       2.08929613e+08, 2.29086765e+08, 2.51188643e+08]), 'kernel': ['rbf'], 'gamma': array([3.25087297e-07, 3.26287172e-07, 3.27491476e-07, 3.28700224e-07,
       3.29913434e-07, 3.31131121e-07, 3.32353304e-07, 3.33579997e-07,
       3.34811217e-07, 3.36046982e-07, 3.37287309e-07])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}], {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}, 0.6948687031082529)
这是第1次迭代微调C和gamma。
第1次迭代，得到delta： [8.41510681e+08 0.00000000e+00]
执行tunemodel()函数前，使用的grid search parameter是：
 [{'C': array([1.00000000e+08, 1.09647820e+08, 1.20226443e+08, 1.31825674e+08,
       1.44543977e+08, 1.58489319e+08, 1.73780083e+08, 1.90546072e+08,
       2.08929613e+08, 2.29086765e+08, 2.51188643e+08]), 'kernel': ['rbf'], 'gamma': array([3.25087297e-07, 3.26287172e-07, 3.27491476e-07, 3.28700224e-07,
       3.29913434e-07, 3.31131121e-07, 3.32353304e-07, 3.33579997e-07,
       3.34811217e-07, 3.36046982e-07, 3.37287309e-07])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.689 (+/-0.422) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.687 (+/-0.424) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.2628717214308483e-07}
0.689 (+/-0.422) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.2749147555557826e-07}
0.706 (+/-0.403) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.287002239687748e-07}
0.739 (+/-0.437) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.299134337888643e-07}
0.739 (+/-0.437) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.739 (+/-0.437) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.3235330357747726e-07}
0.689 (+/-0.421) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.3357999666204803e-07}
0.744 (+/-0.433) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.3481121738605415e-07}
0.694 (+/-0.355) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.3604698246069916e-07}
0.706 (+/-0.418) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.689 (+/-0.422) for {'C': 109647819.61431846, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.687 (+/-0.424) for {'C': 109647819.61431846, 'kernel': 'rbf', 'gamma': 3.2628717214308483e-07}
0.689 (+/-0.422) for {'C': 109647819.61431846, 'kernel': 'rbf', 'gamma': 3.2749147555557826e-07}
0.706 (+/-0.403) for {'C': 109647819.61431846, 'kernel': 'rbf', 'gamma': 3.287002239687748e-07}
0.739 (+/-0.437) for {'C': 109647819.61431846, 'kernel': 'rbf', 'gamma': 3.299134337888643e-07}
0.739 (+/-0.437) for {'C': 109647819.61431846, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.739 (+/-0.437) for {'C': 109647819.61431846, 'kernel': 'rbf', 'gamma': 3.3235330357747726e-07}
0.689 (+/-0.421) for {'C': 109647819.61431846, 'kernel': 'rbf', 'gamma': 3.3357999666204803e-07}
0.743 (+/-0.433) for {'C': 109647819.61431846, 'kernel': 'rbf', 'gamma': 3.3481121738605415e-07}
0.694 (+/-0.355) for {'C': 109647819.61431846, 'kernel': 'rbf', 'gamma': 3.3604698246069916e-07}
0.706 (+/-0.418) for {'C': 109647819.61431846, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.702 (+/-0.405) for {'C': 120226443.46174131, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.687 (+/-0.424) for {'C': 120226443.46174131, 'kernel': 'rbf', 'gamma': 3.2628717214308483e-07}
0.689 (+/-0.422) for {'C': 120226443.46174131, 'kernel': 'rbf', 'gamma': 3.2749147555557826e-07}
0.706 (+/-0.403) for {'C': 120226443.46174131, 'kernel': 'rbf', 'gamma': 3.287002239687748e-07}
0.739 (+/-0.437) for {'C': 120226443.46174131, 'kernel': 'rbf', 'gamma': 3.299134337888643e-07}
0.739 (+/-0.437) for {'C': 120226443.46174131, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.739 (+/-0.437) for {'C': 120226443.46174131, 'kernel': 'rbf', 'gamma': 3.3235330357747726e-07}
0.739 (+/-0.437) for {'C': 120226443.46174131, 'kernel': 'rbf', 'gamma': 3.3357999666204803e-07}
0.744 (+/-0.433) for {'C': 120226443.46174131, 'kernel': 'rbf', 'gamma': 3.3481121738605415e-07}
0.694 (+/-0.355) for {'C': 120226443.46174131, 'kernel': 'rbf', 'gamma': 3.3604698246069916e-07}
0.706 (+/-0.418) for {'C': 120226443.46174131, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.702 (+/-0.405) for {'C': 131825673.85564049, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.687 (+/-0.424) for {'C': 131825673.85564049, 'kernel': 'rbf', 'gamma': 3.2628717214308483e-07}
0.689 (+/-0.422) for {'C': 131825673.85564049, 'kernel': 'rbf', 'gamma': 3.2749147555557826e-07}
0.706 (+/-0.403) for {'C': 131825673.85564049, 'kernel': 'rbf', 'gamma': 3.287002239687748e-07}
0.739 (+/-0.437) for {'C': 131825673.85564049, 'kernel': 'rbf', 'gamma': 3.299134337888643e-07}
0.739 (+/-0.437) for {'C': 131825673.85564049, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.739 (+/-0.437) for {'C': 131825673.85564049, 'kernel': 'rbf', 'gamma': 3.3235330357747726e-07}
0.689 (+/-0.421) for {'C': 131825673.85564049, 'kernel': 'rbf', 'gamma': 3.3357999666204803e-07}
0.743 (+/-0.433) for {'C': 131825673.85564049, 'kernel': 'rbf', 'gamma': 3.3481121738605415e-07}
0.694 (+/-0.355) for {'C': 131825673.85564049, 'kernel': 'rbf', 'gamma': 3.3604698246069916e-07}
0.706 (+/-0.418) for {'C': 131825673.85564049, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.689 (+/-0.422) for {'C': 144543977.0745926, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.687 (+/-0.424) for {'C': 144543977.0745926, 'kernel': 'rbf', 'gamma': 3.2628717214308483e-07}
0.689 (+/-0.422) for {'C': 144543977.0745926, 'kernel': 'rbf', 'gamma': 3.2749147555557826e-07}
0.706 (+/-0.403) for {'C': 144543977.0745926, 'kernel': 'rbf', 'gamma': 3.287002239687748e-07}
0.739 (+/-0.437) for {'C': 144543977.0745926, 'kernel': 'rbf', 'gamma': 3.299134337888643e-07}
0.739 (+/-0.437) for {'C': 144543977.0745926, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.739 (+/-0.437) for {'C': 144543977.0745926, 'kernel': 'rbf', 'gamma': 3.3235330357747726e-07}
0.689 (+/-0.421) for {'C': 144543977.0745926, 'kernel': 'rbf', 'gamma': 3.3357999666204803e-07}
0.744 (+/-0.433) for {'C': 144543977.0745926, 'kernel': 'rbf', 'gamma': 3.3481121738605415e-07}
0.694 (+/-0.355) for {'C': 144543977.0745926, 'kernel': 'rbf', 'gamma': 3.3604698246069916e-07}
0.706 (+/-0.418) for {'C': 144543977.0745926, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.702 (+/-0.405) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.687 (+/-0.424) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.2628717214308483e-07}
0.689 (+/-0.422) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.2749147555557826e-07}
0.706 (+/-0.403) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.287002239687748e-07}
0.739 (+/-0.437) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.299134337888643e-07}
0.739 (+/-0.437) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.739 (+/-0.437) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.3235330357747726e-07}
0.689 (+/-0.421) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.3357999666204803e-07}
0.743 (+/-0.433) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.3481121738605415e-07}
0.694 (+/-0.355) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.3604698246069916e-07}
0.706 (+/-0.418) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.702 (+/-0.405) for {'C': 173780082.87493756, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.687 (+/-0.424) for {'C': 173780082.87493756, 'kernel': 'rbf', 'gamma': 3.2628717214308483e-07}
0.689 (+/-0.422) for {'C': 173780082.87493756, 'kernel': 'rbf', 'gamma': 3.2749147555557826e-07}
0.706 (+/-0.403) for {'C': 173780082.87493756, 'kernel': 'rbf', 'gamma': 3.287002239687748e-07}
0.739 (+/-0.437) for {'C': 173780082.87493756, 'kernel': 'rbf', 'gamma': 3.299134337888643e-07}
0.739 (+/-0.437) for {'C': 173780082.87493756, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.739 (+/-0.437) for {'C': 173780082.87493756, 'kernel': 'rbf', 'gamma': 3.3235330357747726e-07}
0.693 (+/-0.419) for {'C': 173780082.87493756, 'kernel': 'rbf', 'gamma': 3.3357999666204803e-07}
0.744 (+/-0.433) for {'C': 173780082.87493756, 'kernel': 'rbf', 'gamma': 3.3481121738605415e-07}
0.694 (+/-0.355) for {'C': 173780082.87493756, 'kernel': 'rbf', 'gamma': 3.3604698246069916e-07}
0.706 (+/-0.418) for {'C': 173780082.87493756, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.702 (+/-0.405) for {'C': 190546071.79632485, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.689 (+/-0.422) for {'C': 190546071.79632485, 'kernel': 'rbf', 'gamma': 3.2628717214308483e-07}
0.689 (+/-0.422) for {'C': 190546071.79632485, 'kernel': 'rbf', 'gamma': 3.2749147555557826e-07}
0.706 (+/-0.403) for {'C': 190546071.79632485, 'kernel': 'rbf', 'gamma': 3.287002239687748e-07}
0.739 (+/-0.437) for {'C': 190546071.79632485, 'kernel': 'rbf', 'gamma': 3.299134337888643e-07}
0.739 (+/-0.437) for {'C': 190546071.79632485, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.739 (+/-0.437) for {'C': 190546071.79632485, 'kernel': 'rbf', 'gamma': 3.3235330357747726e-07}
0.693 (+/-0.419) for {'C': 190546071.79632485, 'kernel': 'rbf', 'gamma': 3.3357999666204803e-07}
0.744 (+/-0.433) for {'C': 190546071.79632485, 'kernel': 'rbf', 'gamma': 3.3481121738605415e-07}
0.694 (+/-0.355) for {'C': 190546071.79632485, 'kernel': 'rbf', 'gamma': 3.3604698246069916e-07}
0.706 (+/-0.418) for {'C': 190546071.79632485, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.702 (+/-0.405) for {'C': 208929613.08540368, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.689 (+/-0.422) for {'C': 208929613.08540368, 'kernel': 'rbf', 'gamma': 3.2628717214308483e-07}
0.689 (+/-0.422) for {'C': 208929613.08540368, 'kernel': 'rbf', 'gamma': 3.2749147555557826e-07}
0.706 (+/-0.403) for {'C': 208929613.08540368, 'kernel': 'rbf', 'gamma': 3.287002239687748e-07}
0.739 (+/-0.437) for {'C': 208929613.08540368, 'kernel': 'rbf', 'gamma': 3.299134337888643e-07}
0.739 (+/-0.437) for {'C': 208929613.08540368, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.739 (+/-0.437) for {'C': 208929613.08540368, 'kernel': 'rbf', 'gamma': 3.3235330357747726e-07}
0.693 (+/-0.419) for {'C': 208929613.08540368, 'kernel': 'rbf', 'gamma': 3.3357999666204803e-07}
0.744 (+/-0.433) for {'C': 208929613.08540368, 'kernel': 'rbf', 'gamma': 3.3481121738605415e-07}
0.694 (+/-0.355) for {'C': 208929613.08540368, 'kernel': 'rbf', 'gamma': 3.3604698246069916e-07}
0.706 (+/-0.418) for {'C': 208929613.08540368, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.702 (+/-0.405) for {'C': 229086765.27677715, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.689 (+/-0.422) for {'C': 229086765.27677715, 'kernel': 'rbf', 'gamma': 3.2628717214308483e-07}
0.689 (+/-0.422) for {'C': 229086765.27677715, 'kernel': 'rbf', 'gamma': 3.2749147555557826e-07}
0.706 (+/-0.403) for {'C': 229086765.27677715, 'kernel': 'rbf', 'gamma': 3.287002239687748e-07}
0.739 (+/-0.437) for {'C': 229086765.27677715, 'kernel': 'rbf', 'gamma': 3.299134337888643e-07}
0.739 (+/-0.437) for {'C': 229086765.27677715, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.739 (+/-0.437) for {'C': 229086765.27677715, 'kernel': 'rbf', 'gamma': 3.3235330357747726e-07}
0.743 (+/-0.433) for {'C': 229086765.27677715, 'kernel': 'rbf', 'gamma': 3.3357999666204803e-07}
0.744 (+/-0.433) for {'C': 229086765.27677715, 'kernel': 'rbf', 'gamma': 3.3481121738605415e-07}
0.694 (+/-0.355) for {'C': 229086765.27677715, 'kernel': 'rbf', 'gamma': 3.3604698246069916e-07}
0.706 (+/-0.418) for {'C': 229086765.27677715, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.702 (+/-0.405) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.689 (+/-0.422) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.2628717214308483e-07}
0.689 (+/-0.422) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.2749147555557826e-07}
0.706 (+/-0.403) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.287002239687748e-07}
0.739 (+/-0.437) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.299134337888643e-07}
0.739 (+/-0.437) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.739 (+/-0.437) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.3235330357747726e-07}
0.689 (+/-0.421) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.3357999666204803e-07}
0.743 (+/-0.433) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.3481121738605415e-07}
0.719 (+/-0.399) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.3604698246069916e-07}
0.706 (+/-0.418) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.706 (+/-0.383) for {'C': 10.0, 'kernel': 'linear'}
0.635 (+/-0.313) for {'C': 100.0, 'kernel': 'linear'}
0.632 (+/-0.309) for {'C': 1000.0, 'kernel': 'linear'}
0.629 (+/-0.312) for {'C': 10000.0, 'kernel': 'linear'}
0.624 (+/-0.319) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.665 (+/-0.316) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.665 (+/-0.316) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.2628717214308483e-07}
0.665 (+/-0.316) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.2749147555557826e-07}
0.682 (+/-0.295) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.287002239687748e-07}
0.682 (+/-0.296) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.299134337888643e-07}
0.682 (+/-0.296) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.657 (+/-0.216) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.3235330357747726e-07}
0.632 (+/-0.224) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.3357999666204803e-07}
0.682 (+/-0.296) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.3481121738605415e-07}
0.682 (+/-0.296) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.3604698246069916e-07}
0.665 (+/-0.316) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.665 (+/-0.316) for {'C': 109647819.61431846, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.665 (+/-0.316) for {'C': 109647819.61431846, 'kernel': 'rbf', 'gamma': 3.2628717214308483e-07}
0.665 (+/-0.316) for {'C': 109647819.61431846, 'kernel': 'rbf', 'gamma': 3.2749147555557826e-07}
0.682 (+/-0.296) for {'C': 109647819.61431846, 'kernel': 'rbf', 'gamma': 3.287002239687748e-07}
0.682 (+/-0.296) for {'C': 109647819.61431846, 'kernel': 'rbf', 'gamma': 3.299134337888643e-07}
0.682 (+/-0.296) for {'C': 109647819.61431846, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.657 (+/-0.216) for {'C': 109647819.61431846, 'kernel': 'rbf', 'gamma': 3.3235330357747726e-07}
0.632 (+/-0.224) for {'C': 109647819.61431846, 'kernel': 'rbf', 'gamma': 3.3357999666204803e-07}
0.657 (+/-0.216) for {'C': 109647819.61431846, 'kernel': 'rbf', 'gamma': 3.3481121738605415e-07}
0.682 (+/-0.296) for {'C': 109647819.61431846, 'kernel': 'rbf', 'gamma': 3.3604698246069916e-07}
0.665 (+/-0.316) for {'C': 109647819.61431846, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.681 (+/-0.296) for {'C': 120226443.46174131, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.665 (+/-0.316) for {'C': 120226443.46174131, 'kernel': 'rbf', 'gamma': 3.2628717214308483e-07}
0.665 (+/-0.316) for {'C': 120226443.46174131, 'kernel': 'rbf', 'gamma': 3.2749147555557826e-07}
0.682 (+/-0.296) for {'C': 120226443.46174131, 'kernel': 'rbf', 'gamma': 3.287002239687748e-07}
0.682 (+/-0.296) for {'C': 120226443.46174131, 'kernel': 'rbf', 'gamma': 3.299134337888643e-07}
0.682 (+/-0.296) for {'C': 120226443.46174131, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.657 (+/-0.216) for {'C': 120226443.46174131, 'kernel': 'rbf', 'gamma': 3.3235330357747726e-07}
0.657 (+/-0.215) for {'C': 120226443.46174131, 'kernel': 'rbf', 'gamma': 3.3357999666204803e-07}
0.682 (+/-0.296) for {'C': 120226443.46174131, 'kernel': 'rbf', 'gamma': 3.3481121738605415e-07}
0.682 (+/-0.296) for {'C': 120226443.46174131, 'kernel': 'rbf', 'gamma': 3.3604698246069916e-07}
0.665 (+/-0.316) for {'C': 120226443.46174131, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.681 (+/-0.296) for {'C': 131825673.85564049, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.665 (+/-0.316) for {'C': 131825673.85564049, 'kernel': 'rbf', 'gamma': 3.2628717214308483e-07}
0.665 (+/-0.316) for {'C': 131825673.85564049, 'kernel': 'rbf', 'gamma': 3.2749147555557826e-07}
0.682 (+/-0.296) for {'C': 131825673.85564049, 'kernel': 'rbf', 'gamma': 3.287002239687748e-07}
0.682 (+/-0.296) for {'C': 131825673.85564049, 'kernel': 'rbf', 'gamma': 3.299134337888643e-07}
0.682 (+/-0.296) for {'C': 131825673.85564049, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.657 (+/-0.215) for {'C': 131825673.85564049, 'kernel': 'rbf', 'gamma': 3.3235330357747726e-07}
0.632 (+/-0.224) for {'C': 131825673.85564049, 'kernel': 'rbf', 'gamma': 3.3357999666204803e-07}
0.657 (+/-0.216) for {'C': 131825673.85564049, 'kernel': 'rbf', 'gamma': 3.3481121738605415e-07}
0.682 (+/-0.296) for {'C': 131825673.85564049, 'kernel': 'rbf', 'gamma': 3.3604698246069916e-07}
0.665 (+/-0.316) for {'C': 131825673.85564049, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.665 (+/-0.316) for {'C': 144543977.0745926, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.665 (+/-0.316) for {'C': 144543977.0745926, 'kernel': 'rbf', 'gamma': 3.2628717214308483e-07}
0.665 (+/-0.316) for {'C': 144543977.0745926, 'kernel': 'rbf', 'gamma': 3.2749147555557826e-07}
0.682 (+/-0.295) for {'C': 144543977.0745926, 'kernel': 'rbf', 'gamma': 3.287002239687748e-07}
0.682 (+/-0.296) for {'C': 144543977.0745926, 'kernel': 'rbf', 'gamma': 3.299134337888643e-07}
0.682 (+/-0.296) for {'C': 144543977.0745926, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.657 (+/-0.215) for {'C': 144543977.0745926, 'kernel': 'rbf', 'gamma': 3.3235330357747726e-07}
0.632 (+/-0.224) for {'C': 144543977.0745926, 'kernel': 'rbf', 'gamma': 3.3357999666204803e-07}
0.682 (+/-0.296) for {'C': 144543977.0745926, 'kernel': 'rbf', 'gamma': 3.3481121738605415e-07}
0.682 (+/-0.296) for {'C': 144543977.0745926, 'kernel': 'rbf', 'gamma': 3.3604698246069916e-07}
0.665 (+/-0.316) for {'C': 144543977.0745926, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.681 (+/-0.296) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.665 (+/-0.316) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.2628717214308483e-07}
0.665 (+/-0.316) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.2749147555557826e-07}
0.682 (+/-0.296) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.287002239687748e-07}
0.682 (+/-0.296) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.299134337888643e-07}
0.682 (+/-0.296) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.657 (+/-0.216) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.3235330357747726e-07}
0.632 (+/-0.224) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.3357999666204803e-07}
0.657 (+/-0.216) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.3481121738605415e-07}
0.682 (+/-0.296) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.3604698246069916e-07}
0.665 (+/-0.316) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.681 (+/-0.296) for {'C': 173780082.87493756, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.665 (+/-0.316) for {'C': 173780082.87493756, 'kernel': 'rbf', 'gamma': 3.2628717214308483e-07}
0.665 (+/-0.316) for {'C': 173780082.87493756, 'kernel': 'rbf', 'gamma': 3.2749147555557826e-07}
0.682 (+/-0.295) for {'C': 173780082.87493756, 'kernel': 'rbf', 'gamma': 3.287002239687748e-07}
0.682 (+/-0.296) for {'C': 173780082.87493756, 'kernel': 'rbf', 'gamma': 3.299134337888643e-07}
0.682 (+/-0.296) for {'C': 173780082.87493756, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.657 (+/-0.215) for {'C': 173780082.87493756, 'kernel': 'rbf', 'gamma': 3.3235330357747726e-07}
0.632 (+/-0.224) for {'C': 173780082.87493756, 'kernel': 'rbf', 'gamma': 3.3357999666204803e-07}
0.682 (+/-0.296) for {'C': 173780082.87493756, 'kernel': 'rbf', 'gamma': 3.3481121738605415e-07}
0.682 (+/-0.296) for {'C': 173780082.87493756, 'kernel': 'rbf', 'gamma': 3.3604698246069916e-07}
0.665 (+/-0.316) for {'C': 173780082.87493756, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.681 (+/-0.296) for {'C': 190546071.79632485, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.665 (+/-0.316) for {'C': 190546071.79632485, 'kernel': 'rbf', 'gamma': 3.2628717214308483e-07}
0.665 (+/-0.316) for {'C': 190546071.79632485, 'kernel': 'rbf', 'gamma': 3.2749147555557826e-07}
0.682 (+/-0.295) for {'C': 190546071.79632485, 'kernel': 'rbf', 'gamma': 3.287002239687748e-07}
0.682 (+/-0.296) for {'C': 190546071.79632485, 'kernel': 'rbf', 'gamma': 3.299134337888643e-07}
0.682 (+/-0.296) for {'C': 190546071.79632485, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.657 (+/-0.215) for {'C': 190546071.79632485, 'kernel': 'rbf', 'gamma': 3.3235330357747726e-07}
0.632 (+/-0.224) for {'C': 190546071.79632485, 'kernel': 'rbf', 'gamma': 3.3357999666204803e-07}
0.682 (+/-0.296) for {'C': 190546071.79632485, 'kernel': 'rbf', 'gamma': 3.3481121738605415e-07}
0.682 (+/-0.296) for {'C': 190546071.79632485, 'kernel': 'rbf', 'gamma': 3.3604698246069916e-07}
0.665 (+/-0.316) for {'C': 190546071.79632485, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.681 (+/-0.296) for {'C': 208929613.08540368, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.665 (+/-0.316) for {'C': 208929613.08540368, 'kernel': 'rbf', 'gamma': 3.2628717214308483e-07}
0.665 (+/-0.316) for {'C': 208929613.08540368, 'kernel': 'rbf', 'gamma': 3.2749147555557826e-07}
0.682 (+/-0.296) for {'C': 208929613.08540368, 'kernel': 'rbf', 'gamma': 3.287002239687748e-07}
0.682 (+/-0.296) for {'C': 208929613.08540368, 'kernel': 'rbf', 'gamma': 3.299134337888643e-07}
0.682 (+/-0.296) for {'C': 208929613.08540368, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.657 (+/-0.216) for {'C': 208929613.08540368, 'kernel': 'rbf', 'gamma': 3.3235330357747726e-07}
0.632 (+/-0.224) for {'C': 208929613.08540368, 'kernel': 'rbf', 'gamma': 3.3357999666204803e-07}
0.682 (+/-0.296) for {'C': 208929613.08540368, 'kernel': 'rbf', 'gamma': 3.3481121738605415e-07}
0.682 (+/-0.296) for {'C': 208929613.08540368, 'kernel': 'rbf', 'gamma': 3.3604698246069916e-07}
0.665 (+/-0.316) for {'C': 208929613.08540368, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.681 (+/-0.296) for {'C': 229086765.27677715, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.665 (+/-0.316) for {'C': 229086765.27677715, 'kernel': 'rbf', 'gamma': 3.2628717214308483e-07}
0.665 (+/-0.316) for {'C': 229086765.27677715, 'kernel': 'rbf', 'gamma': 3.2749147555557826e-07}
0.682 (+/-0.296) for {'C': 229086765.27677715, 'kernel': 'rbf', 'gamma': 3.287002239687748e-07}
0.682 (+/-0.296) for {'C': 229086765.27677715, 'kernel': 'rbf', 'gamma': 3.299134337888643e-07}
0.682 (+/-0.296) for {'C': 229086765.27677715, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.657 (+/-0.216) for {'C': 229086765.27677715, 'kernel': 'rbf', 'gamma': 3.3235330357747726e-07}
0.657 (+/-0.216) for {'C': 229086765.27677715, 'kernel': 'rbf', 'gamma': 3.3357999666204803e-07}
0.682 (+/-0.296) for {'C': 229086765.27677715, 'kernel': 'rbf', 'gamma': 3.3481121738605415e-07}
0.682 (+/-0.296) for {'C': 229086765.27677715, 'kernel': 'rbf', 'gamma': 3.3604698246069916e-07}
0.665 (+/-0.316) for {'C': 229086765.27677715, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.681 (+/-0.296) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.665 (+/-0.316) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.2628717214308483e-07}
0.665 (+/-0.316) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.2749147555557826e-07}
0.682 (+/-0.296) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.287002239687748e-07}
0.682 (+/-0.296) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.299134337888643e-07}
0.682 (+/-0.296) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.657 (+/-0.216) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.3235330357747726e-07}
0.632 (+/-0.224) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.3357999666204803e-07}
0.657 (+/-0.216) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.3481121738605415e-07}
0.682 (+/-0.296) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.3604698246069916e-07}
0.665 (+/-0.316) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 10.0, 'kernel': 'linear'}
0.636 (+/-0.237) for {'C': 100.0, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.636 (+/-0.240) for {'C': 10000.0, 'kernel': 'linear'}
0.610 (+/-0.245) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.3481121738605415e-07}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6822099925797676
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.620 (+/-0.309) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.629 (+/-0.326) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.2628717214308483e-07}
0.622 (+/-0.308) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.2749147555557826e-07}
0.638 (+/-0.319) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.287002239687748e-07}
0.631 (+/-0.302) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.299134337888643e-07}
0.625 (+/-0.305) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.629 (+/-0.303) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.3235330357747726e-07}
0.621 (+/-0.294) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.3357999666204803e-07}
0.605 (+/-0.301) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.3481121738605415e-07}
0.620 (+/-0.290) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.3604698246069916e-07}
0.617 (+/-0.292) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.620 (+/-0.309) for {'C': 109647819.61431846, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.621 (+/-0.308) for {'C': 109647819.61431846, 'kernel': 'rbf', 'gamma': 3.2628717214308483e-07}
0.622 (+/-0.308) for {'C': 109647819.61431846, 'kernel': 'rbf', 'gamma': 3.2749147555557826e-07}
0.638 (+/-0.319) for {'C': 109647819.61431846, 'kernel': 'rbf', 'gamma': 3.287002239687748e-07}
0.631 (+/-0.302) for {'C': 109647819.61431846, 'kernel': 'rbf', 'gamma': 3.299134337888643e-07}
0.625 (+/-0.305) for {'C': 109647819.61431846, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.626 (+/-0.305) for {'C': 109647819.61431846, 'kernel': 'rbf', 'gamma': 3.3235330357747726e-07}
0.620 (+/-0.294) for {'C': 109647819.61431846, 'kernel': 'rbf', 'gamma': 3.3357999666204803e-07}
0.605 (+/-0.301) for {'C': 109647819.61431846, 'kernel': 'rbf', 'gamma': 3.3481121738605415e-07}
0.620 (+/-0.290) for {'C': 109647819.61431846, 'kernel': 'rbf', 'gamma': 3.3604698246069916e-07}
0.617 (+/-0.292) for {'C': 109647819.61431846, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.620 (+/-0.310) for {'C': 120226443.46174131, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.621 (+/-0.308) for {'C': 120226443.46174131, 'kernel': 'rbf', 'gamma': 3.2628717214308483e-07}
0.622 (+/-0.308) for {'C': 120226443.46174131, 'kernel': 'rbf', 'gamma': 3.2749147555557826e-07}
0.638 (+/-0.319) for {'C': 120226443.46174131, 'kernel': 'rbf', 'gamma': 3.287002239687748e-07}
0.631 (+/-0.302) for {'C': 120226443.46174131, 'kernel': 'rbf', 'gamma': 3.299134337888643e-07}
0.625 (+/-0.305) for {'C': 120226443.46174131, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.626 (+/-0.305) for {'C': 120226443.46174131, 'kernel': 'rbf', 'gamma': 3.3235330357747726e-07}
0.620 (+/-0.294) for {'C': 120226443.46174131, 'kernel': 'rbf', 'gamma': 3.3357999666204803e-07}
0.605 (+/-0.301) for {'C': 120226443.46174131, 'kernel': 'rbf', 'gamma': 3.3481121738605415e-07}
0.620 (+/-0.290) for {'C': 120226443.46174131, 'kernel': 'rbf', 'gamma': 3.3604698246069916e-07}
0.617 (+/-0.292) for {'C': 120226443.46174131, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.620 (+/-0.309) for {'C': 131825673.85564049, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.621 (+/-0.308) for {'C': 131825673.85564049, 'kernel': 'rbf', 'gamma': 3.2628717214308483e-07}
0.622 (+/-0.308) for {'C': 131825673.85564049, 'kernel': 'rbf', 'gamma': 3.2749147555557826e-07}
0.629 (+/-0.302) for {'C': 131825673.85564049, 'kernel': 'rbf', 'gamma': 3.287002239687748e-07}
0.631 (+/-0.302) for {'C': 131825673.85564049, 'kernel': 'rbf', 'gamma': 3.299134337888643e-07}
0.625 (+/-0.305) for {'C': 131825673.85564049, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.626 (+/-0.305) for {'C': 131825673.85564049, 'kernel': 'rbf', 'gamma': 3.3235330357747726e-07}
0.620 (+/-0.294) for {'C': 131825673.85564049, 'kernel': 'rbf', 'gamma': 3.3357999666204803e-07}
0.605 (+/-0.301) for {'C': 131825673.85564049, 'kernel': 'rbf', 'gamma': 3.3481121738605415e-07}
0.622 (+/-0.290) for {'C': 131825673.85564049, 'kernel': 'rbf', 'gamma': 3.3604698246069916e-07}
0.617 (+/-0.292) for {'C': 131825673.85564049, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.620 (+/-0.310) for {'C': 144543977.0745926, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.621 (+/-0.308) for {'C': 144543977.0745926, 'kernel': 'rbf', 'gamma': 3.2628717214308483e-07}
0.622 (+/-0.308) for {'C': 144543977.0745926, 'kernel': 'rbf', 'gamma': 3.2749147555557826e-07}
0.629 (+/-0.302) for {'C': 144543977.0745926, 'kernel': 'rbf', 'gamma': 3.287002239687748e-07}
0.631 (+/-0.302) for {'C': 144543977.0745926, 'kernel': 'rbf', 'gamma': 3.299134337888643e-07}
0.625 (+/-0.305) for {'C': 144543977.0745926, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.626 (+/-0.305) for {'C': 144543977.0745926, 'kernel': 'rbf', 'gamma': 3.3235330357747726e-07}
0.620 (+/-0.294) for {'C': 144543977.0745926, 'kernel': 'rbf', 'gamma': 3.3357999666204803e-07}
0.605 (+/-0.301) for {'C': 144543977.0745926, 'kernel': 'rbf', 'gamma': 3.3481121738605415e-07}
0.620 (+/-0.290) for {'C': 144543977.0745926, 'kernel': 'rbf', 'gamma': 3.3604698246069916e-07}
0.617 (+/-0.292) for {'C': 144543977.0745926, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.620 (+/-0.310) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.621 (+/-0.308) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.2628717214308483e-07}
0.622 (+/-0.308) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.2749147555557826e-07}
0.629 (+/-0.302) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.287002239687748e-07}
0.631 (+/-0.302) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.299134337888643e-07}
0.630 (+/-0.303) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.627 (+/-0.304) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.3235330357747726e-07}
0.620 (+/-0.294) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.3357999666204803e-07}
0.605 (+/-0.301) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.3481121738605415e-07}
0.620 (+/-0.290) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.3604698246069916e-07}
0.617 (+/-0.292) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.620 (+/-0.310) for {'C': 173780082.87493756, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.621 (+/-0.308) for {'C': 173780082.87493756, 'kernel': 'rbf', 'gamma': 3.2628717214308483e-07}
0.622 (+/-0.308) for {'C': 173780082.87493756, 'kernel': 'rbf', 'gamma': 3.2749147555557826e-07}
0.638 (+/-0.319) for {'C': 173780082.87493756, 'kernel': 'rbf', 'gamma': 3.287002239687748e-07}
0.631 (+/-0.302) for {'C': 173780082.87493756, 'kernel': 'rbf', 'gamma': 3.299134337888643e-07}
0.625 (+/-0.306) for {'C': 173780082.87493756, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.626 (+/-0.305) for {'C': 173780082.87493756, 'kernel': 'rbf', 'gamma': 3.3235330357747726e-07}
0.620 (+/-0.294) for {'C': 173780082.87493756, 'kernel': 'rbf', 'gamma': 3.3357999666204803e-07}
0.605 (+/-0.301) for {'C': 173780082.87493756, 'kernel': 'rbf', 'gamma': 3.3481121738605415e-07}
0.618 (+/-0.290) for {'C': 173780082.87493756, 'kernel': 'rbf', 'gamma': 3.3604698246069916e-07}
0.617 (+/-0.292) for {'C': 173780082.87493756, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.620 (+/-0.310) for {'C': 190546071.79632485, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.620 (+/-0.309) for {'C': 190546071.79632485, 'kernel': 'rbf', 'gamma': 3.2628717214308483e-07}
0.622 (+/-0.308) for {'C': 190546071.79632485, 'kernel': 'rbf', 'gamma': 3.2749147555557826e-07}
0.638 (+/-0.319) for {'C': 190546071.79632485, 'kernel': 'rbf', 'gamma': 3.287002239687748e-07}
0.631 (+/-0.302) for {'C': 190546071.79632485, 'kernel': 'rbf', 'gamma': 3.299134337888643e-07}
0.625 (+/-0.306) for {'C': 190546071.79632485, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.626 (+/-0.305) for {'C': 190546071.79632485, 'kernel': 'rbf', 'gamma': 3.3235330357747726e-07}
0.620 (+/-0.294) for {'C': 190546071.79632485, 'kernel': 'rbf', 'gamma': 3.3357999666204803e-07}
0.614 (+/-0.293) for {'C': 190546071.79632485, 'kernel': 'rbf', 'gamma': 3.3481121738605415e-07}
0.618 (+/-0.290) for {'C': 190546071.79632485, 'kernel': 'rbf', 'gamma': 3.3604698246069916e-07}
0.617 (+/-0.292) for {'C': 190546071.79632485, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.619 (+/-0.310) for {'C': 208929613.08540368, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.620 (+/-0.309) for {'C': 208929613.08540368, 'kernel': 'rbf', 'gamma': 3.2628717214308483e-07}
0.622 (+/-0.308) for {'C': 208929613.08540368, 'kernel': 'rbf', 'gamma': 3.2749147555557826e-07}
0.629 (+/-0.302) for {'C': 208929613.08540368, 'kernel': 'rbf', 'gamma': 3.287002239687748e-07}
0.631 (+/-0.302) for {'C': 208929613.08540368, 'kernel': 'rbf', 'gamma': 3.299134337888643e-07}
0.625 (+/-0.306) for {'C': 208929613.08540368, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.626 (+/-0.305) for {'C': 208929613.08540368, 'kernel': 'rbf', 'gamma': 3.3235330357747726e-07}
0.620 (+/-0.294) for {'C': 208929613.08540368, 'kernel': 'rbf', 'gamma': 3.3357999666204803e-07}
0.614 (+/-0.293) for {'C': 208929613.08540368, 'kernel': 'rbf', 'gamma': 3.3481121738605415e-07}
0.618 (+/-0.290) for {'C': 208929613.08540368, 'kernel': 'rbf', 'gamma': 3.3604698246069916e-07}
0.617 (+/-0.292) for {'C': 208929613.08540368, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.619 (+/-0.310) for {'C': 229086765.27677715, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.620 (+/-0.309) for {'C': 229086765.27677715, 'kernel': 'rbf', 'gamma': 3.2628717214308483e-07}
0.622 (+/-0.308) for {'C': 229086765.27677715, 'kernel': 'rbf', 'gamma': 3.2749147555557826e-07}
0.638 (+/-0.319) for {'C': 229086765.27677715, 'kernel': 'rbf', 'gamma': 3.287002239687748e-07}
0.631 (+/-0.302) for {'C': 229086765.27677715, 'kernel': 'rbf', 'gamma': 3.299134337888643e-07}
0.625 (+/-0.306) for {'C': 229086765.27677715, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.626 (+/-0.305) for {'C': 229086765.27677715, 'kernel': 'rbf', 'gamma': 3.3235330357747726e-07}
0.620 (+/-0.294) for {'C': 229086765.27677715, 'kernel': 'rbf', 'gamma': 3.3357999666204803e-07}
0.614 (+/-0.293) for {'C': 229086765.27677715, 'kernel': 'rbf', 'gamma': 3.3481121738605415e-07}
0.618 (+/-0.290) for {'C': 229086765.27677715, 'kernel': 'rbf', 'gamma': 3.3604698246069916e-07}
0.617 (+/-0.292) for {'C': 229086765.27677715, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.619 (+/-0.310) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.620 (+/-0.309) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.2628717214308483e-07}
0.622 (+/-0.308) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.2749147555557826e-07}
0.629 (+/-0.302) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.287002239687748e-07}
0.631 (+/-0.302) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.299134337888643e-07}
0.630 (+/-0.303) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.627 (+/-0.304) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.3235330357747726e-07}
0.620 (+/-0.294) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.3357999666204803e-07}
0.614 (+/-0.293) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.3481121738605415e-07}
0.618 (+/-0.290) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.3604698246069916e-07}
0.617 (+/-0.292) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.666 (+/-0.379) for {'C': 10.0, 'kernel': 'linear'}
0.629 (+/-0.313) for {'C': 100.0, 'kernel': 'linear'}
0.632 (+/-0.309) for {'C': 1000.0, 'kernel': 'linear'}
0.629 (+/-0.312) for {'C': 10000.0, 'kernel': 'linear'}
0.624 (+/-0.319) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.20      0.17      0.18         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.99      0.99       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.677 (+/-0.292) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.678 (+/-0.293) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.2628717214308483e-07}
0.678 (+/-0.292) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.2749147555557826e-07}
0.679 (+/-0.293) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.287002239687748e-07}
0.679 (+/-0.293) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.299134337888643e-07}
0.678 (+/-0.293) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.679 (+/-0.292) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.3235330357747726e-07}
0.679 (+/-0.291) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.3357999666204803e-07}
0.661 (+/-0.311) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.3481121738605415e-07}
0.679 (+/-0.289) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.3604698246069916e-07}
0.678 (+/-0.285) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.677 (+/-0.292) for {'C': 109647819.61431846, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.678 (+/-0.292) for {'C': 109647819.61431846, 'kernel': 'rbf', 'gamma': 3.2628717214308483e-07}
0.678 (+/-0.292) for {'C': 109647819.61431846, 'kernel': 'rbf', 'gamma': 3.2749147555557826e-07}
0.679 (+/-0.293) for {'C': 109647819.61431846, 'kernel': 'rbf', 'gamma': 3.287002239687748e-07}
0.679 (+/-0.293) for {'C': 109647819.61431846, 'kernel': 'rbf', 'gamma': 3.299134337888643e-07}
0.678 (+/-0.293) for {'C': 109647819.61431846, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.678 (+/-0.293) for {'C': 109647819.61431846, 'kernel': 'rbf', 'gamma': 3.3235330357747726e-07}
0.678 (+/-0.291) for {'C': 109647819.61431846, 'kernel': 'rbf', 'gamma': 3.3357999666204803e-07}
0.661 (+/-0.311) for {'C': 109647819.61431846, 'kernel': 'rbf', 'gamma': 3.3481121738605415e-07}
0.679 (+/-0.289) for {'C': 109647819.61431846, 'kernel': 'rbf', 'gamma': 3.3604698246069916e-07}
0.678 (+/-0.285) for {'C': 109647819.61431846, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.677 (+/-0.292) for {'C': 120226443.46174131, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.678 (+/-0.292) for {'C': 120226443.46174131, 'kernel': 'rbf', 'gamma': 3.2628717214308483e-07}
0.678 (+/-0.292) for {'C': 120226443.46174131, 'kernel': 'rbf', 'gamma': 3.2749147555557826e-07}
0.679 (+/-0.293) for {'C': 120226443.46174131, 'kernel': 'rbf', 'gamma': 3.287002239687748e-07}
0.679 (+/-0.293) for {'C': 120226443.46174131, 'kernel': 'rbf', 'gamma': 3.299134337888643e-07}
0.678 (+/-0.293) for {'C': 120226443.46174131, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.678 (+/-0.293) for {'C': 120226443.46174131, 'kernel': 'rbf', 'gamma': 3.3235330357747726e-07}
0.678 (+/-0.291) for {'C': 120226443.46174131, 'kernel': 'rbf', 'gamma': 3.3357999666204803e-07}
0.661 (+/-0.311) for {'C': 120226443.46174131, 'kernel': 'rbf', 'gamma': 3.3481121738605415e-07}
0.679 (+/-0.289) for {'C': 120226443.46174131, 'kernel': 'rbf', 'gamma': 3.3604698246069916e-07}
0.678 (+/-0.285) for {'C': 120226443.46174131, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.677 (+/-0.292) for {'C': 131825673.85564049, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.678 (+/-0.292) for {'C': 131825673.85564049, 'kernel': 'rbf', 'gamma': 3.2628717214308483e-07}
0.678 (+/-0.292) for {'C': 131825673.85564049, 'kernel': 'rbf', 'gamma': 3.2749147555557826e-07}
0.679 (+/-0.293) for {'C': 131825673.85564049, 'kernel': 'rbf', 'gamma': 3.287002239687748e-07}
0.679 (+/-0.293) for {'C': 131825673.85564049, 'kernel': 'rbf', 'gamma': 3.299134337888643e-07}
0.678 (+/-0.293) for {'C': 131825673.85564049, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.678 (+/-0.293) for {'C': 131825673.85564049, 'kernel': 'rbf', 'gamma': 3.3235330357747726e-07}
0.678 (+/-0.291) for {'C': 131825673.85564049, 'kernel': 'rbf', 'gamma': 3.3357999666204803e-07}
0.661 (+/-0.311) for {'C': 131825673.85564049, 'kernel': 'rbf', 'gamma': 3.3481121738605415e-07}
0.679 (+/-0.290) for {'C': 131825673.85564049, 'kernel': 'rbf', 'gamma': 3.3604698246069916e-07}
0.678 (+/-0.285) for {'C': 131825673.85564049, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.677 (+/-0.292) for {'C': 144543977.0745926, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.678 (+/-0.292) for {'C': 144543977.0745926, 'kernel': 'rbf', 'gamma': 3.2628717214308483e-07}
0.678 (+/-0.292) for {'C': 144543977.0745926, 'kernel': 'rbf', 'gamma': 3.2749147555557826e-07}
0.679 (+/-0.293) for {'C': 144543977.0745926, 'kernel': 'rbf', 'gamma': 3.287002239687748e-07}
0.679 (+/-0.293) for {'C': 144543977.0745926, 'kernel': 'rbf', 'gamma': 3.299134337888643e-07}
0.678 (+/-0.293) for {'C': 144543977.0745926, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.678 (+/-0.293) for {'C': 144543977.0745926, 'kernel': 'rbf', 'gamma': 3.3235330357747726e-07}
0.678 (+/-0.291) for {'C': 144543977.0745926, 'kernel': 'rbf', 'gamma': 3.3357999666204803e-07}
0.661 (+/-0.311) for {'C': 144543977.0745926, 'kernel': 'rbf', 'gamma': 3.3481121738605415e-07}
0.679 (+/-0.289) for {'C': 144543977.0745926, 'kernel': 'rbf', 'gamma': 3.3604698246069916e-07}
0.678 (+/-0.285) for {'C': 144543977.0745926, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.677 (+/-0.292) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.678 (+/-0.292) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.2628717214308483e-07}
0.678 (+/-0.292) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.2749147555557826e-07}
0.679 (+/-0.293) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.287002239687748e-07}
0.679 (+/-0.293) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.299134337888643e-07}
0.695 (+/-0.304) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.679 (+/-0.292) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.3235330357747726e-07}
0.678 (+/-0.291) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.3357999666204803e-07}
0.661 (+/-0.311) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.3481121738605415e-07}
0.679 (+/-0.289) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.3604698246069916e-07}
0.678 (+/-0.285) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.677 (+/-0.292) for {'C': 173780082.87493756, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.678 (+/-0.292) for {'C': 173780082.87493756, 'kernel': 'rbf', 'gamma': 3.2628717214308483e-07}
0.678 (+/-0.292) for {'C': 173780082.87493756, 'kernel': 'rbf', 'gamma': 3.2749147555557826e-07}
0.679 (+/-0.293) for {'C': 173780082.87493756, 'kernel': 'rbf', 'gamma': 3.287002239687748e-07}
0.679 (+/-0.293) for {'C': 173780082.87493756, 'kernel': 'rbf', 'gamma': 3.299134337888643e-07}
0.678 (+/-0.292) for {'C': 173780082.87493756, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.678 (+/-0.293) for {'C': 173780082.87493756, 'kernel': 'rbf', 'gamma': 3.3235330357747726e-07}
0.678 (+/-0.291) for {'C': 173780082.87493756, 'kernel': 'rbf', 'gamma': 3.3357999666204803e-07}
0.661 (+/-0.311) for {'C': 173780082.87493756, 'kernel': 'rbf', 'gamma': 3.3481121738605415e-07}
0.679 (+/-0.289) for {'C': 173780082.87493756, 'kernel': 'rbf', 'gamma': 3.3604698246069916e-07}
0.678 (+/-0.285) for {'C': 173780082.87493756, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.677 (+/-0.292) for {'C': 190546071.79632485, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.677 (+/-0.292) for {'C': 190546071.79632485, 'kernel': 'rbf', 'gamma': 3.2628717214308483e-07}
0.678 (+/-0.292) for {'C': 190546071.79632485, 'kernel': 'rbf', 'gamma': 3.2749147555557826e-07}
0.679 (+/-0.293) for {'C': 190546071.79632485, 'kernel': 'rbf', 'gamma': 3.287002239687748e-07}
0.679 (+/-0.293) for {'C': 190546071.79632485, 'kernel': 'rbf', 'gamma': 3.299134337888643e-07}
0.678 (+/-0.292) for {'C': 190546071.79632485, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.678 (+/-0.293) for {'C': 190546071.79632485, 'kernel': 'rbf', 'gamma': 3.3235330357747726e-07}
0.678 (+/-0.291) for {'C': 190546071.79632485, 'kernel': 'rbf', 'gamma': 3.3357999666204803e-07}
0.678 (+/-0.290) for {'C': 190546071.79632485, 'kernel': 'rbf', 'gamma': 3.3481121738605415e-07}
0.679 (+/-0.289) for {'C': 190546071.79632485, 'kernel': 'rbf', 'gamma': 3.3604698246069916e-07}
0.678 (+/-0.285) for {'C': 190546071.79632485, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.677 (+/-0.291) for {'C': 208929613.08540368, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.677 (+/-0.292) for {'C': 208929613.08540368, 'kernel': 'rbf', 'gamma': 3.2628717214308483e-07}
0.678 (+/-0.292) for {'C': 208929613.08540368, 'kernel': 'rbf', 'gamma': 3.2749147555557826e-07}
0.679 (+/-0.293) for {'C': 208929613.08540368, 'kernel': 'rbf', 'gamma': 3.287002239687748e-07}
0.679 (+/-0.293) for {'C': 208929613.08540368, 'kernel': 'rbf', 'gamma': 3.299134337888643e-07}
0.678 (+/-0.292) for {'C': 208929613.08540368, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.678 (+/-0.293) for {'C': 208929613.08540368, 'kernel': 'rbf', 'gamma': 3.3235330357747726e-07}
0.678 (+/-0.291) for {'C': 208929613.08540368, 'kernel': 'rbf', 'gamma': 3.3357999666204803e-07}
0.678 (+/-0.290) for {'C': 208929613.08540368, 'kernel': 'rbf', 'gamma': 3.3481121738605415e-07}
0.679 (+/-0.289) for {'C': 208929613.08540368, 'kernel': 'rbf', 'gamma': 3.3604698246069916e-07}
0.678 (+/-0.285) for {'C': 208929613.08540368, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.677 (+/-0.291) for {'C': 229086765.27677715, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.677 (+/-0.292) for {'C': 229086765.27677715, 'kernel': 'rbf', 'gamma': 3.2628717214308483e-07}
0.678 (+/-0.292) for {'C': 229086765.27677715, 'kernel': 'rbf', 'gamma': 3.2749147555557826e-07}
0.679 (+/-0.293) for {'C': 229086765.27677715, 'kernel': 'rbf', 'gamma': 3.287002239687748e-07}
0.679 (+/-0.293) for {'C': 229086765.27677715, 'kernel': 'rbf', 'gamma': 3.299134337888643e-07}
0.678 (+/-0.292) for {'C': 229086765.27677715, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.678 (+/-0.293) for {'C': 229086765.27677715, 'kernel': 'rbf', 'gamma': 3.3235330357747726e-07}
0.678 (+/-0.291) for {'C': 229086765.27677715, 'kernel': 'rbf', 'gamma': 3.3357999666204803e-07}
0.678 (+/-0.290) for {'C': 229086765.27677715, 'kernel': 'rbf', 'gamma': 3.3481121738605415e-07}
0.679 (+/-0.289) for {'C': 229086765.27677715, 'kernel': 'rbf', 'gamma': 3.3604698246069916e-07}
0.678 (+/-0.285) for {'C': 229086765.27677715, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.677 (+/-0.291) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.677 (+/-0.292) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.2628717214308483e-07}
0.678 (+/-0.292) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.2749147555557826e-07}
0.679 (+/-0.293) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.287002239687748e-07}
0.679 (+/-0.293) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.299134337888643e-07}
0.695 (+/-0.304) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.679 (+/-0.292) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.3235330357747726e-07}
0.678 (+/-0.291) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.3357999666204803e-07}
0.678 (+/-0.290) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.3481121738605415e-07}
0.679 (+/-0.289) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.3604698246069916e-07}
0.678 (+/-0.285) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.662 (+/-0.314) for {'C': 10.0, 'kernel': 'linear'}
0.660 (+/-0.315) for {'C': 100.0, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.636 (+/-0.240) for {'C': 10000.0, 'kernel': 'linear'}
0.610 (+/-0.245) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6948687031082529
第2轮loop中，tunemodel函数得到的最优参数是： ([{'C': array([1.44543977e+08, 1.47231250e+08, 1.49968484e+08, 1.52756606e+08,
       1.55596563e+08, 1.58489319e+08, 1.61435856e+08, 1.64437172e+08,
       1.67494288e+08, 1.70608239e+08, 1.73780083e+08]), 'kernel': ['rbf'], 'gamma': array([3.29913434e-07, 3.30156613e-07, 3.30399971e-07, 3.30643508e-07,
       3.30887225e-07, 3.31131121e-07, 3.31375198e-07, 3.31619454e-07,
       3.31863890e-07, 3.32108507e-07, 3.32353304e-07])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}], {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}, 0.6948687031082529)
这是第2次迭代微调C和gamma。
第2次迭代，得到delta： [0. 0.]
SVC模型clf_op_iter的参数是： {'kernel': 'rbf', 'decision_function_shape': 'ovr', 'class_weight': {-1: 15}, 'tol': 0.001, 'gamma': 3.3113112148259127e-07, 'random_state': 0, 'cache_size': 200, 'verbose': False, 'degree': 3, 'C': 158489319.24611127, 'probability': False, 'shrinking': True, 'coef0': 0.0, 'max_iter': -1}

训练模型SVC对训练样本的预测准确率： 0.9355067328136074

测试集中，预测为舞弊样本的有： (array([  56,  136,  370,  590,  769, 1017, 1122, 1246, 1247, 1248, 1251,
       1252, 1255, 1256], dtype=int64),) 
测试集中，实际为舞弊样本的有： (array([1246, 1247, 1248, 1249, 1250, 1251, 1252, 1253, 1254, 1255, 1256],
      dtype=int64),) 
预测的舞弊样本数目： 14

训练模型SVC对测试样本的预测准确率： 0.9525159461374911
以上是第32次特征筛选。
第32次特征筛选，AUC值是： 0.8153728294177732
X_train_iter_svc.shape is: (1257, 20)
X_test_iter_svc.shape is: (1257, 20)
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.649 (+/-0.386) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.747 (+/-0.449) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.614 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.641 (+/-0.236) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6413461538461539
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.764 (+/-0.421) for {'C': 1.0, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.620 (+/-0.321) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.20      0.17      0.18         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.99      0.99       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.683 (+/-0.296) for {'C': 1.0, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.612 (+/-0.241) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.20      0.17      0.18         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.99      0.99       629

本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6826923076923077
RBF的粗grid search最佳超参： {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001} RBF的粗grid search最高分值： 0.6413461538461539
linear的粗grid search最佳超参： {'C': 1.0, 'kernel': 'linear'} linear的粗grid search最高分值： 0.6826923076923077
粗grid search得到的parameter是：
 [{'C': array([1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03, 1.e+04, 1.e+05,
       1.e+06, 1.e+07, 1.e+08]), 'kernel': ['rbf'], 'gamma': array([1.e-08, 1.e-07, 1.e-06, 1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01,
       1.e+00, 1.e+01, 1.e+02])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.722 (+/-0.473) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.631 (+/-0.319) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.639 (+/-0.326) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.647 (+/-0.387) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.449) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.317) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.649 (+/-0.386) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.798 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.317) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.626 (+/-0.318) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.622 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.649 (+/-0.386) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.747 (+/-0.502) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.798 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.317) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.626 (+/-0.318) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.622 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.649 (+/-0.386) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.797 (+/-0.492) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.823 (+/-0.451) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.651 (+/-0.308) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.631 (+/-0.319) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.619 (+/-0.321) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.622 (+/-0.319) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.649 (+/-0.386) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.699 (+/-0.661) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.768 (+/-0.475) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.731 (+/-0.401) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.642 (+/-0.316) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.620 (+/-0.321) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.620 (+/-0.320) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.622 (+/-0.319) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.649 (+/-0.386) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.700 (+/-0.660) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.607 (+/-0.395) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.706 (+/-0.383) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.641 (+/-0.318) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.614 (+/-0.327) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.614 (+/-0.327) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.622 (+/-0.319) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.649 (+/-0.386) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.673 (+/-0.330) for {'C': 10.0, 'kernel': 'linear'}
0.646 (+/-0.317) for {'C': 100.0, 'kernel': 'linear'}
0.620 (+/-0.321) for {'C': 1000.0, 'kernel': 'linear'}
0.618 (+/-0.322) for {'C': 10000.0, 'kernel': 'linear'}
0.624 (+/-0.319) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.006) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.240) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.006) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.641 (+/-0.236) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.665 (+/-0.314) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.587 (+/-0.233) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.006) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.658 (+/-0.216) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.665 (+/-0.315) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.612 (+/-0.242) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.240) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.006) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.617 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.658 (+/-0.216) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.665 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.612 (+/-0.242) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.587 (+/-0.233) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.006) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.642 (+/-0.236) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.683 (+/-0.296) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.665 (+/-0.314) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.612 (+/-0.243) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.612 (+/-0.239) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.587 (+/-0.234) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.238) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.006) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.633 (+/-0.226) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.666 (+/-0.314) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.699 (+/-0.308) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.663 (+/-0.313) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.610 (+/-0.244) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.612 (+/-0.240) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.587 (+/-0.235) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.238) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.006) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.633 (+/-0.225) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.590 (+/-0.314) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.658 (+/-0.311) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.662 (+/-0.314) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.585 (+/-0.237) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.587 (+/-0.234) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.587 (+/-0.235) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.238) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.006) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.665 (+/-0.315) for {'C': 10.0, 'kernel': 'linear'}
0.664 (+/-0.316) for {'C': 100.0, 'kernel': 'linear'}
0.612 (+/-0.241) for {'C': 1000.0, 'kernel': 'linear'}
0.611 (+/-0.239) for {'C': 10000.0, 'kernel': 'linear'}
0.611 (+/-0.245) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.14      0.17      0.15         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6988782051282051
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.674 (+/-0.358) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.618 (+/-0.322) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.687 (+/-0.359) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.647 (+/-0.387) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.687 (+/-0.359) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.634 (+/-0.310) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.649 (+/-0.386) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.687 (+/-0.359) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.634 (+/-0.310) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.619 (+/-0.321) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.622 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.649 (+/-0.386) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.747 (+/-0.502) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.797 (+/-0.492) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.674 (+/-0.354) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.634 (+/-0.310) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.620 (+/-0.321) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.622 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.649 (+/-0.386) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.547 (+/-0.301) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.798 (+/-0.492) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.703 (+/-0.357) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.630 (+/-0.311) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.619 (+/-0.321) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.619 (+/-0.321) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.622 (+/-0.319) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.649 (+/-0.386) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.699 (+/-0.797) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.621 (+/-0.391) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.677 (+/-0.350) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.633 (+/-0.323) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.620 (+/-0.321) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.620 (+/-0.320) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.622 (+/-0.319) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.649 (+/-0.386) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.550 (+/-0.827) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.601 (+/-0.398) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.680 (+/-0.373) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.640 (+/-0.314) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.614 (+/-0.327) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.614 (+/-0.327) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.622 (+/-0.319) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.649 (+/-0.386) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.764 (+/-0.421) for {'C': 1.0, 'kernel': 'linear'}
0.643 (+/-0.306) for {'C': 10.0, 'kernel': 'linear'}
0.630 (+/-0.312) for {'C': 100.0, 'kernel': 'linear'}
0.620 (+/-0.321) for {'C': 1000.0, 'kernel': 'linear'}
0.618 (+/-0.322) for {'C': 10000.0, 'kernel': 'linear'}
0.624 (+/-0.319) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.007) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.681 (+/-0.294) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.612 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.006) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.306) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.587 (+/-0.234) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.240) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.006) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.698 (+/-0.306) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.662 (+/-0.314) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.587 (+/-0.234) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.006) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.698 (+/-0.306) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.662 (+/-0.315) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.611 (+/-0.242) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.240) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.006) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.617 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.642 (+/-0.236) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.697 (+/-0.305) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.662 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.611 (+/-0.243) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.587 (+/-0.233) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.006) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.525 (+/-0.150) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.667 (+/-0.316) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.698 (+/-0.306) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.661 (+/-0.314) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.610 (+/-0.242) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.612 (+/-0.239) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.587 (+/-0.234) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.238) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.006) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.642 (+/-0.236) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.612 (+/-0.319) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.697 (+/-0.307) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.658 (+/-0.313) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.610 (+/-0.244) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.612 (+/-0.240) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.587 (+/-0.235) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.238) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.006) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.592 (+/-0.228) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.583 (+/-0.305) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.656 (+/-0.310) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.683 (+/-0.294) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.585 (+/-0.237) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.587 (+/-0.234) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.587 (+/-0.235) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.238) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.006) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.683 (+/-0.296) for {'C': 1.0, 'kernel': 'linear'}
0.664 (+/-0.314) for {'C': 10.0, 'kernel': 'linear'}
0.660 (+/-0.316) for {'C': 100.0, 'kernel': 'linear'}
0.612 (+/-0.241) for {'C': 1000.0, 'kernel': 'linear'}
0.611 (+/-0.239) for {'C': 10000.0, 'kernel': 'linear'}
0.611 (+/-0.245) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.12      0.17      0.14         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6982361488993323
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.797 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.797 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.798 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.747 (+/-0.502) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.798 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.823 (+/-0.451) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.748 (+/-0.449) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.752 (+/-0.430) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.698 (+/-0.383) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.681 (+/-0.373) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.651 (+/-0.308) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.798 (+/-0.492) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.797 (+/-0.492) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.798 (+/-0.492) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.848 (+/-0.459) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.739 (+/-0.452) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.798 (+/-0.437) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.714 (+/-0.418) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.726 (+/-0.394) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.673 (+/-0.330) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.664 (+/-0.326) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.648 (+/-0.306) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.798 (+/-0.492) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.797 (+/-0.492) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.798 (+/-0.492) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.848 (+/-0.460) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.706 (+/-0.418) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.773 (+/-0.417) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.683 (+/-0.355) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.723 (+/-0.358) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.664 (+/-0.326) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.652 (+/-0.313) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.641 (+/-0.306) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.798 (+/-0.492) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.798 (+/-0.492) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.798 (+/-0.492) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.748 (+/-0.389) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.681 (+/-0.380) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.691 (+/-0.357) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.666 (+/-0.371) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.704 (+/-0.353) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.681 (+/-0.373) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.652 (+/-0.313) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.647 (+/-0.314) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.773 (+/-0.473) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.797 (+/-0.492) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.798 (+/-0.492) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.731 (+/-0.394) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.662 (+/-0.373) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.715 (+/-0.368) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.675 (+/-0.375) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.668 (+/-0.370) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.647 (+/-0.341) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.660 (+/-0.327) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.642 (+/-0.316) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.768 (+/-0.475) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.797 (+/-0.492) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.798 (+/-0.492) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.702 (+/-0.355) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.675 (+/-0.431) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.731 (+/-0.401) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.641 (+/-0.307) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.677 (+/-0.374) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.660 (+/-0.327) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.662 (+/-0.298) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.642 (+/-0.316) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.717 (+/-0.473) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.798 (+/-0.492) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.773 (+/-0.474) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.702 (+/-0.355) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.667 (+/-0.379) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.731 (+/-0.400) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.641 (+/-0.307) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.666 (+/-0.371) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.656 (+/-0.328) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.655 (+/-0.296) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.641 (+/-0.318) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.717 (+/-0.473) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.798 (+/-0.492) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.773 (+/-0.474) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.702 (+/-0.355) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.671 (+/-0.377) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.689 (+/-0.382) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.633 (+/-0.300) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.666 (+/-0.371) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.656 (+/-0.328) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.655 (+/-0.296) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.641 (+/-0.318) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.614 (+/-0.397) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.798 (+/-0.492) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.764 (+/-0.478) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.702 (+/-0.355) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.673 (+/-0.377) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.714 (+/-0.360) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.634 (+/-0.308) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.633 (+/-0.298) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.673 (+/-0.376) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.655 (+/-0.296) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.641 (+/-0.318) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.662 (+/-0.449) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.798 (+/-0.492) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.739 (+/-0.452) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.689 (+/-0.352) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.664 (+/-0.382) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.706 (+/-0.383) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.628 (+/-0.299) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.633 (+/-0.298) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.656 (+/-0.328) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.655 (+/-0.296) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.641 (+/-0.318) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.607 (+/-0.395) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.798 (+/-0.492) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.764 (+/-0.478) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.689 (+/-0.352) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.673 (+/-0.375) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.706 (+/-0.383) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.665 (+/-0.373) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.633 (+/-0.298) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.653 (+/-0.329) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.653 (+/-0.297) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.641 (+/-0.318) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.673 (+/-0.330) for {'C': 10.0, 'kernel': 'linear'}
0.646 (+/-0.317) for {'C': 100.0, 'kernel': 'linear'}
0.620 (+/-0.321) for {'C': 1000.0, 'kernel': 'linear'}
0.618 (+/-0.322) for {'C': 10000.0, 'kernel': 'linear'}
0.624 (+/-0.319) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.642 (+/-0.236) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.642 (+/-0.236) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.667 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.617 (+/-0.238) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.667 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.683 (+/-0.296) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.666 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.699 (+/-0.309) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.666 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.665 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.665 (+/-0.314) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.667 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.642 (+/-0.236) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.667 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.658 (+/-0.217) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.666 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.700 (+/-0.309) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.666 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.699 (+/-0.307) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.665 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.665 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.664 (+/-0.314) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.667 (+/-0.316) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.642 (+/-0.236) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.667 (+/-0.316) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.683 (+/-0.296) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.666 (+/-0.315) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.700 (+/-0.309) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.681 (+/-0.294) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.699 (+/-0.306) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.665 (+/-0.316) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.664 (+/-0.315) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.664 (+/-0.313) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.667 (+/-0.316) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.667 (+/-0.316) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.667 (+/-0.316) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.683 (+/-0.296) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.665 (+/-0.316) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.698 (+/-0.307) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.665 (+/-0.313) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.698 (+/-0.305) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.665 (+/-0.317) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.664 (+/-0.315) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.664 (+/-0.314) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.666 (+/-0.315) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.642 (+/-0.236) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.667 (+/-0.316) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.682 (+/-0.296) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.664 (+/-0.315) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.699 (+/-0.308) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.664 (+/-0.312) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.656 (+/-0.307) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.640 (+/-0.326) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.664 (+/-0.317) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.663 (+/-0.313) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.666 (+/-0.314) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.642 (+/-0.236) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.667 (+/-0.316) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.682 (+/-0.294) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.664 (+/-0.316) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.699 (+/-0.308) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.664 (+/-0.311) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.665 (+/-0.314) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.664 (+/-0.316) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.688 (+/-0.299) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.663 (+/-0.313) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.641 (+/-0.323) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.667 (+/-0.316) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.667 (+/-0.316) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.682 (+/-0.294) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.680 (+/-0.294) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.682 (+/-0.295) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.664 (+/-0.312) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.664 (+/-0.313) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.664 (+/-0.316) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.688 (+/-0.298) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.663 (+/-0.313) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.641 (+/-0.323) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.667 (+/-0.316) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.667 (+/-0.316) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.682 (+/-0.294) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.696 (+/-0.306) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.666 (+/-0.315) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.663 (+/-0.311) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.664 (+/-0.313) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.664 (+/-0.317) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.688 (+/-0.298) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.663 (+/-0.313) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.591 (+/-0.317) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.667 (+/-0.316) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.666 (+/-0.316) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.682 (+/-0.294) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.697 (+/-0.306) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.682 (+/-0.295) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.663 (+/-0.311) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.664 (+/-0.313) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.664 (+/-0.317) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.688 (+/-0.298) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.662 (+/-0.313) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.616 (+/-0.323) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.667 (+/-0.316) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.666 (+/-0.316) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.682 (+/-0.294) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.679 (+/-0.294) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.658 (+/-0.311) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.663 (+/-0.311) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.664 (+/-0.313) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.664 (+/-0.317) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.688 (+/-0.299) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.662 (+/-0.314) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.590 (+/-0.314) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.667 (+/-0.316) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.666 (+/-0.316) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.682 (+/-0.294) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.697 (+/-0.306) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.658 (+/-0.311) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.664 (+/-0.311) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.664 (+/-0.313) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.664 (+/-0.316) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.688 (+/-0.299) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.662 (+/-0.314) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.665 (+/-0.315) for {'C': 10.0, 'kernel': 'linear'}
0.664 (+/-0.316) for {'C': 100.0, 'kernel': 'linear'}
0.612 (+/-0.241) for {'C': 1000.0, 'kernel': 'linear'}
0.611 (+/-0.239) for {'C': 10000.0, 'kernel': 'linear'}
0.611 (+/-0.245) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6996794871794872
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.798 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.798 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.798 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.814 (+/-0.459) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.673 (+/-0.370) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.703 (+/-0.357) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.657 (+/-0.358) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.687 (+/-0.358) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.647 (+/-0.308) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.629 (+/-0.300) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.630 (+/-0.311) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.798 (+/-0.492) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.798 (+/-0.492) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.798 (+/-0.492) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.706 (+/-0.352) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.643 (+/-0.372) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.651 (+/-0.293) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.642 (+/-0.287) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.683 (+/-0.354) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.637 (+/-0.308) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.632 (+/-0.310) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.627 (+/-0.313) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.714 (+/-0.418) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.739 (+/-0.452) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.714 (+/-0.418) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.677 (+/-0.350) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.638 (+/-0.375) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.656 (+/-0.288) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.640 (+/-0.289) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.679 (+/-0.315) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.637 (+/-0.308) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.631 (+/-0.312) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.625 (+/-0.315) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.681 (+/-0.372) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.714 (+/-0.418) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.706 (+/-0.418) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.669 (+/-0.353) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.616 (+/-0.304) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.653 (+/-0.287) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.622 (+/-0.299) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.688 (+/-0.357) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.634 (+/-0.309) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.628 (+/-0.313) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.633 (+/-0.323) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.624 (+/-0.389) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.706 (+/-0.418) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.731 (+/-0.394) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.666 (+/-0.354) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.611 (+/-0.279) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.664 (+/-0.300) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.634 (+/-0.308) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.680 (+/-0.355) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.631 (+/-0.311) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.628 (+/-0.314) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.639 (+/-0.315) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.621 (+/-0.391) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.689 (+/-0.382) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.719 (+/-0.399) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.657 (+/-0.358) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.600 (+/-0.287) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.677 (+/-0.350) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.626 (+/-0.300) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.654 (+/-0.375) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.630 (+/-0.311) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.628 (+/-0.314) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.633 (+/-0.323) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.620 (+/-0.391) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.673 (+/-0.370) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.706 (+/-0.404) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.643 (+/-0.365) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.608 (+/-0.279) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.677 (+/-0.349) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.626 (+/-0.300) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.653 (+/-0.376) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.625 (+/-0.311) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.628 (+/-0.314) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.639 (+/-0.315) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.612 (+/-0.391) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.673 (+/-0.370) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.680 (+/-0.358) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.643 (+/-0.365) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.614 (+/-0.279) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.635 (+/-0.299) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.617 (+/-0.291) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.653 (+/-0.376) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.624 (+/-0.312) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.628 (+/-0.314) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.639 (+/-0.315) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.601 (+/-0.397) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.668 (+/-0.367) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.676 (+/-0.360) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.613 (+/-0.292) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.611 (+/-0.280) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.660 (+/-0.290) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.618 (+/-0.300) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.625 (+/-0.300) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.624 (+/-0.312) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.628 (+/-0.314) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.640 (+/-0.314) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.601 (+/-0.398) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.660 (+/-0.367) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.675 (+/-0.361) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.638 (+/-0.367) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.600 (+/-0.278) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.683 (+/-0.378) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.613 (+/-0.289) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.625 (+/-0.300) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.624 (+/-0.312) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.633 (+/-0.306) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.640 (+/-0.314) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.601 (+/-0.398) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.664 (+/-0.366) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.674 (+/-0.362) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.613 (+/-0.292) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.610 (+/-0.277) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.678 (+/-0.375) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.650 (+/-0.371) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.623 (+/-0.301) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.624 (+/-0.312) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.627 (+/-0.314) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.640 (+/-0.314) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.764 (+/-0.421) for {'C': 1.0, 'kernel': 'linear'}
0.643 (+/-0.306) for {'C': 10.0, 'kernel': 'linear'}
0.630 (+/-0.312) for {'C': 100.0, 'kernel': 'linear'}
0.620 (+/-0.321) for {'C': 1000.0, 'kernel': 'linear'}
0.618 (+/-0.322) for {'C': 10000.0, 'kernel': 'linear'}
0.624 (+/-0.319) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.667 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.667 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.667 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.683 (+/-0.296) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.665 (+/-0.315) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.698 (+/-0.306) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.679 (+/-0.288) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.697 (+/-0.304) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.664 (+/-0.315) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.663 (+/-0.314) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.661 (+/-0.314) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.667 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.667 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.667 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.682 (+/-0.294) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.663 (+/-0.312) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.697 (+/-0.305) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.680 (+/-0.290) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.697 (+/-0.304) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.663 (+/-0.313) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.662 (+/-0.315) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.659 (+/-0.315) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.666 (+/-0.316) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.666 (+/-0.316) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.666 (+/-0.316) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.681 (+/-0.293) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.662 (+/-0.313) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.698 (+/-0.306) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.679 (+/-0.288) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.697 (+/-0.305) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.663 (+/-0.314) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.661 (+/-0.315) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.658 (+/-0.313) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.665 (+/-0.313) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.666 (+/-0.316) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.666 (+/-0.316) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.681 (+/-0.291) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.662 (+/-0.314) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.697 (+/-0.306) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.663 (+/-0.309) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.697 (+/-0.306) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.662 (+/-0.314) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.659 (+/-0.315) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.658 (+/-0.313) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.614 (+/-0.321) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.666 (+/-0.315) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.682 (+/-0.296) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.680 (+/-0.290) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.679 (+/-0.291) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.697 (+/-0.307) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.663 (+/-0.309) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.680 (+/-0.293) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.662 (+/-0.314) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.659 (+/-0.316) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.683 (+/-0.295) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.612 (+/-0.319) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.666 (+/-0.315) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.682 (+/-0.295) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.680 (+/-0.288) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.661 (+/-0.312) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.697 (+/-0.307) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.663 (+/-0.310) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.663 (+/-0.312) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.661 (+/-0.314) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.658 (+/-0.317) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.658 (+/-0.313) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.612 (+/-0.319) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.665 (+/-0.314) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.682 (+/-0.295) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.679 (+/-0.286) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.677 (+/-0.291) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.681 (+/-0.294) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.662 (+/-0.310) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.662 (+/-0.311) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.661 (+/-0.313) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.659 (+/-0.316) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.683 (+/-0.295) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.610 (+/-0.314) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.665 (+/-0.314) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.681 (+/-0.295) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.679 (+/-0.286) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.694 (+/-0.304) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.664 (+/-0.314) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.662 (+/-0.309) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.662 (+/-0.311) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.661 (+/-0.313) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.658 (+/-0.317) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.683 (+/-0.294) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.584 (+/-0.306) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.665 (+/-0.313) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.681 (+/-0.295) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.678 (+/-0.286) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.693 (+/-0.303) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.680 (+/-0.295) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.662 (+/-0.309) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.662 (+/-0.312) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.661 (+/-0.313) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.658 (+/-0.317) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.683 (+/-0.294) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.583 (+/-0.305) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.665 (+/-0.312) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.681 (+/-0.295) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.678 (+/-0.286) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.676 (+/-0.290) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.656 (+/-0.311) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.662 (+/-0.309) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.662 (+/-0.312) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.660 (+/-0.313) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.683 (+/-0.297) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.683 (+/-0.294) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.583 (+/-0.305) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.665 (+/-0.313) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.681 (+/-0.295) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.678 (+/-0.286) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.693 (+/-0.303) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.656 (+/-0.310) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.662 (+/-0.309) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.662 (+/-0.312) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.660 (+/-0.314) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.658 (+/-0.316) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.683 (+/-0.294) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.683 (+/-0.296) for {'C': 1.0, 'kernel': 'linear'}
0.664 (+/-0.314) for {'C': 10.0, 'kernel': 'linear'}
0.660 (+/-0.316) for {'C': 100.0, 'kernel': 'linear'}
0.612 (+/-0.241) for {'C': 1000.0, 'kernel': 'linear'}
0.611 (+/-0.239) for {'C': 10000.0, 'kernel': 'linear'}
0.611 (+/-0.245) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.12      0.17      0.14         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6982361488993323
循环迭代之前，delta is: [8.41510681e+06 2.11758237e-22]
执行tunemodel()函数前，使用的grid search parameter是：
 [{'C': array([1000000.        , 1096478.19614319, 1202264.43461741,
       1318256.73855641, 1445439.77074593, 1584893.19246111,
       1737800.82874938, 1905460.71796325, 2089296.13085404,
       2290867.65276777, 2511886.43150958]), 'kernel': ['rbf'], 'gamma': array([6.30957344e-07, 6.91830971e-07, 7.58577575e-07, 8.31763771e-07,
       9.12010839e-07, 1.00000000e-06, 1.09647820e-06, 1.20226443e-06,
       1.31825674e-06, 1.44543977e-06, 1.58489319e-06])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.798 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.798 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.773 (+/-0.474) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.773 (+/-0.474) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.773 (+/-0.474) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.823 (+/-0.451) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.798 (+/-0.437) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.748 (+/-0.449) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.798 (+/-0.437) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.747 (+/-0.449) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.748 (+/-0.449) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.798 (+/-0.492) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.798 (+/-0.492) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.773 (+/-0.474) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.748 (+/-0.449) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.773 (+/-0.474) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.798 (+/-0.437) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.798 (+/-0.437) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.748 (+/-0.449) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.798 (+/-0.437) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.748 (+/-0.449) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.748 (+/-0.449) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.773 (+/-0.474) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.773 (+/-0.474) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.773 (+/-0.474) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.748 (+/-0.449) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.739 (+/-0.452) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.798 (+/-0.437) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.798 (+/-0.437) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.723 (+/-0.417) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.798 (+/-0.437) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.748 (+/-0.449) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.748 (+/-0.449) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.748 (+/-0.449) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.748 (+/-0.449) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.748 (+/-0.449) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.748 (+/-0.449) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.714 (+/-0.418) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.798 (+/-0.437) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.798 (+/-0.437) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.723 (+/-0.417) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.773 (+/-0.416) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.748 (+/-0.449) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.748 (+/-0.449) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.748 (+/-0.449) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.748 (+/-0.449) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.748 (+/-0.449) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.748 (+/-0.449) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.714 (+/-0.418) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.798 (+/-0.437) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.798 (+/-0.437) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.723 (+/-0.417) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.773 (+/-0.416) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.748 (+/-0.449) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.723 (+/-0.417) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.739 (+/-0.452) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.748 (+/-0.449) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.714 (+/-0.418) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.723 (+/-0.417) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.714 (+/-0.418) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.798 (+/-0.437) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.773 (+/-0.416) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.723 (+/-0.417) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.748 (+/-0.389) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.739 (+/-0.452) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.714 (+/-0.418) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.739 (+/-0.452) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.748 (+/-0.449) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.714 (+/-0.418) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.714 (+/-0.418) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.714 (+/-0.418) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.798 (+/-0.437) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.739 (+/-0.392) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.689 (+/-0.375) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.739 (+/-0.392) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.714 (+/-0.418) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.706 (+/-0.418) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.739 (+/-0.452) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.748 (+/-0.449) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.714 (+/-0.418) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.723 (+/-0.417) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.714 (+/-0.418) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.773 (+/-0.417) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.740 (+/-0.392) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.714 (+/-0.352) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.731 (+/-0.394) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.714 (+/-0.418) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.691 (+/-0.422) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.739 (+/-0.452) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.748 (+/-0.449) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.714 (+/-0.418) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.723 (+/-0.417) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.714 (+/-0.418) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.773 (+/-0.417) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.740 (+/-0.392) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.714 (+/-0.352) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.740 (+/-0.392) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.698 (+/-0.423) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.674 (+/-0.383) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.714 (+/-0.418) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.739 (+/-0.452) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.714 (+/-0.418) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.714 (+/-0.418) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.760 (+/-0.426) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.773 (+/-0.417) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.740 (+/-0.392) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.706 (+/-0.353) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.740 (+/-0.392) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.689 (+/-0.422) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.674 (+/-0.383) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.706 (+/-0.418) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.739 (+/-0.452) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.714 (+/-0.418) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.714 (+/-0.418) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.760 (+/-0.426) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.773 (+/-0.417) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.731 (+/-0.394) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.706 (+/-0.353) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.701 (+/-0.351) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.668 (+/-0.371) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.683 (+/-0.355) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.673 (+/-0.330) for {'C': 10.0, 'kernel': 'linear'}
0.646 (+/-0.317) for {'C': 100.0, 'kernel': 'linear'}
0.620 (+/-0.321) for {'C': 1000.0, 'kernel': 'linear'}
0.618 (+/-0.322) for {'C': 10000.0, 'kernel': 'linear'}
0.624 (+/-0.319) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.667 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.667 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.667 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.667 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.667 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.683 (+/-0.296) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.683 (+/-0.296) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.666 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.683 (+/-0.296) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.641 (+/-0.236) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.666 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.667 (+/-0.316) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.667 (+/-0.316) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.667 (+/-0.316) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.666 (+/-0.316) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.667 (+/-0.316) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.683 (+/-0.296) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.683 (+/-0.296) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.666 (+/-0.316) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.683 (+/-0.296) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.666 (+/-0.316) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.666 (+/-0.316) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.667 (+/-0.316) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.667 (+/-0.316) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.667 (+/-0.316) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.666 (+/-0.316) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.666 (+/-0.316) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.683 (+/-0.296) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.683 (+/-0.296) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.666 (+/-0.316) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.683 (+/-0.296) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.666 (+/-0.316) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.666 (+/-0.316) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.666 (+/-0.316) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.666 (+/-0.316) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.666 (+/-0.316) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.666 (+/-0.316) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.666 (+/-0.316) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.700 (+/-0.309) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.683 (+/-0.296) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.666 (+/-0.316) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.683 (+/-0.296) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.666 (+/-0.317) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.666 (+/-0.316) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.666 (+/-0.316) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.666 (+/-0.316) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.666 (+/-0.316) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.666 (+/-0.316) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.666 (+/-0.316) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.700 (+/-0.309) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.683 (+/-0.296) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.666 (+/-0.316) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.683 (+/-0.296) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.666 (+/-0.317) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.666 (+/-0.316) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.666 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.666 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.666 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.666 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.666 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.700 (+/-0.309) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.683 (+/-0.296) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.666 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.683 (+/-0.296) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.666 (+/-0.317) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.666 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.666 (+/-0.316) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.666 (+/-0.316) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.666 (+/-0.316) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.666 (+/-0.316) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.666 (+/-0.316) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.700 (+/-0.309) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.683 (+/-0.296) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.666 (+/-0.316) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.683 (+/-0.296) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.666 (+/-0.317) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.666 (+/-0.315) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.666 (+/-0.316) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.666 (+/-0.316) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.666 (+/-0.316) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.666 (+/-0.316) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.666 (+/-0.316) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.700 (+/-0.309) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.699 (+/-0.308) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.682 (+/-0.296) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.682 (+/-0.296) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.666 (+/-0.317) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.665 (+/-0.315) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.666 (+/-0.316) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.666 (+/-0.316) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.666 (+/-0.316) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.666 (+/-0.316) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.666 (+/-0.316) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.700 (+/-0.309) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.699 (+/-0.308) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.682 (+/-0.296) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.699 (+/-0.308) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.665 (+/-0.316) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.665 (+/-0.314) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.666 (+/-0.316) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.666 (+/-0.316) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.666 (+/-0.316) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.666 (+/-0.316) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.683 (+/-0.296) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.700 (+/-0.309) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.699 (+/-0.308) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.682 (+/-0.295) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.699 (+/-0.308) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.665 (+/-0.316) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.665 (+/-0.314) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.666 (+/-0.315) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.666 (+/-0.316) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.666 (+/-0.316) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.666 (+/-0.316) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.683 (+/-0.296) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.700 (+/-0.309) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.699 (+/-0.308) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.682 (+/-0.295) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.698 (+/-0.307) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.665 (+/-0.316) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.681 (+/-0.294) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.665 (+/-0.315) for {'C': 10.0, 'kernel': 'linear'}
0.664 (+/-0.316) for {'C': 100.0, 'kernel': 'linear'}
0.612 (+/-0.241) for {'C': 1000.0, 'kernel': 'linear'}
0.611 (+/-0.239) for {'C': 10000.0, 'kernel': 'linear'}
0.611 (+/-0.245) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6996794871794872
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.673 (+/-0.370) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.723 (+/-0.417) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.714 (+/-0.418) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.689 (+/-0.352) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.684 (+/-0.355) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.703 (+/-0.357) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.714 (+/-0.399) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.636 (+/-0.286) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.624 (+/-0.288) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.645 (+/-0.323) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.657 (+/-0.358) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.668 (+/-0.367) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.723 (+/-0.417) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.714 (+/-0.418) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.689 (+/-0.352) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.679 (+/-0.356) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.690 (+/-0.354) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.704 (+/-0.405) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.632 (+/-0.287) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.624 (+/-0.288) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.637 (+/-0.307) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.632 (+/-0.288) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.663 (+/-0.368) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.714 (+/-0.418) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.698 (+/-0.383) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.674 (+/-0.358) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.675 (+/-0.359) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.691 (+/-0.357) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.695 (+/-0.410) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.631 (+/-0.287) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.627 (+/-0.287) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.641 (+/-0.308) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.632 (+/-0.288) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.654 (+/-0.367) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.681 (+/-0.380) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.683 (+/-0.386) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.655 (+/-0.358) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.664 (+/-0.357) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.656 (+/-0.292) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.674 (+/-0.359) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.629 (+/-0.288) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.634 (+/-0.287) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.632 (+/-0.301) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.637 (+/-0.288) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.644 (+/-0.371) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.674 (+/-0.383) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.653 (+/-0.376) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.644 (+/-0.363) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.665 (+/-0.356) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.651 (+/-0.293) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.674 (+/-0.359) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.629 (+/-0.288) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.634 (+/-0.286) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.632 (+/-0.301) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.641 (+/-0.288) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.643 (+/-0.372) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.659 (+/-0.375) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.626 (+/-0.310) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.608 (+/-0.298) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.662 (+/-0.355) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.651 (+/-0.293) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.674 (+/-0.359) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.631 (+/-0.288) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.634 (+/-0.286) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.636 (+/-0.302) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.642 (+/-0.287) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.643 (+/-0.372) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.654 (+/-0.371) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.621 (+/-0.303) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.608 (+/-0.298) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.660 (+/-0.357) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.646 (+/-0.287) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.676 (+/-0.358) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.631 (+/-0.288) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.634 (+/-0.286) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.626 (+/-0.292) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.641 (+/-0.288) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.641 (+/-0.373) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.654 (+/-0.371) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.615 (+/-0.301) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.609 (+/-0.298) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.662 (+/-0.356) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.658 (+/-0.292) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.679 (+/-0.357) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.632 (+/-0.278) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.622 (+/-0.276) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.631 (+/-0.299) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.624 (+/-0.300) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.639 (+/-0.374) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.624 (+/-0.299) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.612 (+/-0.298) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.609 (+/-0.298) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.660 (+/-0.359) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.651 (+/-0.293) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.681 (+/-0.355) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.614 (+/-0.289) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.629 (+/-0.275) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.631 (+/-0.299) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.624 (+/-0.300) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.638 (+/-0.375) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.620 (+/-0.299) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.612 (+/-0.298) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.610 (+/-0.298) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.661 (+/-0.358) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.653 (+/-0.287) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.681 (+/-0.355) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.639 (+/-0.287) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.636 (+/-0.278) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.631 (+/-0.299) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.624 (+/-0.301) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.638 (+/-0.375) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.620 (+/-0.299) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.627 (+/-0.290) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.610 (+/-0.298) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.665 (+/-0.355) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.656 (+/-0.288) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.685 (+/-0.355) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.643 (+/-0.287) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.636 (+/-0.278) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.631 (+/-0.299) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.640 (+/-0.289) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.764 (+/-0.421) for {'C': 1.0, 'kernel': 'linear'}
0.643 (+/-0.306) for {'C': 10.0, 'kernel': 'linear'}
0.630 (+/-0.312) for {'C': 100.0, 'kernel': 'linear'}
0.620 (+/-0.321) for {'C': 1000.0, 'kernel': 'linear'}
0.618 (+/-0.322) for {'C': 10000.0, 'kernel': 'linear'}
0.624 (+/-0.319) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.20      0.17      0.18         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.99      0.99       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.665 (+/-0.315) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.666 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.666 (+/-0.317) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.682 (+/-0.294) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.698 (+/-0.304) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.698 (+/-0.306) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.698 (+/-0.308) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.680 (+/-0.291) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.679 (+/-0.291) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.664 (+/-0.315) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.679 (+/-0.288) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.665 (+/-0.314) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.666 (+/-0.316) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.666 (+/-0.317) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.682 (+/-0.294) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.697 (+/-0.304) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.698 (+/-0.306) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.698 (+/-0.307) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.680 (+/-0.291) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.679 (+/-0.290) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.664 (+/-0.315) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.679 (+/-0.288) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.665 (+/-0.314) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.666 (+/-0.316) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.665 (+/-0.316) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.681 (+/-0.293) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.697 (+/-0.302) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.698 (+/-0.306) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.697 (+/-0.307) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.680 (+/-0.290) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.679 (+/-0.290) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.664 (+/-0.315) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.679 (+/-0.288) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.664 (+/-0.313) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.665 (+/-0.316) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.665 (+/-0.315) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.680 (+/-0.292) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.696 (+/-0.302) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.697 (+/-0.306) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.697 (+/-0.307) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.679 (+/-0.290) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.680 (+/-0.290) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.664 (+/-0.314) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.680 (+/-0.288) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.664 (+/-0.313) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.665 (+/-0.315) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.664 (+/-0.314) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.679 (+/-0.290) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.697 (+/-0.302) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.697 (+/-0.305) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.697 (+/-0.307) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.679 (+/-0.289) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.680 (+/-0.291) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.664 (+/-0.314) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.680 (+/-0.289) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.663 (+/-0.312) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.664 (+/-0.315) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.663 (+/-0.314) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.662 (+/-0.310) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.697 (+/-0.302) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.697 (+/-0.305) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.697 (+/-0.307) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.679 (+/-0.289) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.680 (+/-0.291) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.664 (+/-0.314) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.680 (+/-0.290) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.663 (+/-0.313) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.664 (+/-0.314) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.663 (+/-0.314) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.662 (+/-0.310) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.696 (+/-0.302) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.697 (+/-0.305) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.698 (+/-0.307) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.679 (+/-0.289) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.680 (+/-0.292) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.664 (+/-0.314) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.680 (+/-0.289) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.663 (+/-0.313) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.664 (+/-0.314) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.662 (+/-0.313) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.662 (+/-0.309) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.696 (+/-0.303) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.698 (+/-0.306) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.698 (+/-0.307) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.680 (+/-0.289) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.679 (+/-0.291) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.664 (+/-0.315) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.663 (+/-0.309) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.663 (+/-0.313) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.664 (+/-0.312) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.662 (+/-0.312) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.662 (+/-0.308) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.680 (+/-0.291) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.697 (+/-0.305) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.698 (+/-0.307) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.663 (+/-0.308) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.680 (+/-0.292) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.664 (+/-0.315) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.663 (+/-0.309) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.662 (+/-0.313) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.663 (+/-0.311) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.662 (+/-0.312) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.662 (+/-0.308) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.680 (+/-0.291) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.697 (+/-0.305) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.698 (+/-0.307) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.680 (+/-0.288) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.697 (+/-0.305) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.664 (+/-0.315) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.663 (+/-0.308) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.662 (+/-0.313) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.663 (+/-0.311) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.679 (+/-0.291) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.662 (+/-0.308) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.697 (+/-0.303) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.698 (+/-0.306) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.698 (+/-0.307) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.680 (+/-0.288) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.697 (+/-0.305) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.664 (+/-0.315) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.679 (+/-0.288) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.683 (+/-0.296) for {'C': 1.0, 'kernel': 'linear'}
0.664 (+/-0.314) for {'C': 10.0, 'kernel': 'linear'}
0.660 (+/-0.316) for {'C': 100.0, 'kernel': 'linear'}
0.612 (+/-0.241) for {'C': 1000.0, 'kernel': 'linear'}
0.611 (+/-0.239) for {'C': 10000.0, 'kernel': 'linear'}
0.611 (+/-0.245) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.14      0.17      0.15         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6983958900156649
第0轮loop中，tunemodel函数得到的最优参数是： ([{'C': array([1202264.43461741, 1224616.19926505, 1247383.51424294,
       1270574.10520854, 1294195.84144999, 1318256.73855641,
       1342764.96113787, 1367728.82559585, 1393156.8029453 ,
       1419057.52168909, 1445439.77074593]), 'kernel': ['rbf'], 'gamma': array([9.12010839e-07, 9.28966387e-07, 9.46237161e-07, 9.63829024e-07,
       9.81747943e-07, 1.00000000e-06, 1.01859139e-06, 1.03752842e-06,
       1.05681751e-06, 1.07646521e-06, 1.09647820e-06])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}], {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}, 0.6996794871794872)
这是第0次迭代微调C和gamma。
第0次迭代，得到delta： [266636.45390471      0.        ]
执行tunemodel()函数前，使用的grid search parameter是：
 [{'C': array([1202264.43461741, 1224616.19926505, 1247383.51424294,
       1270574.10520854, 1294195.84144999, 1318256.73855641,
       1342764.96113787, 1367728.82559585, 1393156.8029453 ,
       1419057.52168909, 1445439.77074593]), 'kernel': ['rbf'], 'gamma': array([9.12010839e-07, 9.28966387e-07, 9.46237161e-07, 9.63829024e-07,
       9.81747943e-07, 1.00000000e-06, 1.01859139e-06, 1.03752842e-06,
       1.05681751e-06, 1.07646521e-06, 1.09647820e-06])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.739 (+/-0.452) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.714 (+/-0.418) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.748 (+/-0.449) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.748 (+/-0.449) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.723 (+/-0.417) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.798 (+/-0.437) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.798 (+/-0.437) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.764 (+/-0.421) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.037528415818012e-06}
0.764 (+/-0.421) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.05681750921366e-06}
0.798 (+/-0.437) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.076465213629836e-06}
0.798 (+/-0.437) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.739 (+/-0.452) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.714 (+/-0.418) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.798 (+/-0.437) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.748 (+/-0.449) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.723 (+/-0.417) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.798 (+/-0.437) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.798 (+/-0.437) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.764 (+/-0.421) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.037528415818012e-06}
0.764 (+/-0.421) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.05681750921366e-06}
0.798 (+/-0.437) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.076465213629836e-06}
0.798 (+/-0.437) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.739 (+/-0.452) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.714 (+/-0.418) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.798 (+/-0.437) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.748 (+/-0.449) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.748 (+/-0.449) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.798 (+/-0.437) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.798 (+/-0.437) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.764 (+/-0.421) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.037528415818012e-06}
0.764 (+/-0.421) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.05681750921366e-06}
0.798 (+/-0.437) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.076465213629836e-06}
0.798 (+/-0.437) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.714 (+/-0.418) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.714 (+/-0.418) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.798 (+/-0.437) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.723 (+/-0.417) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.723 (+/-0.417) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.798 (+/-0.437) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.798 (+/-0.437) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.739 (+/-0.392) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.037528415818012e-06}
0.764 (+/-0.421) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.05681750921366e-06}
0.798 (+/-0.437) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.076465213629836e-06}
0.798 (+/-0.437) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.714 (+/-0.418) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.714 (+/-0.418) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.798 (+/-0.437) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.723 (+/-0.417) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.723 (+/-0.417) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.798 (+/-0.437) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.798 (+/-0.437) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.739 (+/-0.392) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 1.037528415818012e-06}
0.764 (+/-0.421) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 1.05681750921366e-06}
0.798 (+/-0.437) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 1.076465213629836e-06}
0.798 (+/-0.437) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.714 (+/-0.418) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.714 (+/-0.418) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.798 (+/-0.437) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.714 (+/-0.418) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.723 (+/-0.417) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.798 (+/-0.437) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.798 (+/-0.437) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.731 (+/-0.394) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.037528415818012e-06}
0.764 (+/-0.421) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.05681750921366e-06}
0.781 (+/-0.417) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.076465213629836e-06}
0.798 (+/-0.437) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.714 (+/-0.418) for {'C': 1342764.9611378654, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.714 (+/-0.418) for {'C': 1342764.9611378654, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.773 (+/-0.416) for {'C': 1342764.9611378654, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.714 (+/-0.418) for {'C': 1342764.9611378654, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.723 (+/-0.417) for {'C': 1342764.9611378654, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.798 (+/-0.437) for {'C': 1342764.9611378654, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.798 (+/-0.437) for {'C': 1342764.9611378654, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.731 (+/-0.394) for {'C': 1342764.9611378654, 'kernel': 'rbf', 'gamma': 1.037528415818012e-06}
0.764 (+/-0.421) for {'C': 1342764.9611378654, 'kernel': 'rbf', 'gamma': 1.05681750921366e-06}
0.781 (+/-0.417) for {'C': 1342764.9611378654, 'kernel': 'rbf', 'gamma': 1.076465213629836e-06}
0.798 (+/-0.437) for {'C': 1342764.9611378654, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.714 (+/-0.418) for {'C': 1367728.8255958504, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.714 (+/-0.418) for {'C': 1367728.8255958504, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.723 (+/-0.417) for {'C': 1367728.8255958504, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.714 (+/-0.418) for {'C': 1367728.8255958504, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.723 (+/-0.417) for {'C': 1367728.8255958504, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.798 (+/-0.437) for {'C': 1367728.8255958504, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.798 (+/-0.437) for {'C': 1367728.8255958504, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.714 (+/-0.360) for {'C': 1367728.8255958504, 'kernel': 'rbf', 'gamma': 1.037528415818012e-06}
0.764 (+/-0.421) for {'C': 1367728.8255958504, 'kernel': 'rbf', 'gamma': 1.05681750921366e-06}
0.756 (+/-0.391) for {'C': 1367728.8255958504, 'kernel': 'rbf', 'gamma': 1.076465213629836e-06}
0.798 (+/-0.437) for {'C': 1367728.8255958504, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.714 (+/-0.418) for {'C': 1393156.8029453037, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.714 (+/-0.418) for {'C': 1393156.8029453037, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.723 (+/-0.417) for {'C': 1393156.8029453037, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.714 (+/-0.418) for {'C': 1393156.8029453037, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.748 (+/-0.449) for {'C': 1393156.8029453037, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.798 (+/-0.437) for {'C': 1393156.8029453037, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.798 (+/-0.437) for {'C': 1393156.8029453037, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.714 (+/-0.360) for {'C': 1393156.8029453037, 'kernel': 'rbf', 'gamma': 1.037528415818012e-06}
0.731 (+/-0.394) for {'C': 1393156.8029453037, 'kernel': 'rbf', 'gamma': 1.05681750921366e-06}
0.756 (+/-0.391) for {'C': 1393156.8029453037, 'kernel': 'rbf', 'gamma': 1.076465213629836e-06}
0.798 (+/-0.437) for {'C': 1393156.8029453037, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.714 (+/-0.418) for {'C': 1419057.5216890923, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.714 (+/-0.418) for {'C': 1419057.5216890923, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.723 (+/-0.417) for {'C': 1419057.5216890923, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.714 (+/-0.418) for {'C': 1419057.5216890923, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.748 (+/-0.449) for {'C': 1419057.5216890923, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.798 (+/-0.437) for {'C': 1419057.5216890923, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.798 (+/-0.437) for {'C': 1419057.5216890923, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.714 (+/-0.360) for {'C': 1419057.5216890923, 'kernel': 'rbf', 'gamma': 1.037528415818012e-06}
0.731 (+/-0.394) for {'C': 1419057.5216890923, 'kernel': 'rbf', 'gamma': 1.05681750921366e-06}
0.748 (+/-0.388) for {'C': 1419057.5216890923, 'kernel': 'rbf', 'gamma': 1.076465213629836e-06}
0.798 (+/-0.437) for {'C': 1419057.5216890923, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.714 (+/-0.418) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.714 (+/-0.418) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.723 (+/-0.417) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.714 (+/-0.418) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.723 (+/-0.417) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.798 (+/-0.437) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.798 (+/-0.437) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.714 (+/-0.360) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.037528415818012e-06}
0.731 (+/-0.394) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.05681750921366e-06}
0.773 (+/-0.416) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.076465213629836e-06}
0.798 (+/-0.437) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.673 (+/-0.330) for {'C': 10.0, 'kernel': 'linear'}
0.646 (+/-0.317) for {'C': 100.0, 'kernel': 'linear'}
0.620 (+/-0.321) for {'C': 1000.0, 'kernel': 'linear'}
0.618 (+/-0.322) for {'C': 10000.0, 'kernel': 'linear'}
0.624 (+/-0.319) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.666 (+/-0.316) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.666 (+/-0.316) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.666 (+/-0.316) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.666 (+/-0.316) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.666 (+/-0.316) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.683 (+/-0.296) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.683 (+/-0.296) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.683 (+/-0.296) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.037528415818012e-06}
0.683 (+/-0.296) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.05681750921366e-06}
0.683 (+/-0.296) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.076465213629836e-06}
0.683 (+/-0.296) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.666 (+/-0.316) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.666 (+/-0.316) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.683 (+/-0.296) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.666 (+/-0.316) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.666 (+/-0.316) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.683 (+/-0.296) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.683 (+/-0.296) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.683 (+/-0.296) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.037528415818012e-06}
0.683 (+/-0.296) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.05681750921366e-06}
0.683 (+/-0.296) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.076465213629836e-06}
0.683 (+/-0.296) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.666 (+/-0.316) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.666 (+/-0.316) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.683 (+/-0.296) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.666 (+/-0.316) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.666 (+/-0.316) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.700 (+/-0.309) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.683 (+/-0.296) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.683 (+/-0.296) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.037528415818012e-06}
0.683 (+/-0.296) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.05681750921366e-06}
0.683 (+/-0.296) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.076465213629836e-06}
0.683 (+/-0.296) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.666 (+/-0.316) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.666 (+/-0.316) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.683 (+/-0.296) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.666 (+/-0.316) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.666 (+/-0.316) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.700 (+/-0.309) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.683 (+/-0.296) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.683 (+/-0.296) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.037528415818012e-06}
0.683 (+/-0.296) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.05681750921366e-06}
0.683 (+/-0.296) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.076465213629836e-06}
0.683 (+/-0.296) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.666 (+/-0.316) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.666 (+/-0.316) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.683 (+/-0.296) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.666 (+/-0.316) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.666 (+/-0.316) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.700 (+/-0.309) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.683 (+/-0.296) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.683 (+/-0.296) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 1.037528415818012e-06}
0.683 (+/-0.296) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 1.05681750921366e-06}
0.683 (+/-0.296) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 1.076465213629836e-06}
0.683 (+/-0.296) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.666 (+/-0.316) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.666 (+/-0.316) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.683 (+/-0.296) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.666 (+/-0.316) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.666 (+/-0.316) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.700 (+/-0.309) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.683 (+/-0.296) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.682 (+/-0.296) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.037528415818012e-06}
0.683 (+/-0.296) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.05681750921366e-06}
0.683 (+/-0.295) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.076465213629836e-06}
0.683 (+/-0.296) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.666 (+/-0.316) for {'C': 1342764.9611378654, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.666 (+/-0.316) for {'C': 1342764.9611378654, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.683 (+/-0.296) for {'C': 1342764.9611378654, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.666 (+/-0.316) for {'C': 1342764.9611378654, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.666 (+/-0.316) for {'C': 1342764.9611378654, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.700 (+/-0.309) for {'C': 1342764.9611378654, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.683 (+/-0.296) for {'C': 1342764.9611378654, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.682 (+/-0.296) for {'C': 1342764.9611378654, 'kernel': 'rbf', 'gamma': 1.037528415818012e-06}
0.683 (+/-0.296) for {'C': 1342764.9611378654, 'kernel': 'rbf', 'gamma': 1.05681750921366e-06}
0.683 (+/-0.295) for {'C': 1342764.9611378654, 'kernel': 'rbf', 'gamma': 1.076465213629836e-06}
0.683 (+/-0.296) for {'C': 1342764.9611378654, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.666 (+/-0.316) for {'C': 1367728.8255958504, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.666 (+/-0.316) for {'C': 1367728.8255958504, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.666 (+/-0.316) for {'C': 1367728.8255958504, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.666 (+/-0.316) for {'C': 1367728.8255958504, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.666 (+/-0.316) for {'C': 1367728.8255958504, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.700 (+/-0.309) for {'C': 1367728.8255958504, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.683 (+/-0.296) for {'C': 1367728.8255958504, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.682 (+/-0.295) for {'C': 1367728.8255958504, 'kernel': 'rbf', 'gamma': 1.037528415818012e-06}
0.683 (+/-0.296) for {'C': 1367728.8255958504, 'kernel': 'rbf', 'gamma': 1.05681750921366e-06}
0.683 (+/-0.295) for {'C': 1367728.8255958504, 'kernel': 'rbf', 'gamma': 1.076465213629836e-06}
0.683 (+/-0.296) for {'C': 1367728.8255958504, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.666 (+/-0.316) for {'C': 1393156.8029453037, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.666 (+/-0.316) for {'C': 1393156.8029453037, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.666 (+/-0.316) for {'C': 1393156.8029453037, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.666 (+/-0.316) for {'C': 1393156.8029453037, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.666 (+/-0.316) for {'C': 1393156.8029453037, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.700 (+/-0.309) for {'C': 1393156.8029453037, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.683 (+/-0.296) for {'C': 1393156.8029453037, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.682 (+/-0.295) for {'C': 1393156.8029453037, 'kernel': 'rbf', 'gamma': 1.037528415818012e-06}
0.682 (+/-0.296) for {'C': 1393156.8029453037, 'kernel': 'rbf', 'gamma': 1.05681750921366e-06}
0.683 (+/-0.295) for {'C': 1393156.8029453037, 'kernel': 'rbf', 'gamma': 1.076465213629836e-06}
0.683 (+/-0.296) for {'C': 1393156.8029453037, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.666 (+/-0.316) for {'C': 1419057.5216890923, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.666 (+/-0.316) for {'C': 1419057.5216890923, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.666 (+/-0.316) for {'C': 1419057.5216890923, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.666 (+/-0.316) for {'C': 1419057.5216890923, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.666 (+/-0.316) for {'C': 1419057.5216890923, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.700 (+/-0.309) for {'C': 1419057.5216890923, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.683 (+/-0.296) for {'C': 1419057.5216890923, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.682 (+/-0.295) for {'C': 1419057.5216890923, 'kernel': 'rbf', 'gamma': 1.037528415818012e-06}
0.682 (+/-0.296) for {'C': 1419057.5216890923, 'kernel': 'rbf', 'gamma': 1.05681750921366e-06}
0.683 (+/-0.294) for {'C': 1419057.5216890923, 'kernel': 'rbf', 'gamma': 1.076465213629836e-06}
0.683 (+/-0.296) for {'C': 1419057.5216890923, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.666 (+/-0.316) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.666 (+/-0.316) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.666 (+/-0.316) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.666 (+/-0.316) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.666 (+/-0.316) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.700 (+/-0.309) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.683 (+/-0.296) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.682 (+/-0.295) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.037528415818012e-06}
0.682 (+/-0.296) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.05681750921366e-06}
0.683 (+/-0.296) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.076465213629836e-06}
0.683 (+/-0.296) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.665 (+/-0.315) for {'C': 10.0, 'kernel': 'linear'}
0.664 (+/-0.316) for {'C': 100.0, 'kernel': 'linear'}
0.612 (+/-0.241) for {'C': 1000.0, 'kernel': 'linear'}
0.611 (+/-0.239) for {'C': 10000.0, 'kernel': 'linear'}
0.611 (+/-0.245) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6996794871794872
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.675 (+/-0.359) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.659 (+/-0.359) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.660 (+/-0.357) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.663 (+/-0.357) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.670 (+/-0.353) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.691 (+/-0.357) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.666 (+/-0.354) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.640 (+/-0.299) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.037528415818012e-06}
0.669 (+/-0.363) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.05681750921366e-06}
0.693 (+/-0.412) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.076465213629836e-06}
0.695 (+/-0.410) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.667 (+/-0.356) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.659 (+/-0.359) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.660 (+/-0.357) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.659 (+/-0.358) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.662 (+/-0.356) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.681 (+/-0.355) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.664 (+/-0.356) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.648 (+/-0.315) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.037528415818012e-06}
0.669 (+/-0.363) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.05681750921366e-06}
0.693 (+/-0.412) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.076465213629836e-06}
0.674 (+/-0.359) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.668 (+/-0.355) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.659 (+/-0.359) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.660 (+/-0.357) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.654 (+/-0.360) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.664 (+/-0.355) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.689 (+/-0.358) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.660 (+/-0.357) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.635 (+/-0.293) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.037528415818012e-06}
0.669 (+/-0.363) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.05681750921366e-06}
0.693 (+/-0.412) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.076465213629836e-06}
0.674 (+/-0.359) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.668 (+/-0.355) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.652 (+/-0.361) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.658 (+/-0.358) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.654 (+/-0.360) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.664 (+/-0.355) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.681 (+/-0.355) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.660 (+/-0.357) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.665 (+/-0.366) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.037528415818012e-06}
0.669 (+/-0.363) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.05681750921366e-06}
0.668 (+/-0.362) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.076465213629836e-06}
0.674 (+/-0.359) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.664 (+/-0.357) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.652 (+/-0.361) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.656 (+/-0.358) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.654 (+/-0.360) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.664 (+/-0.355) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.656 (+/-0.292) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.634 (+/-0.288) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.665 (+/-0.366) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 1.037528415818012e-06}
0.669 (+/-0.363) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 1.05681750921366e-06}
0.668 (+/-0.362) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 1.076465213629836e-06}
0.674 (+/-0.359) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.664 (+/-0.357) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.651 (+/-0.362) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.656 (+/-0.358) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.652 (+/-0.362) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.662 (+/-0.356) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.656 (+/-0.292) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.635 (+/-0.287) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.635 (+/-0.293) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.037528415818012e-06}
0.668 (+/-0.364) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.05681750921366e-06}
0.672 (+/-0.361) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.076465213629836e-06}
0.674 (+/-0.359) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.664 (+/-0.357) for {'C': 1342764.9611378654, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.657 (+/-0.359) for {'C': 1342764.9611378654, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.656 (+/-0.358) for {'C': 1342764.9611378654, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.651 (+/-0.363) for {'C': 1342764.9611378654, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.666 (+/-0.355) for {'C': 1342764.9611378654, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.681 (+/-0.355) for {'C': 1342764.9611378654, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.637 (+/-0.288) for {'C': 1342764.9611378654, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.665 (+/-0.366) for {'C': 1342764.9611378654, 'kernel': 'rbf', 'gamma': 1.037528415818012e-06}
0.668 (+/-0.364) for {'C': 1342764.9611378654, 'kernel': 'rbf', 'gamma': 1.05681750921366e-06}
0.668 (+/-0.362) for {'C': 1342764.9611378654, 'kernel': 'rbf', 'gamma': 1.076465213629836e-06}
0.674 (+/-0.359) for {'C': 1342764.9611378654, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.664 (+/-0.357) for {'C': 1367728.8255958504, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.657 (+/-0.359) for {'C': 1367728.8255958504, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.656 (+/-0.358) for {'C': 1367728.8255958504, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.651 (+/-0.363) for {'C': 1367728.8255958504, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.666 (+/-0.355) for {'C': 1367728.8255958504, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.651 (+/-0.293) for {'C': 1367728.8255958504, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.640 (+/-0.290) for {'C': 1367728.8255958504, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.665 (+/-0.366) for {'C': 1367728.8255958504, 'kernel': 'rbf', 'gamma': 1.037528415818012e-06}
0.673 (+/-0.362) for {'C': 1367728.8255958504, 'kernel': 'rbf', 'gamma': 1.05681750921366e-06}
0.670 (+/-0.362) for {'C': 1367728.8255958504, 'kernel': 'rbf', 'gamma': 1.076465213629836e-06}
0.674 (+/-0.359) for {'C': 1367728.8255958504, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.667 (+/-0.354) for {'C': 1393156.8029453037, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.657 (+/-0.359) for {'C': 1393156.8029453037, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.656 (+/-0.358) for {'C': 1393156.8029453037, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.651 (+/-0.363) for {'C': 1393156.8029453037, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.666 (+/-0.355) for {'C': 1393156.8029453037, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.651 (+/-0.293) for {'C': 1393156.8029453037, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.640 (+/-0.290) for {'C': 1393156.8029453037, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.648 (+/-0.315) for {'C': 1393156.8029453037, 'kernel': 'rbf', 'gamma': 1.037528415818012e-06}
0.673 (+/-0.362) for {'C': 1393156.8029453037, 'kernel': 'rbf', 'gamma': 1.05681750921366e-06}
0.670 (+/-0.362) for {'C': 1393156.8029453037, 'kernel': 'rbf', 'gamma': 1.076465213629836e-06}
0.674 (+/-0.359) for {'C': 1393156.8029453037, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.665 (+/-0.356) for {'C': 1419057.5216890923, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.657 (+/-0.359) for {'C': 1419057.5216890923, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.657 (+/-0.357) for {'C': 1419057.5216890923, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.655 (+/-0.362) for {'C': 1419057.5216890923, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.666 (+/-0.355) for {'C': 1419057.5216890923, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.651 (+/-0.293) for {'C': 1419057.5216890923, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.637 (+/-0.288) for {'C': 1419057.5216890923, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.648 (+/-0.315) for {'C': 1419057.5216890923, 'kernel': 'rbf', 'gamma': 1.037528415818012e-06}
0.673 (+/-0.362) for {'C': 1419057.5216890923, 'kernel': 'rbf', 'gamma': 1.05681750921366e-06}
0.675 (+/-0.360) for {'C': 1419057.5216890923, 'kernel': 'rbf', 'gamma': 1.076465213629836e-06}
0.674 (+/-0.359) for {'C': 1419057.5216890923, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.665 (+/-0.356) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.657 (+/-0.359) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.657 (+/-0.357) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.655 (+/-0.362) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.666 (+/-0.355) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.651 (+/-0.293) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.640 (+/-0.290) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.650 (+/-0.314) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.037528415818012e-06}
0.673 (+/-0.362) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.05681750921366e-06}
0.672 (+/-0.361) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.076465213629836e-06}
0.674 (+/-0.359) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.764 (+/-0.421) for {'C': 1.0, 'kernel': 'linear'}
0.643 (+/-0.306) for {'C': 10.0, 'kernel': 'linear'}
0.630 (+/-0.312) for {'C': 100.0, 'kernel': 'linear'}
0.620 (+/-0.321) for {'C': 1000.0, 'kernel': 'linear'}
0.618 (+/-0.322) for {'C': 10000.0, 'kernel': 'linear'}
0.624 (+/-0.319) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.20      0.17      0.18         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.99      0.99       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.697 (+/-0.302) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.696 (+/-0.302) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.697 (+/-0.303) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.680 (+/-0.292) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.697 (+/-0.305) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.698 (+/-0.306) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.697 (+/-0.305) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.696 (+/-0.305) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.037528415818012e-06}
0.697 (+/-0.307) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.05681750921366e-06}
0.697 (+/-0.307) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.076465213629836e-06}
0.697 (+/-0.307) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.697 (+/-0.302) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.696 (+/-0.302) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.697 (+/-0.303) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.680 (+/-0.292) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.697 (+/-0.304) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.698 (+/-0.306) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.697 (+/-0.304) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.697 (+/-0.306) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.037528415818012e-06}
0.697 (+/-0.307) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.05681750921366e-06}
0.697 (+/-0.307) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.076465213629836e-06}
0.697 (+/-0.307) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.697 (+/-0.302) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.696 (+/-0.302) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.697 (+/-0.303) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.680 (+/-0.292) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.697 (+/-0.304) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.698 (+/-0.306) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.697 (+/-0.304) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.696 (+/-0.305) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.037528415818012e-06}
0.697 (+/-0.307) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.05681750921366e-06}
0.697 (+/-0.307) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.076465213629836e-06}
0.697 (+/-0.307) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.697 (+/-0.302) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.680 (+/-0.290) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.696 (+/-0.303) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.680 (+/-0.292) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.697 (+/-0.304) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.698 (+/-0.306) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.697 (+/-0.304) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.697 (+/-0.306) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.037528415818012e-06}
0.697 (+/-0.307) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.05681750921366e-06}
0.697 (+/-0.306) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.076465213629836e-06}
0.697 (+/-0.307) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.696 (+/-0.302) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.680 (+/-0.290) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.696 (+/-0.303) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.680 (+/-0.292) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.697 (+/-0.304) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.697 (+/-0.306) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.696 (+/-0.304) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.697 (+/-0.306) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 1.037528415818012e-06}
0.697 (+/-0.307) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 1.05681750921366e-06}
0.697 (+/-0.306) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 1.076465213629836e-06}
0.697 (+/-0.307) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.696 (+/-0.302) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.679 (+/-0.290) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.696 (+/-0.303) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.680 (+/-0.292) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.697 (+/-0.304) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.697 (+/-0.306) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.696 (+/-0.304) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.696 (+/-0.305) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.037528415818012e-06}
0.697 (+/-0.307) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.05681750921366e-06}
0.697 (+/-0.307) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.076465213629836e-06}
0.697 (+/-0.307) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.696 (+/-0.302) for {'C': 1342764.9611378654, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.696 (+/-0.302) for {'C': 1342764.9611378654, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.696 (+/-0.303) for {'C': 1342764.9611378654, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.680 (+/-0.292) for {'C': 1342764.9611378654, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.697 (+/-0.304) for {'C': 1342764.9611378654, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.698 (+/-0.306) for {'C': 1342764.9611378654, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.697 (+/-0.304) for {'C': 1342764.9611378654, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.697 (+/-0.306) for {'C': 1342764.9611378654, 'kernel': 'rbf', 'gamma': 1.037528415818012e-06}
0.697 (+/-0.307) for {'C': 1342764.9611378654, 'kernel': 'rbf', 'gamma': 1.05681750921366e-06}
0.697 (+/-0.306) for {'C': 1342764.9611378654, 'kernel': 'rbf', 'gamma': 1.076465213629836e-06}
0.697 (+/-0.307) for {'C': 1342764.9611378654, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.696 (+/-0.302) for {'C': 1367728.8255958504, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.696 (+/-0.302) for {'C': 1367728.8255958504, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.696 (+/-0.303) for {'C': 1367728.8255958504, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.680 (+/-0.292) for {'C': 1367728.8255958504, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.697 (+/-0.304) for {'C': 1367728.8255958504, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.697 (+/-0.305) for {'C': 1367728.8255958504, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.697 (+/-0.305) for {'C': 1367728.8255958504, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.697 (+/-0.306) for {'C': 1367728.8255958504, 'kernel': 'rbf', 'gamma': 1.037528415818012e-06}
0.697 (+/-0.307) for {'C': 1367728.8255958504, 'kernel': 'rbf', 'gamma': 1.05681750921366e-06}
0.697 (+/-0.307) for {'C': 1367728.8255958504, 'kernel': 'rbf', 'gamma': 1.076465213629836e-06}
0.697 (+/-0.307) for {'C': 1367728.8255958504, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.697 (+/-0.303) for {'C': 1393156.8029453037, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.696 (+/-0.302) for {'C': 1393156.8029453037, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.696 (+/-0.303) for {'C': 1393156.8029453037, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.680 (+/-0.292) for {'C': 1393156.8029453037, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.697 (+/-0.304) for {'C': 1393156.8029453037, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.697 (+/-0.305) for {'C': 1393156.8029453037, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.697 (+/-0.305) for {'C': 1393156.8029453037, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.697 (+/-0.306) for {'C': 1393156.8029453037, 'kernel': 'rbf', 'gamma': 1.037528415818012e-06}
0.697 (+/-0.307) for {'C': 1393156.8029453037, 'kernel': 'rbf', 'gamma': 1.05681750921366e-06}
0.697 (+/-0.307) for {'C': 1393156.8029453037, 'kernel': 'rbf', 'gamma': 1.076465213629836e-06}
0.697 (+/-0.307) for {'C': 1393156.8029453037, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.697 (+/-0.302) for {'C': 1419057.5216890923, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.696 (+/-0.302) for {'C': 1419057.5216890923, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.696 (+/-0.303) for {'C': 1419057.5216890923, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.680 (+/-0.292) for {'C': 1419057.5216890923, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.697 (+/-0.304) for {'C': 1419057.5216890923, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.697 (+/-0.305) for {'C': 1419057.5216890923, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.697 (+/-0.304) for {'C': 1419057.5216890923, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.697 (+/-0.306) for {'C': 1419057.5216890923, 'kernel': 'rbf', 'gamma': 1.037528415818012e-06}
0.697 (+/-0.307) for {'C': 1419057.5216890923, 'kernel': 'rbf', 'gamma': 1.05681750921366e-06}
0.697 (+/-0.307) for {'C': 1419057.5216890923, 'kernel': 'rbf', 'gamma': 1.076465213629836e-06}
0.697 (+/-0.307) for {'C': 1419057.5216890923, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.697 (+/-0.302) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.696 (+/-0.302) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.696 (+/-0.303) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.680 (+/-0.292) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.697 (+/-0.304) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.697 (+/-0.305) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.697 (+/-0.305) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.697 (+/-0.306) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.037528415818012e-06}
0.697 (+/-0.307) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.05681750921366e-06}
0.697 (+/-0.307) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.076465213629836e-06}
0.697 (+/-0.307) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.683 (+/-0.296) for {'C': 1.0, 'kernel': 'linear'}
0.664 (+/-0.314) for {'C': 10.0, 'kernel': 'linear'}
0.660 (+/-0.316) for {'C': 100.0, 'kernel': 'linear'}
0.612 (+/-0.241) for {'C': 1000.0, 'kernel': 'linear'}
0.611 (+/-0.239) for {'C': 10000.0, 'kernel': 'linear'}
0.611 (+/-0.245) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.11      0.17      0.13         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6979146054909721
第1轮loop中，tunemodel函数得到的最优参数是： ([{'C': array([1224616.19926505, 1229136.17306017, 1233672.82976633,
       1238226.23095899, 1242796.43844091, 1247383.51424294,
       1251987.52062488, 1256608.52007633, 1261246.57531754,
       1265901.74930024, 1270574.10520854]), 'kernel': ['rbf'], 'gamma': array([9.81747943e-07, 9.85371507e-07, 9.89008445e-07, 9.92658807e-07,
       9.96322642e-07, 1.00000000e-06, 1.00369093e-06, 1.00739548e-06,
       1.01111371e-06, 1.01484566e-06, 1.01859139e-06])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}], {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}, 0.6996794871794872)
这是第1次迭代微调C和gamma。
第1次迭代，得到delta： [70873.22431346     0.        ]
执行tunemodel()函数前，使用的grid search parameter是：
 [{'C': array([1224616.19926505, 1229136.17306017, 1233672.82976633,
       1238226.23095899, 1242796.43844091, 1247383.51424294,
       1251987.52062488, 1256608.52007633, 1261246.57531754,
       1265901.74930024, 1270574.10520854]), 'kernel': ['rbf'], 'gamma': array([9.81747943e-07, 9.85371507e-07, 9.89008445e-07, 9.92658807e-07,
       9.96322642e-07, 1.00000000e-06, 1.00369093e-06, 1.00739548e-06,
       1.01111371e-06, 1.01484566e-06, 1.01859139e-06])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.723 (+/-0.417) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.748 (+/-0.449) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 9.853715068586198e-07}
0.748 (+/-0.449) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 9.890084450210653e-07}
0.748 (+/-0.449) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 9.926588068710316e-07}
0.748 (+/-0.449) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 9.963226419544168e-07}
0.798 (+/-0.437) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.748 (+/-0.449) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.003690930920098e-06}
0.798 (+/-0.437) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.0073954848112503e-06}
0.748 (+/-0.449) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.0111137119549073e-06}
0.798 (+/-0.437) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.0148456628180941e-06}
0.798 (+/-0.437) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.723 (+/-0.417) for {'C': 1229136.1730601697, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.748 (+/-0.449) for {'C': 1229136.1730601697, 'kernel': 'rbf', 'gamma': 9.853715068586198e-07}
0.748 (+/-0.449) for {'C': 1229136.1730601697, 'kernel': 'rbf', 'gamma': 9.890084450210653e-07}
0.748 (+/-0.449) for {'C': 1229136.1730601697, 'kernel': 'rbf', 'gamma': 9.926588068710316e-07}
0.748 (+/-0.449) for {'C': 1229136.1730601697, 'kernel': 'rbf', 'gamma': 9.963226419544168e-07}
0.798 (+/-0.437) for {'C': 1229136.1730601697, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.748 (+/-0.449) for {'C': 1229136.1730601697, 'kernel': 'rbf', 'gamma': 1.003690930920098e-06}
0.798 (+/-0.437) for {'C': 1229136.1730601697, 'kernel': 'rbf', 'gamma': 1.0073954848112503e-06}
0.748 (+/-0.449) for {'C': 1229136.1730601697, 'kernel': 'rbf', 'gamma': 1.0111137119549073e-06}
0.798 (+/-0.437) for {'C': 1229136.1730601697, 'kernel': 'rbf', 'gamma': 1.0148456628180941e-06}
0.798 (+/-0.437) for {'C': 1229136.1730601697, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.723 (+/-0.417) for {'C': 1233672.8297663252, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.748 (+/-0.449) for {'C': 1233672.8297663252, 'kernel': 'rbf', 'gamma': 9.853715068586198e-07}
0.748 (+/-0.449) for {'C': 1233672.8297663252, 'kernel': 'rbf', 'gamma': 9.890084450210653e-07}
0.748 (+/-0.449) for {'C': 1233672.8297663252, 'kernel': 'rbf', 'gamma': 9.926588068710316e-07}
0.798 (+/-0.437) for {'C': 1233672.8297663252, 'kernel': 'rbf', 'gamma': 9.963226419544168e-07}
0.798 (+/-0.437) for {'C': 1233672.8297663252, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.798 (+/-0.437) for {'C': 1233672.8297663252, 'kernel': 'rbf', 'gamma': 1.003690930920098e-06}
0.798 (+/-0.437) for {'C': 1233672.8297663252, 'kernel': 'rbf', 'gamma': 1.0073954848112503e-06}
0.748 (+/-0.449) for {'C': 1233672.8297663252, 'kernel': 'rbf', 'gamma': 1.0111137119549073e-06}
0.798 (+/-0.437) for {'C': 1233672.8297663252, 'kernel': 'rbf', 'gamma': 1.0148456628180941e-06}
0.798 (+/-0.437) for {'C': 1233672.8297663252, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.723 (+/-0.417) for {'C': 1238226.2309589945, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.748 (+/-0.449) for {'C': 1238226.2309589945, 'kernel': 'rbf', 'gamma': 9.853715068586198e-07}
0.748 (+/-0.449) for {'C': 1238226.2309589945, 'kernel': 'rbf', 'gamma': 9.890084450210653e-07}
0.748 (+/-0.449) for {'C': 1238226.2309589945, 'kernel': 'rbf', 'gamma': 9.926588068710316e-07}
0.748 (+/-0.449) for {'C': 1238226.2309589945, 'kernel': 'rbf', 'gamma': 9.963226419544168e-07}
0.798 (+/-0.437) for {'C': 1238226.2309589945, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.798 (+/-0.437) for {'C': 1238226.2309589945, 'kernel': 'rbf', 'gamma': 1.003690930920098e-06}
0.798 (+/-0.437) for {'C': 1238226.2309589945, 'kernel': 'rbf', 'gamma': 1.0073954848112503e-06}
0.748 (+/-0.449) for {'C': 1238226.2309589945, 'kernel': 'rbf', 'gamma': 1.0111137119549073e-06}
0.798 (+/-0.437) for {'C': 1238226.2309589945, 'kernel': 'rbf', 'gamma': 1.0148456628180941e-06}
0.798 (+/-0.437) for {'C': 1238226.2309589945, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.723 (+/-0.417) for {'C': 1242796.4384409143, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.748 (+/-0.449) for {'C': 1242796.4384409143, 'kernel': 'rbf', 'gamma': 9.853715068586198e-07}
0.748 (+/-0.449) for {'C': 1242796.4384409143, 'kernel': 'rbf', 'gamma': 9.890084450210653e-07}
0.748 (+/-0.449) for {'C': 1242796.4384409143, 'kernel': 'rbf', 'gamma': 9.926588068710316e-07}
0.748 (+/-0.449) for {'C': 1242796.4384409143, 'kernel': 'rbf', 'gamma': 9.963226419544168e-07}
0.798 (+/-0.437) for {'C': 1242796.4384409143, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.798 (+/-0.437) for {'C': 1242796.4384409143, 'kernel': 'rbf', 'gamma': 1.003690930920098e-06}
0.798 (+/-0.437) for {'C': 1242796.4384409143, 'kernel': 'rbf', 'gamma': 1.0073954848112503e-06}
0.748 (+/-0.449) for {'C': 1242796.4384409143, 'kernel': 'rbf', 'gamma': 1.0111137119549073e-06}
0.798 (+/-0.437) for {'C': 1242796.4384409143, 'kernel': 'rbf', 'gamma': 1.0148456628180941e-06}
0.798 (+/-0.437) for {'C': 1242796.4384409143, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.748 (+/-0.449) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.748 (+/-0.449) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 9.853715068586198e-07}
0.748 (+/-0.449) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 9.890084450210653e-07}
0.748 (+/-0.449) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 9.926588068710316e-07}
0.748 (+/-0.449) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 9.963226419544168e-07}
0.798 (+/-0.437) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.798 (+/-0.437) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.003690930920098e-06}
0.798 (+/-0.437) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.0073954848112503e-06}
0.748 (+/-0.449) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.0111137119549073e-06}
0.798 (+/-0.437) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.0148456628180941e-06}
0.798 (+/-0.437) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.723 (+/-0.417) for {'C': 1251987.520624883, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.748 (+/-0.449) for {'C': 1251987.520624883, 'kernel': 'rbf', 'gamma': 9.853715068586198e-07}
0.748 (+/-0.449) for {'C': 1251987.520624883, 'kernel': 'rbf', 'gamma': 9.890084450210653e-07}
0.748 (+/-0.449) for {'C': 1251987.520624883, 'kernel': 'rbf', 'gamma': 9.926588068710316e-07}
0.798 (+/-0.437) for {'C': 1251987.520624883, 'kernel': 'rbf', 'gamma': 9.963226419544168e-07}
0.798 (+/-0.437) for {'C': 1251987.520624883, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.798 (+/-0.437) for {'C': 1251987.520624883, 'kernel': 'rbf', 'gamma': 1.003690930920098e-06}
0.798 (+/-0.437) for {'C': 1251987.520624883, 'kernel': 'rbf', 'gamma': 1.0073954848112503e-06}
0.748 (+/-0.449) for {'C': 1251987.520624883, 'kernel': 'rbf', 'gamma': 1.0111137119549073e-06}
0.798 (+/-0.437) for {'C': 1251987.520624883, 'kernel': 'rbf', 'gamma': 1.0148456628180941e-06}
0.798 (+/-0.437) for {'C': 1251987.520624883, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.723 (+/-0.417) for {'C': 1256608.520076331, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.748 (+/-0.449) for {'C': 1256608.520076331, 'kernel': 'rbf', 'gamma': 9.853715068586198e-07}
0.748 (+/-0.449) for {'C': 1256608.520076331, 'kernel': 'rbf', 'gamma': 9.890084450210653e-07}
0.748 (+/-0.449) for {'C': 1256608.520076331, 'kernel': 'rbf', 'gamma': 9.926588068710316e-07}
0.748 (+/-0.449) for {'C': 1256608.520076331, 'kernel': 'rbf', 'gamma': 9.963226419544168e-07}
0.798 (+/-0.437) for {'C': 1256608.520076331, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.798 (+/-0.437) for {'C': 1256608.520076331, 'kernel': 'rbf', 'gamma': 1.003690930920098e-06}
0.798 (+/-0.437) for {'C': 1256608.520076331, 'kernel': 'rbf', 'gamma': 1.0073954848112503e-06}
0.748 (+/-0.449) for {'C': 1256608.520076331, 'kernel': 'rbf', 'gamma': 1.0111137119549073e-06}
0.798 (+/-0.437) for {'C': 1256608.520076331, 'kernel': 'rbf', 'gamma': 1.0148456628180941e-06}
0.798 (+/-0.437) for {'C': 1256608.520076331, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.723 (+/-0.417) for {'C': 1261246.5753175393, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.748 (+/-0.449) for {'C': 1261246.5753175393, 'kernel': 'rbf', 'gamma': 9.853715068586198e-07}
0.748 (+/-0.449) for {'C': 1261246.5753175393, 'kernel': 'rbf', 'gamma': 9.890084450210653e-07}
0.748 (+/-0.449) for {'C': 1261246.5753175393, 'kernel': 'rbf', 'gamma': 9.926588068710316e-07}
0.748 (+/-0.449) for {'C': 1261246.5753175393, 'kernel': 'rbf', 'gamma': 9.963226419544168e-07}
0.798 (+/-0.437) for {'C': 1261246.5753175393, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.798 (+/-0.437) for {'C': 1261246.5753175393, 'kernel': 'rbf', 'gamma': 1.003690930920098e-06}
0.798 (+/-0.437) for {'C': 1261246.5753175393, 'kernel': 'rbf', 'gamma': 1.0073954848112503e-06}
0.748 (+/-0.449) for {'C': 1261246.5753175393, 'kernel': 'rbf', 'gamma': 1.0111137119549073e-06}
0.798 (+/-0.437) for {'C': 1261246.5753175393, 'kernel': 'rbf', 'gamma': 1.0148456628180941e-06}
0.798 (+/-0.437) for {'C': 1261246.5753175393, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.723 (+/-0.417) for {'C': 1265901.7493002433, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.748 (+/-0.449) for {'C': 1265901.7493002433, 'kernel': 'rbf', 'gamma': 9.853715068586198e-07}
0.748 (+/-0.449) for {'C': 1265901.7493002433, 'kernel': 'rbf', 'gamma': 9.890084450210653e-07}
0.748 (+/-0.449) for {'C': 1265901.7493002433, 'kernel': 'rbf', 'gamma': 9.926588068710316e-07}
0.748 (+/-0.449) for {'C': 1265901.7493002433, 'kernel': 'rbf', 'gamma': 9.963226419544168e-07}
0.798 (+/-0.437) for {'C': 1265901.7493002433, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.798 (+/-0.437) for {'C': 1265901.7493002433, 'kernel': 'rbf', 'gamma': 1.003690930920098e-06}
0.798 (+/-0.437) for {'C': 1265901.7493002433, 'kernel': 'rbf', 'gamma': 1.0073954848112503e-06}
0.748 (+/-0.449) for {'C': 1265901.7493002433, 'kernel': 'rbf', 'gamma': 1.0111137119549073e-06}
0.798 (+/-0.437) for {'C': 1265901.7493002433, 'kernel': 'rbf', 'gamma': 1.0148456628180941e-06}
0.798 (+/-0.437) for {'C': 1265901.7493002433, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.723 (+/-0.417) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.748 (+/-0.449) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 9.853715068586198e-07}
0.748 (+/-0.449) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 9.890084450210653e-07}
0.748 (+/-0.449) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 9.926588068710316e-07}
0.748 (+/-0.449) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 9.963226419544168e-07}
0.798 (+/-0.437) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.798 (+/-0.437) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.003690930920098e-06}
0.798 (+/-0.437) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.0073954848112503e-06}
0.748 (+/-0.449) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.0111137119549073e-06}
0.798 (+/-0.437) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.0148456628180941e-06}
0.798 (+/-0.437) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.673 (+/-0.330) for {'C': 10.0, 'kernel': 'linear'}
0.646 (+/-0.317) for {'C': 100.0, 'kernel': 'linear'}
0.620 (+/-0.321) for {'C': 1000.0, 'kernel': 'linear'}
0.618 (+/-0.322) for {'C': 10000.0, 'kernel': 'linear'}
0.624 (+/-0.319) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.666 (+/-0.316) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.666 (+/-0.316) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 9.853715068586198e-07}
0.666 (+/-0.316) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 9.890084450210653e-07}
0.666 (+/-0.316) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 9.926588068710316e-07}
0.666 (+/-0.316) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 9.963226419544168e-07}
0.683 (+/-0.296) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.666 (+/-0.316) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.003690930920098e-06}
0.683 (+/-0.296) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.0073954848112503e-06}
0.666 (+/-0.316) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.0111137119549073e-06}
0.683 (+/-0.296) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.0148456628180941e-06}
0.683 (+/-0.296) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.666 (+/-0.316) for {'C': 1229136.1730601697, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.666 (+/-0.316) for {'C': 1229136.1730601697, 'kernel': 'rbf', 'gamma': 9.853715068586198e-07}
0.666 (+/-0.316) for {'C': 1229136.1730601697, 'kernel': 'rbf', 'gamma': 9.890084450210653e-07}
0.666 (+/-0.316) for {'C': 1229136.1730601697, 'kernel': 'rbf', 'gamma': 9.926588068710316e-07}
0.666 (+/-0.316) for {'C': 1229136.1730601697, 'kernel': 'rbf', 'gamma': 9.963226419544168e-07}
0.683 (+/-0.296) for {'C': 1229136.1730601697, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.666 (+/-0.316) for {'C': 1229136.1730601697, 'kernel': 'rbf', 'gamma': 1.003690930920098e-06}
0.683 (+/-0.296) for {'C': 1229136.1730601697, 'kernel': 'rbf', 'gamma': 1.0073954848112503e-06}
0.666 (+/-0.316) for {'C': 1229136.1730601697, 'kernel': 'rbf', 'gamma': 1.0111137119549073e-06}
0.683 (+/-0.296) for {'C': 1229136.1730601697, 'kernel': 'rbf', 'gamma': 1.0148456628180941e-06}
0.683 (+/-0.296) for {'C': 1229136.1730601697, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.666 (+/-0.316) for {'C': 1233672.8297663252, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.666 (+/-0.316) for {'C': 1233672.8297663252, 'kernel': 'rbf', 'gamma': 9.853715068586198e-07}
0.666 (+/-0.316) for {'C': 1233672.8297663252, 'kernel': 'rbf', 'gamma': 9.890084450210653e-07}
0.666 (+/-0.316) for {'C': 1233672.8297663252, 'kernel': 'rbf', 'gamma': 9.926588068710316e-07}
0.683 (+/-0.296) for {'C': 1233672.8297663252, 'kernel': 'rbf', 'gamma': 9.963226419544168e-07}
0.683 (+/-0.296) for {'C': 1233672.8297663252, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.683 (+/-0.296) for {'C': 1233672.8297663252, 'kernel': 'rbf', 'gamma': 1.003690930920098e-06}
0.683 (+/-0.296) for {'C': 1233672.8297663252, 'kernel': 'rbf', 'gamma': 1.0073954848112503e-06}
0.666 (+/-0.316) for {'C': 1233672.8297663252, 'kernel': 'rbf', 'gamma': 1.0111137119549073e-06}
0.683 (+/-0.296) for {'C': 1233672.8297663252, 'kernel': 'rbf', 'gamma': 1.0148456628180941e-06}
0.683 (+/-0.296) for {'C': 1233672.8297663252, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.666 (+/-0.316) for {'C': 1238226.2309589945, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.666 (+/-0.316) for {'C': 1238226.2309589945, 'kernel': 'rbf', 'gamma': 9.853715068586198e-07}
0.666 (+/-0.316) for {'C': 1238226.2309589945, 'kernel': 'rbf', 'gamma': 9.890084450210653e-07}
0.666 (+/-0.316) for {'C': 1238226.2309589945, 'kernel': 'rbf', 'gamma': 9.926588068710316e-07}
0.666 (+/-0.316) for {'C': 1238226.2309589945, 'kernel': 'rbf', 'gamma': 9.963226419544168e-07}
0.683 (+/-0.296) for {'C': 1238226.2309589945, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.683 (+/-0.296) for {'C': 1238226.2309589945, 'kernel': 'rbf', 'gamma': 1.003690930920098e-06}
0.683 (+/-0.296) for {'C': 1238226.2309589945, 'kernel': 'rbf', 'gamma': 1.0073954848112503e-06}
0.666 (+/-0.316) for {'C': 1238226.2309589945, 'kernel': 'rbf', 'gamma': 1.0111137119549073e-06}
0.683 (+/-0.296) for {'C': 1238226.2309589945, 'kernel': 'rbf', 'gamma': 1.0148456628180941e-06}
0.683 (+/-0.296) for {'C': 1238226.2309589945, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.666 (+/-0.316) for {'C': 1242796.4384409143, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.666 (+/-0.316) for {'C': 1242796.4384409143, 'kernel': 'rbf', 'gamma': 9.853715068586198e-07}
0.666 (+/-0.316) for {'C': 1242796.4384409143, 'kernel': 'rbf', 'gamma': 9.890084450210653e-07}
0.666 (+/-0.316) for {'C': 1242796.4384409143, 'kernel': 'rbf', 'gamma': 9.926588068710316e-07}
0.666 (+/-0.316) for {'C': 1242796.4384409143, 'kernel': 'rbf', 'gamma': 9.963226419544168e-07}
0.700 (+/-0.309) for {'C': 1242796.4384409143, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.683 (+/-0.296) for {'C': 1242796.4384409143, 'kernel': 'rbf', 'gamma': 1.003690930920098e-06}
0.683 (+/-0.296) for {'C': 1242796.4384409143, 'kernel': 'rbf', 'gamma': 1.0073954848112503e-06}
0.666 (+/-0.316) for {'C': 1242796.4384409143, 'kernel': 'rbf', 'gamma': 1.0111137119549073e-06}
0.683 (+/-0.296) for {'C': 1242796.4384409143, 'kernel': 'rbf', 'gamma': 1.0148456628180941e-06}
0.683 (+/-0.296) for {'C': 1242796.4384409143, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.666 (+/-0.316) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.666 (+/-0.316) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 9.853715068586198e-07}
0.666 (+/-0.316) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 9.890084450210653e-07}
0.666 (+/-0.316) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 9.926588068710316e-07}
0.666 (+/-0.316) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 9.963226419544168e-07}
0.700 (+/-0.309) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.683 (+/-0.296) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.003690930920098e-06}
0.683 (+/-0.296) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.0073954848112503e-06}
0.666 (+/-0.316) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.0111137119549073e-06}
0.683 (+/-0.296) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.0148456628180941e-06}
0.683 (+/-0.296) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.666 (+/-0.316) for {'C': 1251987.520624883, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.666 (+/-0.316) for {'C': 1251987.520624883, 'kernel': 'rbf', 'gamma': 9.853715068586198e-07}
0.666 (+/-0.316) for {'C': 1251987.520624883, 'kernel': 'rbf', 'gamma': 9.890084450210653e-07}
0.666 (+/-0.316) for {'C': 1251987.520624883, 'kernel': 'rbf', 'gamma': 9.926588068710316e-07}
0.683 (+/-0.296) for {'C': 1251987.520624883, 'kernel': 'rbf', 'gamma': 9.963226419544168e-07}
0.700 (+/-0.309) for {'C': 1251987.520624883, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.683 (+/-0.296) for {'C': 1251987.520624883, 'kernel': 'rbf', 'gamma': 1.003690930920098e-06}
0.683 (+/-0.296) for {'C': 1251987.520624883, 'kernel': 'rbf', 'gamma': 1.0073954848112503e-06}
0.666 (+/-0.316) for {'C': 1251987.520624883, 'kernel': 'rbf', 'gamma': 1.0111137119549073e-06}
0.683 (+/-0.296) for {'C': 1251987.520624883, 'kernel': 'rbf', 'gamma': 1.0148456628180941e-06}
0.683 (+/-0.296) for {'C': 1251987.520624883, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.666 (+/-0.316) for {'C': 1256608.520076331, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.666 (+/-0.316) for {'C': 1256608.520076331, 'kernel': 'rbf', 'gamma': 9.853715068586198e-07}
0.666 (+/-0.316) for {'C': 1256608.520076331, 'kernel': 'rbf', 'gamma': 9.890084450210653e-07}
0.666 (+/-0.316) for {'C': 1256608.520076331, 'kernel': 'rbf', 'gamma': 9.926588068710316e-07}
0.666 (+/-0.316) for {'C': 1256608.520076331, 'kernel': 'rbf', 'gamma': 9.963226419544168e-07}
0.700 (+/-0.309) for {'C': 1256608.520076331, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.683 (+/-0.296) for {'C': 1256608.520076331, 'kernel': 'rbf', 'gamma': 1.003690930920098e-06}
0.683 (+/-0.296) for {'C': 1256608.520076331, 'kernel': 'rbf', 'gamma': 1.0073954848112503e-06}
0.666 (+/-0.316) for {'C': 1256608.520076331, 'kernel': 'rbf', 'gamma': 1.0111137119549073e-06}
0.683 (+/-0.296) for {'C': 1256608.520076331, 'kernel': 'rbf', 'gamma': 1.0148456628180941e-06}
0.683 (+/-0.296) for {'C': 1256608.520076331, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.666 (+/-0.316) for {'C': 1261246.5753175393, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.666 (+/-0.316) for {'C': 1261246.5753175393, 'kernel': 'rbf', 'gamma': 9.853715068586198e-07}
0.666 (+/-0.316) for {'C': 1261246.5753175393, 'kernel': 'rbf', 'gamma': 9.890084450210653e-07}
0.666 (+/-0.316) for {'C': 1261246.5753175393, 'kernel': 'rbf', 'gamma': 9.926588068710316e-07}
0.666 (+/-0.316) for {'C': 1261246.5753175393, 'kernel': 'rbf', 'gamma': 9.963226419544168e-07}
0.700 (+/-0.309) for {'C': 1261246.5753175393, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.683 (+/-0.296) for {'C': 1261246.5753175393, 'kernel': 'rbf', 'gamma': 1.003690930920098e-06}
0.683 (+/-0.296) for {'C': 1261246.5753175393, 'kernel': 'rbf', 'gamma': 1.0073954848112503e-06}
0.666 (+/-0.316) for {'C': 1261246.5753175393, 'kernel': 'rbf', 'gamma': 1.0111137119549073e-06}
0.683 (+/-0.296) for {'C': 1261246.5753175393, 'kernel': 'rbf', 'gamma': 1.0148456628180941e-06}
0.683 (+/-0.296) for {'C': 1261246.5753175393, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.666 (+/-0.316) for {'C': 1265901.7493002433, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.666 (+/-0.316) for {'C': 1265901.7493002433, 'kernel': 'rbf', 'gamma': 9.853715068586198e-07}
0.666 (+/-0.316) for {'C': 1265901.7493002433, 'kernel': 'rbf', 'gamma': 9.890084450210653e-07}
0.666 (+/-0.316) for {'C': 1265901.7493002433, 'kernel': 'rbf', 'gamma': 9.926588068710316e-07}
0.666 (+/-0.316) for {'C': 1265901.7493002433, 'kernel': 'rbf', 'gamma': 9.963226419544168e-07}
0.700 (+/-0.309) for {'C': 1265901.7493002433, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.683 (+/-0.296) for {'C': 1265901.7493002433, 'kernel': 'rbf', 'gamma': 1.003690930920098e-06}
0.683 (+/-0.296) for {'C': 1265901.7493002433, 'kernel': 'rbf', 'gamma': 1.0073954848112503e-06}
0.666 (+/-0.316) for {'C': 1265901.7493002433, 'kernel': 'rbf', 'gamma': 1.0111137119549073e-06}
0.683 (+/-0.296) for {'C': 1265901.7493002433, 'kernel': 'rbf', 'gamma': 1.0148456628180941e-06}
0.683 (+/-0.296) for {'C': 1265901.7493002433, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.666 (+/-0.316) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.666 (+/-0.316) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 9.853715068586198e-07}
0.666 (+/-0.316) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 9.890084450210653e-07}
0.666 (+/-0.316) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 9.926588068710316e-07}
0.666 (+/-0.316) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 9.963226419544168e-07}
0.700 (+/-0.309) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.683 (+/-0.296) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.003690930920098e-06}
0.683 (+/-0.296) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.0073954848112503e-06}
0.666 (+/-0.316) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.0111137119549073e-06}
0.683 (+/-0.296) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.0148456628180941e-06}
0.683 (+/-0.296) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.665 (+/-0.315) for {'C': 10.0, 'kernel': 'linear'}
0.664 (+/-0.316) for {'C': 100.0, 'kernel': 'linear'}
0.612 (+/-0.241) for {'C': 1000.0, 'kernel': 'linear'}
0.611 (+/-0.239) for {'C': 10000.0, 'kernel': 'linear'}
0.611 (+/-0.245) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 1242796.4384409143, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6996794871794872
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.662 (+/-0.356) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.659 (+/-0.359) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 9.853715068586198e-07}
0.665 (+/-0.356) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 9.890084450210653e-07}
0.676 (+/-0.357) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 9.926588068710316e-07}
0.676 (+/-0.357) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 9.963226419544168e-07}
0.681 (+/-0.355) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.685 (+/-0.360) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.003690930920098e-06}
0.650 (+/-0.294) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.0073954848112503e-06}
0.664 (+/-0.355) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.0111137119549073e-06}
0.663 (+/-0.358) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.0148456628180941e-06}
0.664 (+/-0.356) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.662 (+/-0.356) for {'C': 1229136.1730601697, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.663 (+/-0.357) for {'C': 1229136.1730601697, 'kernel': 'rbf', 'gamma': 9.853715068586198e-07}
0.665 (+/-0.356) for {'C': 1229136.1730601697, 'kernel': 'rbf', 'gamma': 9.890084450210653e-07}
0.676 (+/-0.357) for {'C': 1229136.1730601697, 'kernel': 'rbf', 'gamma': 9.926588068710316e-07}
0.676 (+/-0.357) for {'C': 1229136.1730601697, 'kernel': 'rbf', 'gamma': 9.963226419544168e-07}
0.681 (+/-0.355) for {'C': 1229136.1730601697, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.685 (+/-0.360) for {'C': 1229136.1730601697, 'kernel': 'rbf', 'gamma': 1.003690930920098e-06}
0.650 (+/-0.294) for {'C': 1229136.1730601697, 'kernel': 'rbf', 'gamma': 1.0073954848112503e-06}
0.664 (+/-0.355) for {'C': 1229136.1730601697, 'kernel': 'rbf', 'gamma': 1.0111137119549073e-06}
0.663 (+/-0.358) for {'C': 1229136.1730601697, 'kernel': 'rbf', 'gamma': 1.0148456628180941e-06}
0.662 (+/-0.357) for {'C': 1229136.1730601697, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.662 (+/-0.356) for {'C': 1233672.8297663252, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.663 (+/-0.357) for {'C': 1233672.8297663252, 'kernel': 'rbf', 'gamma': 9.853715068586198e-07}
0.665 (+/-0.356) for {'C': 1233672.8297663252, 'kernel': 'rbf', 'gamma': 9.890084450210653e-07}
0.676 (+/-0.357) for {'C': 1233672.8297663252, 'kernel': 'rbf', 'gamma': 9.926588068710316e-07}
0.676 (+/-0.357) for {'C': 1233672.8297663252, 'kernel': 'rbf', 'gamma': 9.963226419544168e-07}
0.681 (+/-0.355) for {'C': 1233672.8297663252, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.685 (+/-0.360) for {'C': 1233672.8297663252, 'kernel': 'rbf', 'gamma': 1.003690930920098e-06}
0.677 (+/-0.358) for {'C': 1233672.8297663252, 'kernel': 'rbf', 'gamma': 1.0073954848112503e-06}
0.664 (+/-0.355) for {'C': 1233672.8297663252, 'kernel': 'rbf', 'gamma': 1.0111137119549073e-06}
0.665 (+/-0.357) for {'C': 1233672.8297663252, 'kernel': 'rbf', 'gamma': 1.0148456628180941e-06}
0.662 (+/-0.357) for {'C': 1233672.8297663252, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.662 (+/-0.356) for {'C': 1238226.2309589945, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.659 (+/-0.359) for {'C': 1238226.2309589945, 'kernel': 'rbf', 'gamma': 9.853715068586198e-07}
0.663 (+/-0.357) for {'C': 1238226.2309589945, 'kernel': 'rbf', 'gamma': 9.890084450210653e-07}
0.676 (+/-0.357) for {'C': 1238226.2309589945, 'kernel': 'rbf', 'gamma': 9.926588068710316e-07}
0.676 (+/-0.357) for {'C': 1238226.2309589945, 'kernel': 'rbf', 'gamma': 9.963226419544168e-07}
0.681 (+/-0.355) for {'C': 1238226.2309589945, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.685 (+/-0.360) for {'C': 1238226.2309589945, 'kernel': 'rbf', 'gamma': 1.003690930920098e-06}
0.675 (+/-0.358) for {'C': 1238226.2309589945, 'kernel': 'rbf', 'gamma': 1.0073954848112503e-06}
0.639 (+/-0.286) for {'C': 1238226.2309589945, 'kernel': 'rbf', 'gamma': 1.0111137119549073e-06}
0.665 (+/-0.357) for {'C': 1238226.2309589945, 'kernel': 'rbf', 'gamma': 1.0148456628180941e-06}
0.662 (+/-0.357) for {'C': 1238226.2309589945, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.664 (+/-0.355) for {'C': 1242796.4384409143, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.659 (+/-0.359) for {'C': 1242796.4384409143, 'kernel': 'rbf', 'gamma': 9.853715068586198e-07}
0.663 (+/-0.357) for {'C': 1242796.4384409143, 'kernel': 'rbf', 'gamma': 9.890084450210653e-07}
0.674 (+/-0.360) for {'C': 1242796.4384409143, 'kernel': 'rbf', 'gamma': 9.926588068710316e-07}
0.676 (+/-0.357) for {'C': 1242796.4384409143, 'kernel': 'rbf', 'gamma': 9.963226419544168e-07}
0.681 (+/-0.355) for {'C': 1242796.4384409143, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.685 (+/-0.360) for {'C': 1242796.4384409143, 'kernel': 'rbf', 'gamma': 1.003690930920098e-06}
0.675 (+/-0.358) for {'C': 1242796.4384409143, 'kernel': 'rbf', 'gamma': 1.0073954848112503e-06}
0.639 (+/-0.286) for {'C': 1242796.4384409143, 'kernel': 'rbf', 'gamma': 1.0111137119549073e-06}
0.663 (+/-0.358) for {'C': 1242796.4384409143, 'kernel': 'rbf', 'gamma': 1.0148456628180941e-06}
0.662 (+/-0.357) for {'C': 1242796.4384409143, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.664 (+/-0.355) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.659 (+/-0.359) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 9.853715068586198e-07}
0.663 (+/-0.357) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 9.890084450210653e-07}
0.674 (+/-0.360) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 9.926588068710316e-07}
0.676 (+/-0.357) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 9.963226419544168e-07}
0.689 (+/-0.358) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.685 (+/-0.360) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.003690930920098e-06}
0.650 (+/-0.294) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.0073954848112503e-06}
0.639 (+/-0.286) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.0111137119549073e-06}
0.638 (+/-0.290) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.0148456628180941e-06}
0.660 (+/-0.357) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.664 (+/-0.355) for {'C': 1251987.520624883, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.661 (+/-0.358) for {'C': 1251987.520624883, 'kernel': 'rbf', 'gamma': 9.853715068586198e-07}
0.663 (+/-0.357) for {'C': 1251987.520624883, 'kernel': 'rbf', 'gamma': 9.890084450210653e-07}
0.674 (+/-0.360) for {'C': 1251987.520624883, 'kernel': 'rbf', 'gamma': 9.926588068710316e-07}
0.676 (+/-0.357) for {'C': 1251987.520624883, 'kernel': 'rbf', 'gamma': 9.963226419544168e-07}
0.681 (+/-0.355) for {'C': 1251987.520624883, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.685 (+/-0.360) for {'C': 1251987.520624883, 'kernel': 'rbf', 'gamma': 1.003690930920098e-06}
0.647 (+/-0.294) for {'C': 1251987.520624883, 'kernel': 'rbf', 'gamma': 1.0073954848112503e-06}
0.639 (+/-0.286) for {'C': 1251987.520624883, 'kernel': 'rbf', 'gamma': 1.0111137119549073e-06}
0.663 (+/-0.358) for {'C': 1251987.520624883, 'kernel': 'rbf', 'gamma': 1.0148456628180941e-06}
0.660 (+/-0.357) for {'C': 1251987.520624883, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.664 (+/-0.355) for {'C': 1256608.520076331, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.659 (+/-0.359) for {'C': 1256608.520076331, 'kernel': 'rbf', 'gamma': 9.853715068586198e-07}
0.663 (+/-0.357) for {'C': 1256608.520076331, 'kernel': 'rbf', 'gamma': 9.890084450210653e-07}
0.674 (+/-0.360) for {'C': 1256608.520076331, 'kernel': 'rbf', 'gamma': 9.926588068710316e-07}
0.676 (+/-0.357) for {'C': 1256608.520076331, 'kernel': 'rbf', 'gamma': 9.963226419544168e-07}
0.681 (+/-0.355) for {'C': 1256608.520076331, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.685 (+/-0.360) for {'C': 1256608.520076331, 'kernel': 'rbf', 'gamma': 1.003690930920098e-06}
0.652 (+/-0.294) for {'C': 1256608.520076331, 'kernel': 'rbf', 'gamma': 1.0073954848112503e-06}
0.639 (+/-0.286) for {'C': 1256608.520076331, 'kernel': 'rbf', 'gamma': 1.0111137119549073e-06}
0.663 (+/-0.358) for {'C': 1256608.520076331, 'kernel': 'rbf', 'gamma': 1.0148456628180941e-06}
0.660 (+/-0.357) for {'C': 1256608.520076331, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.664 (+/-0.355) for {'C': 1261246.5753175393, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.659 (+/-0.359) for {'C': 1261246.5753175393, 'kernel': 'rbf', 'gamma': 9.853715068586198e-07}
0.663 (+/-0.357) for {'C': 1261246.5753175393, 'kernel': 'rbf', 'gamma': 9.890084450210653e-07}
0.676 (+/-0.357) for {'C': 1261246.5753175393, 'kernel': 'rbf', 'gamma': 9.926588068710316e-07}
0.676 (+/-0.357) for {'C': 1261246.5753175393, 'kernel': 'rbf', 'gamma': 9.963226419544168e-07}
0.681 (+/-0.355) for {'C': 1261246.5753175393, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.685 (+/-0.360) for {'C': 1261246.5753175393, 'kernel': 'rbf', 'gamma': 1.003690930920098e-06}
0.650 (+/-0.294) for {'C': 1261246.5753175393, 'kernel': 'rbf', 'gamma': 1.0073954848112503e-06}
0.639 (+/-0.286) for {'C': 1261246.5753175393, 'kernel': 'rbf', 'gamma': 1.0111137119549073e-06}
0.663 (+/-0.358) for {'C': 1261246.5753175393, 'kernel': 'rbf', 'gamma': 1.0148456628180941e-06}
0.660 (+/-0.357) for {'C': 1261246.5753175393, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.664 (+/-0.355) for {'C': 1265901.7493002433, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.659 (+/-0.359) for {'C': 1265901.7493002433, 'kernel': 'rbf', 'gamma': 9.853715068586198e-07}
0.663 (+/-0.357) for {'C': 1265901.7493002433, 'kernel': 'rbf', 'gamma': 9.890084450210653e-07}
0.674 (+/-0.360) for {'C': 1265901.7493002433, 'kernel': 'rbf', 'gamma': 9.926588068710316e-07}
0.676 (+/-0.357) for {'C': 1265901.7493002433, 'kernel': 'rbf', 'gamma': 9.963226419544168e-07}
0.681 (+/-0.355) for {'C': 1265901.7493002433, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.677 (+/-0.358) for {'C': 1265901.7493002433, 'kernel': 'rbf', 'gamma': 1.003690930920098e-06}
0.650 (+/-0.294) for {'C': 1265901.7493002433, 'kernel': 'rbf', 'gamma': 1.0073954848112503e-06}
0.639 (+/-0.286) for {'C': 1265901.7493002433, 'kernel': 'rbf', 'gamma': 1.0111137119549073e-06}
0.663 (+/-0.358) for {'C': 1265901.7493002433, 'kernel': 'rbf', 'gamma': 1.0148456628180941e-06}
0.660 (+/-0.357) for {'C': 1265901.7493002433, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.664 (+/-0.355) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.659 (+/-0.359) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 9.853715068586198e-07}
0.663 (+/-0.357) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 9.890084450210653e-07}
0.674 (+/-0.360) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 9.926588068710316e-07}
0.676 (+/-0.357) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 9.963226419544168e-07}
0.681 (+/-0.355) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.677 (+/-0.358) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.003690930920098e-06}
0.650 (+/-0.294) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.0073954848112503e-06}
0.639 (+/-0.286) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.0111137119549073e-06}
0.663 (+/-0.358) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.0148456628180941e-06}
0.660 (+/-0.357) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.764 (+/-0.421) for {'C': 1.0, 'kernel': 'linear'}
0.643 (+/-0.306) for {'C': 10.0, 'kernel': 'linear'}
0.630 (+/-0.312) for {'C': 100.0, 'kernel': 'linear'}
0.620 (+/-0.321) for {'C': 1000.0, 'kernel': 'linear'}
0.618 (+/-0.322) for {'C': 10000.0, 'kernel': 'linear'}
0.624 (+/-0.319) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.20      0.17      0.18         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.99      0.99       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.697 (+/-0.304) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.696 (+/-0.304) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 9.853715068586198e-07}
0.697 (+/-0.304) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 9.890084450210653e-07}
0.697 (+/-0.306) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 9.926588068710316e-07}
0.697 (+/-0.306) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 9.963226419544168e-07}
0.698 (+/-0.306) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.698 (+/-0.306) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.003690930920098e-06}
0.697 (+/-0.304) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.0073954848112503e-06}
0.697 (+/-0.304) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.0111137119549073e-06}
0.697 (+/-0.305) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.0148456628180941e-06}
0.697 (+/-0.304) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.697 (+/-0.304) for {'C': 1229136.1730601697, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.697 (+/-0.304) for {'C': 1229136.1730601697, 'kernel': 'rbf', 'gamma': 9.853715068586198e-07}
0.697 (+/-0.304) for {'C': 1229136.1730601697, 'kernel': 'rbf', 'gamma': 9.890084450210653e-07}
0.697 (+/-0.306) for {'C': 1229136.1730601697, 'kernel': 'rbf', 'gamma': 9.926588068710316e-07}
0.697 (+/-0.306) for {'C': 1229136.1730601697, 'kernel': 'rbf', 'gamma': 9.963226419544168e-07}
0.698 (+/-0.306) for {'C': 1229136.1730601697, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.698 (+/-0.306) for {'C': 1229136.1730601697, 'kernel': 'rbf', 'gamma': 1.003690930920098e-06}
0.697 (+/-0.304) for {'C': 1229136.1730601697, 'kernel': 'rbf', 'gamma': 1.0073954848112503e-06}
0.697 (+/-0.304) for {'C': 1229136.1730601697, 'kernel': 'rbf', 'gamma': 1.0111137119549073e-06}
0.697 (+/-0.305) for {'C': 1229136.1730601697, 'kernel': 'rbf', 'gamma': 1.0148456628180941e-06}
0.697 (+/-0.304) for {'C': 1229136.1730601697, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.697 (+/-0.304) for {'C': 1233672.8297663252, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.697 (+/-0.304) for {'C': 1233672.8297663252, 'kernel': 'rbf', 'gamma': 9.853715068586198e-07}
0.697 (+/-0.304) for {'C': 1233672.8297663252, 'kernel': 'rbf', 'gamma': 9.890084450210653e-07}
0.697 (+/-0.306) for {'C': 1233672.8297663252, 'kernel': 'rbf', 'gamma': 9.926588068710316e-07}
0.697 (+/-0.306) for {'C': 1233672.8297663252, 'kernel': 'rbf', 'gamma': 9.963226419544168e-07}
0.698 (+/-0.306) for {'C': 1233672.8297663252, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.698 (+/-0.306) for {'C': 1233672.8297663252, 'kernel': 'rbf', 'gamma': 1.003690930920098e-06}
0.697 (+/-0.305) for {'C': 1233672.8297663252, 'kernel': 'rbf', 'gamma': 1.0073954848112503e-06}
0.697 (+/-0.304) for {'C': 1233672.8297663252, 'kernel': 'rbf', 'gamma': 1.0111137119549073e-06}
0.697 (+/-0.305) for {'C': 1233672.8297663252, 'kernel': 'rbf', 'gamma': 1.0148456628180941e-06}
0.697 (+/-0.304) for {'C': 1233672.8297663252, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.697 (+/-0.304) for {'C': 1238226.2309589945, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.696 (+/-0.304) for {'C': 1238226.2309589945, 'kernel': 'rbf', 'gamma': 9.853715068586198e-07}
0.697 (+/-0.304) for {'C': 1238226.2309589945, 'kernel': 'rbf', 'gamma': 9.890084450210653e-07}
0.697 (+/-0.306) for {'C': 1238226.2309589945, 'kernel': 'rbf', 'gamma': 9.926588068710316e-07}
0.697 (+/-0.306) for {'C': 1238226.2309589945, 'kernel': 'rbf', 'gamma': 9.963226419544168e-07}
0.698 (+/-0.306) for {'C': 1238226.2309589945, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.698 (+/-0.306) for {'C': 1238226.2309589945, 'kernel': 'rbf', 'gamma': 1.003690930920098e-06}
0.697 (+/-0.304) for {'C': 1238226.2309589945, 'kernel': 'rbf', 'gamma': 1.0073954848112503e-06}
0.697 (+/-0.304) for {'C': 1238226.2309589945, 'kernel': 'rbf', 'gamma': 1.0111137119549073e-06}
0.697 (+/-0.305) for {'C': 1238226.2309589945, 'kernel': 'rbf', 'gamma': 1.0148456628180941e-06}
0.697 (+/-0.304) for {'C': 1238226.2309589945, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.697 (+/-0.304) for {'C': 1242796.4384409143, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.696 (+/-0.304) for {'C': 1242796.4384409143, 'kernel': 'rbf', 'gamma': 9.853715068586198e-07}
0.697 (+/-0.304) for {'C': 1242796.4384409143, 'kernel': 'rbf', 'gamma': 9.890084450210653e-07}
0.697 (+/-0.305) for {'C': 1242796.4384409143, 'kernel': 'rbf', 'gamma': 9.926588068710316e-07}
0.697 (+/-0.306) for {'C': 1242796.4384409143, 'kernel': 'rbf', 'gamma': 9.963226419544168e-07}
0.698 (+/-0.306) for {'C': 1242796.4384409143, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.698 (+/-0.306) for {'C': 1242796.4384409143, 'kernel': 'rbf', 'gamma': 1.003690930920098e-06}
0.697 (+/-0.304) for {'C': 1242796.4384409143, 'kernel': 'rbf', 'gamma': 1.0073954848112503e-06}
0.697 (+/-0.304) for {'C': 1242796.4384409143, 'kernel': 'rbf', 'gamma': 1.0111137119549073e-06}
0.697 (+/-0.305) for {'C': 1242796.4384409143, 'kernel': 'rbf', 'gamma': 1.0148456628180941e-06}
0.697 (+/-0.304) for {'C': 1242796.4384409143, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.697 (+/-0.304) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.696 (+/-0.304) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 9.853715068586198e-07}
0.697 (+/-0.304) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 9.890084450210653e-07}
0.697 (+/-0.305) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 9.926588068710316e-07}
0.697 (+/-0.306) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 9.963226419544168e-07}
0.698 (+/-0.306) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.698 (+/-0.306) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.003690930920098e-06}
0.697 (+/-0.304) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.0073954848112503e-06}
0.697 (+/-0.304) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.0111137119549073e-06}
0.697 (+/-0.305) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.0148456628180941e-06}
0.697 (+/-0.304) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.697 (+/-0.304) for {'C': 1251987.520624883, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.697 (+/-0.304) for {'C': 1251987.520624883, 'kernel': 'rbf', 'gamma': 9.853715068586198e-07}
0.697 (+/-0.304) for {'C': 1251987.520624883, 'kernel': 'rbf', 'gamma': 9.890084450210653e-07}
0.697 (+/-0.305) for {'C': 1251987.520624883, 'kernel': 'rbf', 'gamma': 9.926588068710316e-07}
0.697 (+/-0.306) for {'C': 1251987.520624883, 'kernel': 'rbf', 'gamma': 9.963226419544168e-07}
0.698 (+/-0.306) for {'C': 1251987.520624883, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.698 (+/-0.306) for {'C': 1251987.520624883, 'kernel': 'rbf', 'gamma': 1.003690930920098e-06}
0.697 (+/-0.304) for {'C': 1251987.520624883, 'kernel': 'rbf', 'gamma': 1.0073954848112503e-06}
0.697 (+/-0.304) for {'C': 1251987.520624883, 'kernel': 'rbf', 'gamma': 1.0111137119549073e-06}
0.697 (+/-0.305) for {'C': 1251987.520624883, 'kernel': 'rbf', 'gamma': 1.0148456628180941e-06}
0.697 (+/-0.304) for {'C': 1251987.520624883, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.697 (+/-0.304) for {'C': 1256608.520076331, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.696 (+/-0.304) for {'C': 1256608.520076331, 'kernel': 'rbf', 'gamma': 9.853715068586198e-07}
0.697 (+/-0.304) for {'C': 1256608.520076331, 'kernel': 'rbf', 'gamma': 9.890084450210653e-07}
0.697 (+/-0.305) for {'C': 1256608.520076331, 'kernel': 'rbf', 'gamma': 9.926588068710316e-07}
0.697 (+/-0.306) for {'C': 1256608.520076331, 'kernel': 'rbf', 'gamma': 9.963226419544168e-07}
0.698 (+/-0.306) for {'C': 1256608.520076331, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.698 (+/-0.306) for {'C': 1256608.520076331, 'kernel': 'rbf', 'gamma': 1.003690930920098e-06}
0.697 (+/-0.305) for {'C': 1256608.520076331, 'kernel': 'rbf', 'gamma': 1.0073954848112503e-06}
0.697 (+/-0.304) for {'C': 1256608.520076331, 'kernel': 'rbf', 'gamma': 1.0111137119549073e-06}
0.697 (+/-0.305) for {'C': 1256608.520076331, 'kernel': 'rbf', 'gamma': 1.0148456628180941e-06}
0.697 (+/-0.304) for {'C': 1256608.520076331, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.697 (+/-0.304) for {'C': 1261246.5753175393, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.696 (+/-0.304) for {'C': 1261246.5753175393, 'kernel': 'rbf', 'gamma': 9.853715068586198e-07}
0.697 (+/-0.304) for {'C': 1261246.5753175393, 'kernel': 'rbf', 'gamma': 9.890084450210653e-07}
0.697 (+/-0.306) for {'C': 1261246.5753175393, 'kernel': 'rbf', 'gamma': 9.926588068710316e-07}
0.697 (+/-0.306) for {'C': 1261246.5753175393, 'kernel': 'rbf', 'gamma': 9.963226419544168e-07}
0.698 (+/-0.306) for {'C': 1261246.5753175393, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.698 (+/-0.306) for {'C': 1261246.5753175393, 'kernel': 'rbf', 'gamma': 1.003690930920098e-06}
0.697 (+/-0.304) for {'C': 1261246.5753175393, 'kernel': 'rbf', 'gamma': 1.0073954848112503e-06}
0.697 (+/-0.304) for {'C': 1261246.5753175393, 'kernel': 'rbf', 'gamma': 1.0111137119549073e-06}
0.697 (+/-0.305) for {'C': 1261246.5753175393, 'kernel': 'rbf', 'gamma': 1.0148456628180941e-06}
0.697 (+/-0.304) for {'C': 1261246.5753175393, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.697 (+/-0.304) for {'C': 1265901.7493002433, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.696 (+/-0.304) for {'C': 1265901.7493002433, 'kernel': 'rbf', 'gamma': 9.853715068586198e-07}
0.697 (+/-0.304) for {'C': 1265901.7493002433, 'kernel': 'rbf', 'gamma': 9.890084450210653e-07}
0.697 (+/-0.305) for {'C': 1265901.7493002433, 'kernel': 'rbf', 'gamma': 9.926588068710316e-07}
0.697 (+/-0.306) for {'C': 1265901.7493002433, 'kernel': 'rbf', 'gamma': 9.963226419544168e-07}
0.698 (+/-0.306) for {'C': 1265901.7493002433, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.697 (+/-0.306) for {'C': 1265901.7493002433, 'kernel': 'rbf', 'gamma': 1.003690930920098e-06}
0.697 (+/-0.304) for {'C': 1265901.7493002433, 'kernel': 'rbf', 'gamma': 1.0073954848112503e-06}
0.697 (+/-0.304) for {'C': 1265901.7493002433, 'kernel': 'rbf', 'gamma': 1.0111137119549073e-06}
0.697 (+/-0.305) for {'C': 1265901.7493002433, 'kernel': 'rbf', 'gamma': 1.0148456628180941e-06}
0.697 (+/-0.304) for {'C': 1265901.7493002433, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.697 (+/-0.304) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.696 (+/-0.304) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 9.853715068586198e-07}
0.697 (+/-0.304) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 9.890084450210653e-07}
0.697 (+/-0.305) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 9.926588068710316e-07}
0.697 (+/-0.306) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 9.963226419544168e-07}
0.698 (+/-0.306) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.697 (+/-0.306) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.003690930920098e-06}
0.697 (+/-0.304) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.0073954848112503e-06}
0.697 (+/-0.304) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.0111137119549073e-06}
0.697 (+/-0.305) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.0148456628180941e-06}
0.697 (+/-0.304) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.683 (+/-0.296) for {'C': 1.0, 'kernel': 'linear'}
0.664 (+/-0.314) for {'C': 10.0, 'kernel': 'linear'}
0.660 (+/-0.316) for {'C': 100.0, 'kernel': 'linear'}
0.612 (+/-0.241) for {'C': 1000.0, 'kernel': 'linear'}
0.611 (+/-0.239) for {'C': 10000.0, 'kernel': 'linear'}
0.611 (+/-0.245) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.11      0.17      0.13         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.697753833786792
第2轮loop中，tunemodel函数得到的最优参数是： ([{'C': array([1238226.23095899, 1239138.92597054, 1240052.29372844,
       1240966.33472858, 1241881.0494672 , 1242796.43844091,
       1243712.5021467 , 1244629.2410819 , 1245546.65574423,
       1246464.74663176, 1247383.51424294]), 'kernel': ['rbf'], 'gamma': array([9.96322642e-07, 9.97057030e-07, 9.97791960e-07, 9.98527431e-07,
       9.99263444e-07, 1.00000000e-06, 1.00073710e-06, 1.00147474e-06,
       1.00221293e-06, 1.00295166e-06, 1.00369093e-06])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}], {'C': 1242796.4384409143, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}, 0.6996794871794872)
这是第2次迭代微调C和gamma。
第2次迭代，得到delta： [4587.07580203    0.        ]
SVC模型clf_op_iter的参数是： {'kernel': 'rbf', 'decision_function_shape': 'ovr', 'class_weight': {-1: 15}, 'tol': 0.001, 'gamma': 1.0000000000000002e-06, 'random_state': 0, 'cache_size': 200, 'verbose': False, 'degree': 3, 'C': 1242796.4384409143, 'probability': False, 'shrinking': True, 'coef0': 0.0, 'max_iter': -1}

训练模型SVC对训练样本的预测准确率： 0.9461374911410347

测试集中，预测为舞弊样本的有： (array([   8,   10,   11,   12,   13,   14,   21,   22,   24,   25,   26,
         27,   28,   30,   33,   34,   35,   36,   37,   39,   40,   42,
         43,   44,   46,   47,   48,   49,   50,   51,   52,   53,   56,
         58,   59,   60,   61,   62,   64,   65,   66,   67,   68,   69,
         70,   71,   72,   73,   74,   75,   76,   77,   78,   79,   82,
         88,   90,   91,   97,   98,  101,  102,  109,  111,  112,  113,
        114,  115,  116,  117,  118,  119,  121,  127,  129,  136,  142,
        143,  144,  145,  146,  147,  148,  150,  151,  153,  157,  158,
        163,  164,  165,  166,  176,  177,  178,  179,  180,  181,  182,
        183,  184,  185,  186,  192,  193,  195,  197,  200,  201,  202,
        205,  207,  208,  209,  211,  212,  213,  214,  215,  216,  219,
        225,  229,  230,  231,  232,  233,  234,  235,  236,  237,  238,
        239,  243,  244,  245,  246,  247,  248,  250,  251,  252,  253,
        254,  255,  256,  257,  258,  259,  260,  261,  264,  268,  269,
        270,  276,  277,  279,  281,  284,  286,  287,  288,  289,  300,
        301,  302,  306,  316,  318,  322,  323,  324,  325,  330,  331,
        332,  333,  338,  339,  340,  342,  344,  346,  347,  348,  349,
        351,  352,  353,  354,  358,  359,  360,  361,  362,  363,  364,
        365,  366,  370,  383,  384,  385,  386,  391,  392,  393,  394,
        395,  396,  397,  398,  399,  403,  404,  414,  419,  420,  421,
        422,  423,  426,  427,  428,  429,  430,  431,  432,  433,  434,
        438,  439,  440,  441,  442,  443,  444,  445,  447,  449,  450,
        454,  455,  457,  458,  459,  460,  461,  462,  463,  464,  465,
        466,  467,  468,  469,  471,  472,  473,  474,  475,  476,  477,
        478,  479,  481,  482,  483,  484,  485,  486,  487,  488,  489,
        490,  491,  495,  496,  498,  499,  500,  501,  502,  503,  504,
        505,  506,  507,  508,  509,  511,  513,  515,  516,  521,  529,
        532,  534,  535,  540,  543,  544,  545,  546,  547,  549,  555,
        558,  560,  561,  562,  563,  564,  565,  566,  567,  568,  571,
        572,  573,  584,  587,  589,  590,  593,  604,  605,  611,  614,
        615,  616,  617,  618,  619,  620,  621,  622,  623,  624,  629,
        630,  635,  641,  642,  643,  651,  652,  653,  655,  656,  657,
        658,  659,  660,  661,  662,  666,  667,  668,  669,  675,  676,
        677,  678,  685,  686,  687,  688,  693,  694,  695,  696,  697,
        699,  701,  702,  703,  706,  709,  716,  717,  718,  719,  720,
        721,  722,  723,  724,  725,  729,  735,  736,  742,  743,  750,
        751,  752,  753,  754,  756,  757,  758,  759,  760,  761,  769,
        774,  775,  776,  777,  778,  786,  787,  788,  789,  790,  791,
        792,  793,  794,  795,  796,  797,  813,  814,  816,  817,  818,
        819,  820,  825,  826,  829,  830,  840,  841,  845,  851,  852,
        853,  854,  855,  856,  857,  858,  859,  860,  861,  862,  864,
        872,  874,  875,  876,  877,  878,  879,  880,  881,  882,  883,
        884,  885,  897,  898,  900,  902,  903,  904,  905,  906,  907,
        910,  911,  912,  913,  914,  917,  919,  927,  928,  929,  930,
        931,  932,  933,  934,  935,  947,  951,  952,  953,  954,  955,
        956,  957,  958,  959,  960,  961,  962,  963,  964,  965,  966,
        967,  977,  978,  979,  980,  981,  982,  983,  984,  985,  995,
        998,  999, 1000, 1002, 1003, 1004, 1005, 1006, 1007, 1008, 1009,
       1012, 1013, 1014, 1015, 1016, 1017, 1020, 1021, 1022, 1030, 1037,
       1041, 1049, 1050, 1051, 1055, 1056, 1057, 1060, 1061, 1065, 1067,
       1068, 1074, 1080, 1081, 1082, 1085, 1086, 1089, 1095, 1096, 1097,
       1098, 1105, 1106, 1107, 1110, 1112, 1113, 1114, 1117, 1118, 1119,
       1121, 1122, 1123, 1124, 1125, 1127, 1129, 1131, 1132, 1134, 1135,
       1136, 1137, 1140, 1141, 1142, 1144, 1148, 1152, 1153, 1154, 1156,
       1158, 1160, 1164, 1167, 1168, 1170, 1171, 1175, 1180, 1181, 1183,
       1184, 1188, 1189, 1191, 1194, 1200, 1204, 1205, 1208, 1211, 1215,
       1216, 1217, 1218, 1219, 1222, 1230, 1232, 1233, 1234, 1235, 1236,
       1237, 1238, 1239, 1240, 1241, 1242, 1243, 1244, 1246, 1247, 1248,
       1249, 1250, 1251, 1252, 1254, 1255, 1256], dtype=int64),) 
测试集中，实际为舞弊样本的有： (array([1246, 1247, 1248, 1249, 1250, 1251, 1252, 1253, 1254, 1255, 1256],
      dtype=int64),) 
预测的舞弊样本数目： 645

训练模型SVC对测试样本的预测准确率： 0.5393338058114813
以上是第33次特征筛选。
第33次特征筛选，AUC值是： 0.6997300452356633
X_train_iter_svc.shape is: (1257, 19)
X_test_iter_svc.shape is: (1257, 19)
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.647 (+/-0.387) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.656 (+/-0.218) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.614 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.682 (+/-0.294) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.17      0.17      0.17         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6817292233489982
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.643 (+/-0.202) for {'C': 1.0, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.632 (+/-0.309) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.17      0.17      0.17         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.698 (+/-0.306) for {'C': 1.0, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.661 (+/-0.314) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.17      0.17      0.17         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6979151207848958
RBF的粗grid search最佳超参： {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001} RBF的粗grid search最高分值： 0.6817292233489982
linear的粗grid search最佳超参： {'C': 1.0, 'kernel': 'linear'} linear的粗grid search最高分值： 0.6979151207848958
粗grid search得到的parameter是：
 [{'C': array([1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03, 1.e+04, 1.e+05,
       1.e+06, 1.e+07, 1.e+08]), 'kernel': ['rbf'], 'gamma': array([1.e-08, 1.e-07, 1.e-06, 1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01,
       1.e+00, 1.e+01, 1.e+02])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.797 (+/-0.492) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.848 (+/-0.459) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.665 (+/-0.225) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.631 (+/-0.319) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.848 (+/-0.459) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.656 (+/-0.218) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.312) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.645 (+/-0.388) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.848 (+/-0.459) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.656 (+/-0.218) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.310) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.647 (+/-0.387) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.848 (+/-0.459) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.656 (+/-0.218) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.690 (+/-0.299) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.639 (+/-0.314) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.622 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.647 (+/-0.387) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.848 (+/-0.460) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.651 (+/-0.211) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.690 (+/-0.299) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.314) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.622 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.647 (+/-0.387) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.823 (+/-0.451) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.685 (+/-0.297) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.668 (+/-0.290) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.633 (+/-0.312) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.622 (+/-0.319) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.622 (+/-0.319) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.647 (+/-0.387) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.848 (+/-0.460) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.773 (+/-0.474) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.601 (+/-0.176) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.628 (+/-0.312) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.623 (+/-0.316) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.323) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.622 (+/-0.319) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.647 (+/-0.387) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.701 (+/-0.417) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.656 (+/-0.413) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.658 (+/-0.293) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.624 (+/-0.316) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.619 (+/-0.322) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.614 (+/-0.327) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.622 (+/-0.319) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.647 (+/-0.387) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.690 (+/-0.300) for {'C': 1.0, 'kernel': 'linear'}
0.673 (+/-0.294) for {'C': 10.0, 'kernel': 'linear'}
0.648 (+/-0.315) for {'C': 100.0, 'kernel': 'linear'}
0.632 (+/-0.309) for {'C': 1000.0, 'kernel': 'linear'}
0.622 (+/-0.319) for {'C': 10000.0, 'kernel': 'linear'}
0.618 (+/-0.322) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.642 (+/-0.236) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.658 (+/-0.217) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.698 (+/-0.307) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.006) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.658 (+/-0.217) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.682 (+/-0.294) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.632 (+/-0.225) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.240) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.006) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.658 (+/-0.217) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.682 (+/-0.294) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.699 (+/-0.308) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.587 (+/-0.233) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.006) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.658 (+/-0.217) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.682 (+/-0.294) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.698 (+/-0.307) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.662 (+/-0.316) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.240) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.006) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.683 (+/-0.296) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.698 (+/-0.306) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.698 (+/-0.307) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.662 (+/-0.316) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.587 (+/-0.233) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.006) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.683 (+/-0.296) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.698 (+/-0.306) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.681 (+/-0.294) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.661 (+/-0.315) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.636 (+/-0.323) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.587 (+/-0.233) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.239) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.006) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.683 (+/-0.296) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.667 (+/-0.316) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.696 (+/-0.301) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.661 (+/-0.311) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.658 (+/-0.308) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.611 (+/-0.237) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.587 (+/-0.233) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.239) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.006) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.674 (+/-0.324) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.616 (+/-0.326) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.698 (+/-0.308) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.659 (+/-0.307) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.633 (+/-0.321) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.587 (+/-0.235) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.587 (+/-0.233) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.239) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.006) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.699 (+/-0.307) for {'C': 1.0, 'kernel': 'linear'}
0.698 (+/-0.307) for {'C': 10.0, 'kernel': 'linear'}
0.664 (+/-0.316) for {'C': 100.0, 'kernel': 'linear'}
0.661 (+/-0.314) for {'C': 1000.0, 'kernel': 'linear'}
0.636 (+/-0.322) for {'C': 10000.0, 'kernel': 'linear'}
0.611 (+/-0.239) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.14      0.17      0.15         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6985566617198451
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.582 (+/-0.184) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.671 (+/-0.291) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.645 (+/-0.386) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.680 (+/-0.295) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.185) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.618 (+/-0.322) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.680 (+/-0.295) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.605 (+/-0.165) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.643 (+/-0.306) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.645 (+/-0.388) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.680 (+/-0.295) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.601 (+/-0.159) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.658 (+/-0.302) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.647 (+/-0.387) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.680 (+/-0.295) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.604 (+/-0.159) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.658 (+/-0.302) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.627 (+/-0.313) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.622 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.647 (+/-0.387) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.747 (+/-0.502) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.642 (+/-0.204) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.607 (+/-0.170) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.657 (+/-0.303) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.626 (+/-0.313) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.622 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.647 (+/-0.387) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.747 (+/-0.502) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.739 (+/-0.392) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.634 (+/-0.288) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.646 (+/-0.299) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.628 (+/-0.313) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.622 (+/-0.319) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.622 (+/-0.319) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.647 (+/-0.387) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.724 (+/-0.786) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.582 (+/-0.317) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.591 (+/-0.149) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.623 (+/-0.316) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.623 (+/-0.316) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.323) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.622 (+/-0.319) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.647 (+/-0.387) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.436 (+/-0.582) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.575 (+/-0.319) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.620 (+/-0.184) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.622 (+/-0.317) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.619 (+/-0.322) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.614 (+/-0.327) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.622 (+/-0.319) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.647 (+/-0.387) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.798 (+/-0.437) for {'C': 0.1, 'kernel': 'linear'}
0.643 (+/-0.202) for {'C': 1.0, 'kernel': 'linear'}
0.650 (+/-0.295) for {'C': 10.0, 'kernel': 'linear'}
0.626 (+/-0.314) for {'C': 100.0, 'kernel': 'linear'}
0.632 (+/-0.309) for {'C': 1000.0, 'kernel': 'linear'}
0.622 (+/-0.319) for {'C': 10000.0, 'kernel': 'linear'}
0.618 (+/-0.322) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.236) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.698 (+/-0.306) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.236) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.007) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.306) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.304) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.612 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.006) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.698 (+/-0.306) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.696 (+/-0.303) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.653 (+/-0.313) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.240) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.006) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.698 (+/-0.306) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.696 (+/-0.303) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.695 (+/-0.307) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.586 (+/-0.235) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.006) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.698 (+/-0.306) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.696 (+/-0.303) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.695 (+/-0.307) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.660 (+/-0.314) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.240) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.006) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.617 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.698 (+/-0.306) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.696 (+/-0.303) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.695 (+/-0.307) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.660 (+/-0.313) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.587 (+/-0.233) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.006) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.617 (+/-0.238) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.683 (+/-0.296) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.696 (+/-0.302) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.678 (+/-0.293) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.660 (+/-0.315) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.636 (+/-0.323) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.587 (+/-0.233) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.239) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.006) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.683 (+/-0.296) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.589 (+/-0.315) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.695 (+/-0.304) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.657 (+/-0.311) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.658 (+/-0.308) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.611 (+/-0.237) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.587 (+/-0.233) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.239) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.006) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.649 (+/-0.335) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.584 (+/-0.300) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.696 (+/-0.307) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.657 (+/-0.309) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.633 (+/-0.321) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.587 (+/-0.235) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.587 (+/-0.233) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.239) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.006) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.683 (+/-0.296) for {'C': 0.1, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 1.0, 'kernel': 'linear'}
0.696 (+/-0.304) for {'C': 10.0, 'kernel': 'linear'}
0.659 (+/-0.314) for {'C': 100.0, 'kernel': 'linear'}
0.661 (+/-0.314) for {'C': 1000.0, 'kernel': 'linear'}
0.636 (+/-0.322) for {'C': 10000.0, 'kernel': 'linear'}
0.611 (+/-0.239) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6982351183114849
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.848 (+/-0.459) for {'C': 99.99999999999997, 'kernel': 'rbf', 'gamma': 0.001}
0.715 (+/-0.319) for {'C': 99.99999999999997, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.690 (+/-0.300) for {'C': 99.99999999999997, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.690 (+/-0.300) for {'C': 99.99999999999997, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.690 (+/-0.300) for {'C': 99.99999999999997, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.656 (+/-0.218) for {'C': 99.99999999999997, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.673 (+/-0.294) for {'C': 99.99999999999997, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.673 (+/-0.294) for {'C': 99.99999999999997, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.673 (+/-0.294) for {'C': 99.99999999999997, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.681 (+/-0.297) for {'C': 99.99999999999997, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.656 (+/-0.312) for {'C': 99.99999999999997, 'kernel': 'rbf', 'gamma': 0.1}
0.715 (+/-0.319) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.001}
0.690 (+/-0.300) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.690 (+/-0.300) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.690 (+/-0.300) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.656 (+/-0.218) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.673 (+/-0.294) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.673 (+/-0.294) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.673 (+/-0.294) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.681 (+/-0.297) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.706 (+/-0.353) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.631 (+/-0.319) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.1}
0.690 (+/-0.300) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.001}
0.690 (+/-0.300) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.690 (+/-0.300) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.656 (+/-0.218) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.673 (+/-0.294) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.673 (+/-0.294) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.673 (+/-0.294) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.681 (+/-0.297) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.706 (+/-0.353) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.631 (+/-0.319) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.624 (+/-0.318) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.1}
0.690 (+/-0.300) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.001}
0.690 (+/-0.300) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.656 (+/-0.218) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.673 (+/-0.294) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.673 (+/-0.294) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.673 (+/-0.294) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.681 (+/-0.297) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.698 (+/-0.310) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.681 (+/-0.373) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.626 (+/-0.318) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.622 (+/-0.319) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.1}
0.690 (+/-0.300) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.001}
0.656 (+/-0.218) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.673 (+/-0.294) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.648 (+/-0.210) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.673 (+/-0.294) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.690 (+/-0.299) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.698 (+/-0.310) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.698 (+/-0.352) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.643 (+/-0.306) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.622 (+/-0.319) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.614 (+/-0.327) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.1}
0.656 (+/-0.218) for {'C': 999.9999999999987, 'kernel': 'rbf', 'gamma': 0.001}
0.673 (+/-0.294) for {'C': 999.9999999999987, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.648 (+/-0.210) for {'C': 999.9999999999987, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.673 (+/-0.294) for {'C': 999.9999999999987, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.681 (+/-0.297) for {'C': 999.9999999999987, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.698 (+/-0.310) for {'C': 999.9999999999987, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.698 (+/-0.352) for {'C': 999.9999999999987, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.656 (+/-0.312) for {'C': 999.9999999999987, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.622 (+/-0.319) for {'C': 999.9999999999987, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.614 (+/-0.327) for {'C': 999.9999999999987, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.614 (+/-0.327) for {'C': 999.9999999999987, 'kernel': 'rbf', 'gamma': 0.1}
0.673 (+/-0.294) for {'C': 1584.8931924611122, 'kernel': 'rbf', 'gamma': 0.001}
0.648 (+/-0.210) for {'C': 1584.8931924611122, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.673 (+/-0.294) for {'C': 1584.8931924611122, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.681 (+/-0.297) for {'C': 1584.8931924611122, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.698 (+/-0.310) for {'C': 1584.8931924611122, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.698 (+/-0.352) for {'C': 1584.8931924611122, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.656 (+/-0.312) for {'C': 1584.8931924611122, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.637 (+/-0.307) for {'C': 1584.8931924611122, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.614 (+/-0.327) for {'C': 1584.8931924611122, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.614 (+/-0.327) for {'C': 1584.8931924611122, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.620 (+/-0.320) for {'C': 1584.8931924611122, 'kernel': 'rbf', 'gamma': 0.1}
0.648 (+/-0.210) for {'C': 2511.886431509579, 'kernel': 'rbf', 'gamma': 0.001}
0.673 (+/-0.294) for {'C': 2511.886431509579, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.681 (+/-0.297) for {'C': 2511.886431509579, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.698 (+/-0.310) for {'C': 2511.886431509579, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.698 (+/-0.352) for {'C': 2511.886431509579, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.656 (+/-0.312) for {'C': 2511.886431509579, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.649 (+/-0.314) for {'C': 2511.886431509579, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.614 (+/-0.327) for {'C': 2511.886431509579, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.614 (+/-0.327) for {'C': 2511.886431509579, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.614 (+/-0.327) for {'C': 2511.886431509579, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.620 (+/-0.320) for {'C': 2511.886431509579, 'kernel': 'rbf', 'gamma': 0.1}
0.673 (+/-0.294) for {'C': 3981.0717055349724, 'kernel': 'rbf', 'gamma': 0.001}
0.681 (+/-0.297) for {'C': 3981.0717055349724, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.690 (+/-0.299) for {'C': 3981.0717055349724, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.698 (+/-0.352) for {'C': 3981.0717055349724, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.664 (+/-0.326) for {'C': 3981.0717055349724, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.644 (+/-0.309) for {'C': 3981.0717055349724, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.643 (+/-0.310) for {'C': 3981.0717055349724, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.614 (+/-0.327) for {'C': 3981.0717055349724, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.614 (+/-0.327) for {'C': 3981.0717055349724, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.620 (+/-0.320) for {'C': 3981.0717055349724, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.619 (+/-0.321) for {'C': 3981.0717055349724, 'kernel': 'rbf', 'gamma': 0.1}
0.681 (+/-0.297) for {'C': 6309.573444801927, 'kernel': 'rbf', 'gamma': 0.001}
0.690 (+/-0.299) for {'C': 6309.573444801927, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.698 (+/-0.352) for {'C': 6309.573444801927, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.664 (+/-0.326) for {'C': 6309.573444801927, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.649 (+/-0.314) for {'C': 6309.573444801927, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.643 (+/-0.310) for {'C': 6309.573444801927, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.626 (+/-0.318) for {'C': 6309.573444801927, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.614 (+/-0.327) for {'C': 6309.573444801927, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.614 (+/-0.327) for {'C': 6309.573444801927, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.620 (+/-0.320) for {'C': 6309.573444801927, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.621 (+/-0.320) for {'C': 6309.573444801927, 'kernel': 'rbf', 'gamma': 0.1}
0.690 (+/-0.299) for {'C': 9999.999999999995, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.352) for {'C': 9999.999999999995, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.664 (+/-0.326) for {'C': 9999.999999999995, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.649 (+/-0.314) for {'C': 9999.999999999995, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.646 (+/-0.317) for {'C': 9999.999999999995, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.639 (+/-0.314) for {'C': 9999.999999999995, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.626 (+/-0.318) for {'C': 9999.999999999995, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.614 (+/-0.327) for {'C': 9999.999999999995, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.614 (+/-0.327) for {'C': 9999.999999999995, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.620 (+/-0.320) for {'C': 9999.999999999995, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.622 (+/-0.319) for {'C': 9999.999999999995, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.690 (+/-0.300) for {'C': 1.0, 'kernel': 'linear'}
0.673 (+/-0.294) for {'C': 10.0, 'kernel': 'linear'}
0.648 (+/-0.315) for {'C': 100.0, 'kernel': 'linear'}
0.632 (+/-0.309) for {'C': 1000.0, 'kernel': 'linear'}
0.622 (+/-0.319) for {'C': 10000.0, 'kernel': 'linear'}
0.618 (+/-0.322) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.658 (+/-0.217) for {'C': 99.99999999999997, 'kernel': 'rbf', 'gamma': 0.001}
0.699 (+/-0.308) for {'C': 99.99999999999997, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.699 (+/-0.307) for {'C': 99.99999999999997, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.699 (+/-0.307) for {'C': 99.99999999999997, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.699 (+/-0.307) for {'C': 99.99999999999997, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.682 (+/-0.294) for {'C': 99.99999999999997, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.698 (+/-0.307) for {'C': 99.99999999999997, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.698 (+/-0.307) for {'C': 99.99999999999997, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.698 (+/-0.307) for {'C': 99.99999999999997, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.682 (+/-0.294) for {'C': 99.99999999999997, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.632 (+/-0.225) for {'C': 99.99999999999997, 'kernel': 'rbf', 'gamma': 0.1}
0.699 (+/-0.308) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.001}
0.699 (+/-0.307) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.699 (+/-0.307) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.699 (+/-0.307) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.682 (+/-0.294) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.698 (+/-0.307) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.698 (+/-0.307) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.698 (+/-0.307) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.682 (+/-0.294) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.682 (+/-0.296) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.615 (+/-0.237) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.1}
0.699 (+/-0.307) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.001}
0.699 (+/-0.307) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.699 (+/-0.307) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.682 (+/-0.294) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.698 (+/-0.307) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.698 (+/-0.307) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.698 (+/-0.307) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.682 (+/-0.294) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.682 (+/-0.296) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.615 (+/-0.237) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.614 (+/-0.237) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.1}
0.699 (+/-0.307) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.001}
0.699 (+/-0.307) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.682 (+/-0.294) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.698 (+/-0.307) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.698 (+/-0.307) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.698 (+/-0.307) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.682 (+/-0.294) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.699 (+/-0.307) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.665 (+/-0.316) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.614 (+/-0.238) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.614 (+/-0.238) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.1}
0.699 (+/-0.307) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.001}
0.682 (+/-0.294) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.698 (+/-0.307) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.698 (+/-0.307) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.698 (+/-0.307) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.699 (+/-0.307) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.699 (+/-0.308) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.682 (+/-0.296) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.640 (+/-0.235) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.614 (+/-0.237) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.587 (+/-0.233) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.1}
0.682 (+/-0.294) for {'C': 999.9999999999987, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.307) for {'C': 999.9999999999987, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.698 (+/-0.307) for {'C': 999.9999999999987, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.698 (+/-0.307) for {'C': 999.9999999999987, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.698 (+/-0.307) for {'C': 999.9999999999987, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.699 (+/-0.308) for {'C': 999.9999999999987, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.682 (+/-0.296) for {'C': 999.9999999999987, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.665 (+/-0.315) for {'C': 999.9999999999987, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.614 (+/-0.237) for {'C': 999.9999999999987, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.588 (+/-0.233) for {'C': 999.9999999999987, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.587 (+/-0.233) for {'C': 999.9999999999987, 'kernel': 'rbf', 'gamma': 0.1}
0.698 (+/-0.307) for {'C': 1584.8931924611122, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.307) for {'C': 1584.8931924611122, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.698 (+/-0.307) for {'C': 1584.8931924611122, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.698 (+/-0.307) for {'C': 1584.8931924611122, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.699 (+/-0.308) for {'C': 1584.8931924611122, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.682 (+/-0.296) for {'C': 1584.8931924611122, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.665 (+/-0.315) for {'C': 1584.8931924611122, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.639 (+/-0.235) for {'C': 1584.8931924611122, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.588 (+/-0.233) for {'C': 1584.8931924611122, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.587 (+/-0.234) for {'C': 1584.8931924611122, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.612 (+/-0.240) for {'C': 1584.8931924611122, 'kernel': 'rbf', 'gamma': 0.1}
0.698 (+/-0.307) for {'C': 2511.886431509579, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.307) for {'C': 2511.886431509579, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.698 (+/-0.307) for {'C': 2511.886431509579, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.699 (+/-0.308) for {'C': 2511.886431509579, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.682 (+/-0.296) for {'C': 2511.886431509579, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.665 (+/-0.316) for {'C': 2511.886431509579, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.664 (+/-0.316) for {'C': 2511.886431509579, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.588 (+/-0.232) for {'C': 2511.886431509579, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.587 (+/-0.234) for {'C': 2511.886431509579, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.588 (+/-0.233) for {'C': 2511.886431509579, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.612 (+/-0.240) for {'C': 2511.886431509579, 'kernel': 'rbf', 'gamma': 0.1}
0.698 (+/-0.307) for {'C': 3981.0717055349724, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.307) for {'C': 3981.0717055349724, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.698 (+/-0.307) for {'C': 3981.0717055349724, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.682 (+/-0.296) for {'C': 3981.0717055349724, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.665 (+/-0.316) for {'C': 3981.0717055349724, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.664 (+/-0.315) for {'C': 3981.0717055349724, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.663 (+/-0.316) for {'C': 3981.0717055349724, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.587 (+/-0.234) for {'C': 3981.0717055349724, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.587 (+/-0.233) for {'C': 3981.0717055349724, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.612 (+/-0.240) for {'C': 3981.0717055349724, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.612 (+/-0.240) for {'C': 3981.0717055349724, 'kernel': 'rbf', 'gamma': 0.1}
0.698 (+/-0.307) for {'C': 6309.573444801927, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.307) for {'C': 6309.573444801927, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.682 (+/-0.296) for {'C': 6309.573444801927, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.665 (+/-0.316) for {'C': 6309.573444801927, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.664 (+/-0.316) for {'C': 6309.573444801927, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.663 (+/-0.316) for {'C': 6309.573444801927, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.612 (+/-0.242) for {'C': 6309.573444801927, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.587 (+/-0.233) for {'C': 6309.573444801927, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.587 (+/-0.234) for {'C': 6309.573444801927, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.612 (+/-0.240) for {'C': 6309.573444801927, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.612 (+/-0.240) for {'C': 6309.573444801927, 'kernel': 'rbf', 'gamma': 0.1}
0.698 (+/-0.307) for {'C': 9999.999999999995, 'kernel': 'rbf', 'gamma': 0.001}
0.682 (+/-0.296) for {'C': 9999.999999999995, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.665 (+/-0.316) for {'C': 9999.999999999995, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.664 (+/-0.316) for {'C': 9999.999999999995, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.663 (+/-0.317) for {'C': 9999.999999999995, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.662 (+/-0.316) for {'C': 9999.999999999995, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.612 (+/-0.242) for {'C': 9999.999999999995, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.587 (+/-0.234) for {'C': 9999.999999999995, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.587 (+/-0.234) for {'C': 9999.999999999995, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.612 (+/-0.240) for {'C': 9999.999999999995, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.612 (+/-0.240) for {'C': 9999.999999999995, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.699 (+/-0.307) for {'C': 1.0, 'kernel': 'linear'}
0.698 (+/-0.307) for {'C': 10.0, 'kernel': 'linear'}
0.664 (+/-0.316) for {'C': 100.0, 'kernel': 'linear'}
0.661 (+/-0.314) for {'C': 1000.0, 'kernel': 'linear'}
0.636 (+/-0.322) for {'C': 10000.0, 'kernel': 'linear'}
0.611 (+/-0.239) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 99.99999999999997, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6990374309506142
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.680 (+/-0.295) for {'C': 99.99999999999997, 'kernel': 'rbf', 'gamma': 0.001}
0.665 (+/-0.225) for {'C': 99.99999999999997, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.660 (+/-0.219) for {'C': 99.99999999999997, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.660 (+/-0.219) for {'C': 99.99999999999997, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.640 (+/-0.199) for {'C': 99.99999999999997, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.605 (+/-0.165) for {'C': 99.99999999999997, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.643 (+/-0.285) for {'C': 99.99999999999997, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.646 (+/-0.284) for {'C': 99.99999999999997, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.656 (+/-0.291) for {'C': 99.99999999999997, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.650 (+/-0.296) for {'C': 99.99999999999997, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.643 (+/-0.306) for {'C': 99.99999999999997, 'kernel': 'rbf', 'gamma': 0.1}
0.665 (+/-0.225) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.001}
0.660 (+/-0.219) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.660 (+/-0.219) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.640 (+/-0.199) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.605 (+/-0.165) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.647 (+/-0.285) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.646 (+/-0.284) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.656 (+/-0.291) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.651 (+/-0.295) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.649 (+/-0.298) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.619 (+/-0.322) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.219) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.001}
0.660 (+/-0.219) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.640 (+/-0.199) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.601 (+/-0.159) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.647 (+/-0.285) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.646 (+/-0.284) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.656 (+/-0.291) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.649 (+/-0.296) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.647 (+/-0.298) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.628 (+/-0.313) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.618 (+/-0.322) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.219) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.199) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.601 (+/-0.159) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.647 (+/-0.285) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.650 (+/-0.284) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.658 (+/-0.290) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.649 (+/-0.296) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.656 (+/-0.304) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.628 (+/-0.313) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.622 (+/-0.319) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.614 (+/-0.328) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.1}
0.640 (+/-0.199) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.001}
0.601 (+/-0.159) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.647 (+/-0.285) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.625 (+/-0.184) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.658 (+/-0.290) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.658 (+/-0.302) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.658 (+/-0.302) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.645 (+/-0.301) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.625 (+/-0.315) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.618 (+/-0.322) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.614 (+/-0.327) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.1}
0.601 (+/-0.159) for {'C': 999.9999999999987, 'kernel': 'rbf', 'gamma': 0.001}
0.647 (+/-0.285) for {'C': 999.9999999999987, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.623 (+/-0.183) for {'C': 999.9999999999987, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.658 (+/-0.290) for {'C': 999.9999999999987, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.658 (+/-0.302) for {'C': 999.9999999999987, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.658 (+/-0.302) for {'C': 999.9999999999987, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.645 (+/-0.301) for {'C': 999.9999999999987, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.626 (+/-0.314) for {'C': 999.9999999999987, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.621 (+/-0.320) for {'C': 999.9999999999987, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.619 (+/-0.322) for {'C': 999.9999999999987, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.614 (+/-0.327) for {'C': 999.9999999999987, 'kernel': 'rbf', 'gamma': 0.1}
0.647 (+/-0.285) for {'C': 1584.8931924611122, 'kernel': 'rbf', 'gamma': 0.001}
0.623 (+/-0.183) for {'C': 1584.8931924611122, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.658 (+/-0.290) for {'C': 1584.8931924611122, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.658 (+/-0.302) for {'C': 1584.8931924611122, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.658 (+/-0.302) for {'C': 1584.8931924611122, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.645 (+/-0.301) for {'C': 1584.8931924611122, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.626 (+/-0.314) for {'C': 1584.8931924611122, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.626 (+/-0.314) for {'C': 1584.8931924611122, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.623 (+/-0.319) for {'C': 1584.8931924611122, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.614 (+/-0.327) for {'C': 1584.8931924611122, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.620 (+/-0.320) for {'C': 1584.8931924611122, 'kernel': 'rbf', 'gamma': 0.1}
0.623 (+/-0.183) for {'C': 2511.886431509579, 'kernel': 'rbf', 'gamma': 0.001}
0.658 (+/-0.290) for {'C': 2511.886431509579, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.658 (+/-0.302) for {'C': 2511.886431509579, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.658 (+/-0.302) for {'C': 2511.886431509579, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.645 (+/-0.301) for {'C': 2511.886431509579, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.625 (+/-0.314) for {'C': 2511.886431509579, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.626 (+/-0.314) for {'C': 2511.886431509579, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.622 (+/-0.319) for {'C': 2511.886431509579, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.619 (+/-0.322) for {'C': 2511.886431509579, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.614 (+/-0.327) for {'C': 2511.886431509579, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.620 (+/-0.320) for {'C': 2511.886431509579, 'kernel': 'rbf', 'gamma': 0.1}
0.658 (+/-0.290) for {'C': 3981.0717055349724, 'kernel': 'rbf', 'gamma': 0.001}
0.658 (+/-0.302) for {'C': 3981.0717055349724, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.658 (+/-0.302) for {'C': 3981.0717055349724, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.645 (+/-0.301) for {'C': 3981.0717055349724, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.625 (+/-0.314) for {'C': 3981.0717055349724, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.626 (+/-0.314) for {'C': 3981.0717055349724, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.625 (+/-0.315) for {'C': 3981.0717055349724, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.623 (+/-0.319) for {'C': 3981.0717055349724, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.614 (+/-0.327) for {'C': 3981.0717055349724, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.620 (+/-0.320) for {'C': 3981.0717055349724, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.619 (+/-0.321) for {'C': 3981.0717055349724, 'kernel': 'rbf', 'gamma': 0.1}
0.658 (+/-0.302) for {'C': 6309.573444801927, 'kernel': 'rbf', 'gamma': 0.001}
0.658 (+/-0.302) for {'C': 6309.573444801927, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.646 (+/-0.301) for {'C': 6309.573444801927, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.625 (+/-0.314) for {'C': 6309.573444801927, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.626 (+/-0.314) for {'C': 6309.573444801927, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.625 (+/-0.315) for {'C': 6309.573444801927, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.623 (+/-0.319) for {'C': 6309.573444801927, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.618 (+/-0.322) for {'C': 6309.573444801927, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.614 (+/-0.327) for {'C': 6309.573444801927, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.620 (+/-0.320) for {'C': 6309.573444801927, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.621 (+/-0.320) for {'C': 6309.573444801927, 'kernel': 'rbf', 'gamma': 0.1}
0.658 (+/-0.302) for {'C': 9999.999999999995, 'kernel': 'rbf', 'gamma': 0.001}
0.646 (+/-0.301) for {'C': 9999.999999999995, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.625 (+/-0.314) for {'C': 9999.999999999995, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.626 (+/-0.314) for {'C': 9999.999999999995, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.625 (+/-0.315) for {'C': 9999.999999999995, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.627 (+/-0.313) for {'C': 9999.999999999995, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.622 (+/-0.319) for {'C': 9999.999999999995, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.614 (+/-0.327) for {'C': 9999.999999999995, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.614 (+/-0.327) for {'C': 9999.999999999995, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.620 (+/-0.320) for {'C': 9999.999999999995, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.622 (+/-0.319) for {'C': 9999.999999999995, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.798 (+/-0.437) for {'C': 0.1, 'kernel': 'linear'}
0.643 (+/-0.202) for {'C': 1.0, 'kernel': 'linear'}
0.650 (+/-0.295) for {'C': 10.0, 'kernel': 'linear'}
0.626 (+/-0.314) for {'C': 100.0, 'kernel': 'linear'}
0.632 (+/-0.309) for {'C': 1000.0, 'kernel': 'linear'}
0.622 (+/-0.319) for {'C': 10000.0, 'kernel': 'linear'}
0.618 (+/-0.322) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.698 (+/-0.306) for {'C': 99.99999999999997, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.307) for {'C': 99.99999999999997, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.698 (+/-0.306) for {'C': 99.99999999999997, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.698 (+/-0.306) for {'C': 99.99999999999997, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.698 (+/-0.305) for {'C': 99.99999999999997, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.696 (+/-0.303) for {'C': 99.99999999999997, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.697 (+/-0.304) for {'C': 99.99999999999997, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.697 (+/-0.305) for {'C': 99.99999999999997, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.697 (+/-0.305) for {'C': 99.99999999999997, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.680 (+/-0.292) for {'C': 99.99999999999997, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.653 (+/-0.313) for {'C': 99.99999999999997, 'kernel': 'rbf', 'gamma': 0.1}
0.698 (+/-0.307) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.306) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.698 (+/-0.306) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.698 (+/-0.305) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.696 (+/-0.303) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.697 (+/-0.304) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.697 (+/-0.305) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.697 (+/-0.305) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.680 (+/-0.293) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.678 (+/-0.294) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.609 (+/-0.244) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.1}
0.698 (+/-0.306) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.306) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.698 (+/-0.305) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.696 (+/-0.303) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.697 (+/-0.304) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.697 (+/-0.305) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.697 (+/-0.305) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.679 (+/-0.292) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.678 (+/-0.294) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.659 (+/-0.315) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.609 (+/-0.243) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.1}
0.698 (+/-0.306) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.305) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.696 (+/-0.303) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.697 (+/-0.305) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.697 (+/-0.305) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.697 (+/-0.306) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.679 (+/-0.292) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.695 (+/-0.307) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.660 (+/-0.315) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.634 (+/-0.327) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.585 (+/-0.238) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.1}
0.698 (+/-0.305) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.001}
0.696 (+/-0.303) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.697 (+/-0.305) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.697 (+/-0.305) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.697 (+/-0.306) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.696 (+/-0.305) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.695 (+/-0.307) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.676 (+/-0.296) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.659 (+/-0.313) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.609 (+/-0.243) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.585 (+/-0.237) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.1}
0.696 (+/-0.303) for {'C': 999.9999999999987, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.305) for {'C': 999.9999999999987, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.697 (+/-0.305) for {'C': 999.9999999999987, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.697 (+/-0.306) for {'C': 999.9999999999987, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.696 (+/-0.305) for {'C': 999.9999999999987, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.695 (+/-0.307) for {'C': 999.9999999999987, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.676 (+/-0.296) for {'C': 999.9999999999987, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.659 (+/-0.313) for {'C': 999.9999999999987, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.634 (+/-0.326) for {'C': 999.9999999999987, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.610 (+/-0.242) for {'C': 999.9999999999987, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.586 (+/-0.235) for {'C': 999.9999999999987, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.305) for {'C': 1584.8931924611122, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.305) for {'C': 1584.8931924611122, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.697 (+/-0.306) for {'C': 1584.8931924611122, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.696 (+/-0.305) for {'C': 1584.8931924611122, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.695 (+/-0.307) for {'C': 1584.8931924611122, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.676 (+/-0.296) for {'C': 1584.8931924611122, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.659 (+/-0.313) for {'C': 1584.8931924611122, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.659 (+/-0.313) for {'C': 1584.8931924611122, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.635 (+/-0.326) for {'C': 1584.8931924611122, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.585 (+/-0.236) for {'C': 1584.8931924611122, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.612 (+/-0.240) for {'C': 1584.8931924611122, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.305) for {'C': 2511.886431509579, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.306) for {'C': 2511.886431509579, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.696 (+/-0.305) for {'C': 2511.886431509579, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.695 (+/-0.307) for {'C': 2511.886431509579, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.676 (+/-0.296) for {'C': 2511.886431509579, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.659 (+/-0.313) for {'C': 2511.886431509579, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.659 (+/-0.314) for {'C': 2511.886431509579, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.634 (+/-0.326) for {'C': 2511.886431509579, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.610 (+/-0.242) for {'C': 2511.886431509579, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.587 (+/-0.234) for {'C': 2511.886431509579, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.612 (+/-0.240) for {'C': 2511.886431509579, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.306) for {'C': 3981.0717055349724, 'kernel': 'rbf', 'gamma': 0.001}
0.696 (+/-0.305) for {'C': 3981.0717055349724, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.695 (+/-0.307) for {'C': 3981.0717055349724, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.676 (+/-0.296) for {'C': 3981.0717055349724, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.659 (+/-0.313) for {'C': 3981.0717055349724, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.659 (+/-0.314) for {'C': 3981.0717055349724, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.659 (+/-0.313) for {'C': 3981.0717055349724, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.635 (+/-0.326) for {'C': 3981.0717055349724, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.586 (+/-0.235) for {'C': 3981.0717055349724, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.612 (+/-0.240) for {'C': 3981.0717055349724, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.612 (+/-0.240) for {'C': 3981.0717055349724, 'kernel': 'rbf', 'gamma': 0.1}
0.696 (+/-0.305) for {'C': 6309.573444801927, 'kernel': 'rbf', 'gamma': 0.001}
0.695 (+/-0.307) for {'C': 6309.573444801927, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.676 (+/-0.296) for {'C': 6309.573444801927, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.659 (+/-0.313) for {'C': 6309.573444801927, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.659 (+/-0.313) for {'C': 6309.573444801927, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.659 (+/-0.312) for {'C': 6309.573444801927, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.635 (+/-0.326) for {'C': 6309.573444801927, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.611 (+/-0.241) for {'C': 6309.573444801927, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.587 (+/-0.235) for {'C': 6309.573444801927, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.612 (+/-0.240) for {'C': 6309.573444801927, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.612 (+/-0.240) for {'C': 6309.573444801927, 'kernel': 'rbf', 'gamma': 0.1}
0.695 (+/-0.307) for {'C': 9999.999999999995, 'kernel': 'rbf', 'gamma': 0.001}
0.676 (+/-0.296) for {'C': 9999.999999999995, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.659 (+/-0.312) for {'C': 9999.999999999995, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.659 (+/-0.313) for {'C': 9999.999999999995, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.659 (+/-0.312) for {'C': 9999.999999999995, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.660 (+/-0.314) for {'C': 9999.999999999995, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.636 (+/-0.324) for {'C': 9999.999999999995, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.586 (+/-0.235) for {'C': 9999.999999999995, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.587 (+/-0.234) for {'C': 9999.999999999995, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.612 (+/-0.240) for {'C': 9999.999999999995, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.612 (+/-0.240) for {'C': 9999.999999999995, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.683 (+/-0.296) for {'C': 0.1, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 1.0, 'kernel': 'linear'}
0.696 (+/-0.304) for {'C': 10.0, 'kernel': 'linear'}
0.659 (+/-0.314) for {'C': 100.0, 'kernel': 'linear'}
0.661 (+/-0.314) for {'C': 1000.0, 'kernel': 'linear'}
0.636 (+/-0.322) for {'C': 10000.0, 'kernel': 'linear'}
0.611 (+/-0.239) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 99.99999999999997, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6983958900156649
发现最优参数C为原先的最大/最小值，直接重新设置超参。
循环迭代之前，delta is: [9.00000000e+02 8.41510681e-03]
执行tunemodel()函数前，使用的grid search parameter是：
 [{'C': array([1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03, 1.e+04,
       1.e+05, 1.e+06, 1.e+07]), 'kernel': ['rbf'], 'gamma': array([0.001     , 0.00109648, 0.00120226, 0.00131826, 0.00144544,
       0.00158489, 0.0017378 , 0.00190546, 0.0020893 , 0.00229087,
       0.00251189])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0010964781961431843}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0012022644346174128}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0013182567385564077}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0014454397707459273}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0015848931924611123}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0017378008287493752}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001905460717963248}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.002089296130854039}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.002290867652767771}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0010964781961431843}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0012022644346174128}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0013182567385564077}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0014454397707459273}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0015848931924611123}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0017378008287493752}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001905460717963248}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.002089296130854039}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.002290867652767771}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0010964781961431843}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0012022644346174128}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0013182567385564077}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0014454397707459273}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0015848931924611123}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0017378008287493752}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001905460717963248}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.002089296130854039}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.002290867652767771}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0010964781961431843}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0012022644346174128}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0013182567385564077}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0014454397707459273}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0015848931924611123}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0017378008287493752}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001905460717963248}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.002089296130854039}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.002290867652767771}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0010964781961431843}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0012022644346174128}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0013182567385564077}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0014454397707459273}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0015848931924611123}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0017378008287493752}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001905460717963248}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.002089296130854039}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.002290867652767771}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.848 (+/-0.459) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.848 (+/-0.459) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0010964781961431843}
0.823 (+/-0.451) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0012022644346174128}
0.823 (+/-0.451) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0013182567385564077}
0.748 (+/-0.389) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0014454397707459273}
0.715 (+/-0.319) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0015848931924611123}
0.698 (+/-0.319) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0017378008287493752}
0.693 (+/-0.308) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001905460717963248}
0.685 (+/-0.297) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.002089296130854039}
0.685 (+/-0.297) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.002290867652767771}
0.690 (+/-0.300) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.656 (+/-0.218) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.681 (+/-0.297) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0010964781961431843}
0.681 (+/-0.297) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0012022644346174128}
0.673 (+/-0.294) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0013182567385564077}
0.673 (+/-0.294) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0014454397707459273}
0.673 (+/-0.294) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0015848931924611123}
0.648 (+/-0.210) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0017378008287493752}
0.648 (+/-0.210) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001905460717963248}
0.648 (+/-0.210) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.002089296130854039}
0.648 (+/-0.210) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.002290867652767771}
0.648 (+/-0.210) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.690 (+/-0.299) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.310) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0010964781961431843}
0.690 (+/-0.309) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0012022644346174128}
0.698 (+/-0.352) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0013182567385564077}
0.698 (+/-0.352) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0014454397707459273}
0.698 (+/-0.352) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0015848931924611123}
0.681 (+/-0.373) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0017378008287493752}
0.681 (+/-0.373) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001905460717963248}
0.681 (+/-0.373) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.002089296130854039}
0.681 (+/-0.373) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.002290867652767771}
0.664 (+/-0.326) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.639 (+/-0.314) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.635 (+/-0.312) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0010964781961431843}
0.636 (+/-0.312) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0012022644346174128}
0.636 (+/-0.312) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0013182567385564077}
0.636 (+/-0.312) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0014454397707459273}
0.636 (+/-0.311) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0015848931924611123}
0.634 (+/-0.310) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0017378008287493752}
0.635 (+/-0.310) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001905460717963248}
0.636 (+/-0.311) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.002089296130854039}
0.636 (+/-0.311) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.002290867652767771}
0.635 (+/-0.310) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.622 (+/-0.319) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.623 (+/-0.319) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0010964781961431843}
0.622 (+/-0.319) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0012022644346174128}
0.621 (+/-0.320) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0013182567385564077}
0.624 (+/-0.318) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0014454397707459273}
0.619 (+/-0.322) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0015848931924611123}
0.620 (+/-0.321) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0017378008287493752}
0.618 (+/-0.322) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001905460717963248}
0.620 (+/-0.320) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.002089296130854039}
0.618 (+/-0.323) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.002290867652767771}
0.618 (+/-0.322) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.617 (+/-0.323) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0010964781961431843}
0.620 (+/-0.321) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0012022644346174128}
0.617 (+/-0.324) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0013182567385564077}
0.620 (+/-0.320) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0014454397707459273}
0.614 (+/-0.327) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0015848931924611123}
0.618 (+/-0.322) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0017378008287493752}
0.614 (+/-0.327) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001905460717963248}
0.614 (+/-0.327) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.002089296130854039}
0.614 (+/-0.327) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.002290867652767771}
0.614 (+/-0.327) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.690 (+/-0.300) for {'C': 1.0, 'kernel': 'linear'}
0.673 (+/-0.294) for {'C': 10.0, 'kernel': 'linear'}
0.648 (+/-0.315) for {'C': 100.0, 'kernel': 'linear'}
0.632 (+/-0.309) for {'C': 1000.0, 'kernel': 'linear'}
0.622 (+/-0.319) for {'C': 10000.0, 'kernel': 'linear'}
0.618 (+/-0.322) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0010964781961431843}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0012022644346174128}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0013182567385564077}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0014454397707459273}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0015848931924611123}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0017378008287493752}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001905460717963248}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.002089296130854039}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.002290867652767771}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0010964781961431843}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0012022644346174128}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0013182567385564077}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0014454397707459273}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0015848931924611123}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0017378008287493752}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001905460717963248}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.002089296130854039}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.002290867652767771}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0010964781961431843}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0012022644346174128}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0013182567385564077}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0014454397707459273}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0015848931924611123}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0017378008287493752}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001905460717963248}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.002089296130854039}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.002290867652767771}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0010964781961431843}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0012022644346174128}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0013182567385564077}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0014454397707459273}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0015848931924611123}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0017378008287493752}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001905460717963248}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.002089296130854039}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.002290867652767771}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0010964781961431843}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0012022644346174128}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0013182567385564077}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0014454397707459273}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0015848931924611123}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0017378008287493752}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001905460717963248}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.002089296130854039}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.002290867652767771}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.658 (+/-0.217) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.658 (+/-0.217) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0010964781961431843}
0.683 (+/-0.296) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0012022644346174128}
0.683 (+/-0.296) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0013182567385564077}
0.683 (+/-0.296) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0014454397707459273}
0.699 (+/-0.308) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0015848931924611123}
0.699 (+/-0.308) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0017378008287493752}
0.699 (+/-0.307) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001905460717963248}
0.698 (+/-0.306) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.002089296130854039}
0.698 (+/-0.306) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.002290867652767771}
0.699 (+/-0.307) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.682 (+/-0.294) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.307) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0010964781961431843}
0.698 (+/-0.307) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0012022644346174128}
0.698 (+/-0.307) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0013182567385564077}
0.698 (+/-0.307) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0014454397707459273}
0.698 (+/-0.307) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0015848931924611123}
0.698 (+/-0.307) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0017378008287493752}
0.698 (+/-0.307) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001905460717963248}
0.698 (+/-0.307) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.002089296130854039}
0.698 (+/-0.307) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.002290867652767771}
0.698 (+/-0.307) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.698 (+/-0.307) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.699 (+/-0.308) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0010964781961431843}
0.698 (+/-0.308) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0012022644346174128}
0.682 (+/-0.296) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0013182567385564077}
0.682 (+/-0.296) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0014454397707459273}
0.682 (+/-0.296) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0015848931924611123}
0.665 (+/-0.316) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0017378008287493752}
0.665 (+/-0.316) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001905460717963248}
0.665 (+/-0.317) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.002089296130854039}
0.665 (+/-0.317) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.002290867652767771}
0.665 (+/-0.316) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.662 (+/-0.316) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.662 (+/-0.316) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0010964781961431843}
0.662 (+/-0.316) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0012022644346174128}
0.662 (+/-0.316) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0013182567385564077}
0.662 (+/-0.316) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0014454397707459273}
0.662 (+/-0.316) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0015848931924611123}
0.662 (+/-0.316) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0017378008287493752}
0.662 (+/-0.316) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001905460717963248}
0.662 (+/-0.316) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.002089296130854039}
0.662 (+/-0.316) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.002290867652767771}
0.662 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.636 (+/-0.323) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.323) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0010964781961431843}
0.637 (+/-0.322) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0012022644346174128}
0.636 (+/-0.321) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0013182567385564077}
0.637 (+/-0.324) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0014454397707459273}
0.612 (+/-0.240) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0015848931924611123}
0.612 (+/-0.241) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0017378008287493752}
0.611 (+/-0.239) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001905460717963248}
0.612 (+/-0.240) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.002089296130854039}
0.611 (+/-0.238) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.002290867652767771}
0.611 (+/-0.239) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.611 (+/-0.237) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.587 (+/-0.234) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0010964781961431843}
0.636 (+/-0.318) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0012022644346174128}
0.610 (+/-0.238) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0013182567385564077}
0.612 (+/-0.240) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0014454397707459273}
0.587 (+/-0.234) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0015848931924611123}
0.612 (+/-0.239) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0017378008287493752}
0.587 (+/-0.234) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001905460717963248}
0.587 (+/-0.234) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.002089296130854039}
0.587 (+/-0.234) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.002290867652767771}
0.587 (+/-0.234) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.699 (+/-0.307) for {'C': 1.0, 'kernel': 'linear'}
0.698 (+/-0.307) for {'C': 10.0, 'kernel': 'linear'}
0.664 (+/-0.316) for {'C': 100.0, 'kernel': 'linear'}
0.661 (+/-0.314) for {'C': 1000.0, 'kernel': 'linear'}
0.636 (+/-0.322) for {'C': 10000.0, 'kernel': 'linear'}
0.611 (+/-0.239) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0015848931924611123}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6990374309506142
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0010964781961431843}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0012022644346174128}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0013182567385564077}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0014454397707459273}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0015848931924611123}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0017378008287493752}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001905460717963248}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.002089296130854039}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.002290867652767771}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0010964781961431843}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0012022644346174128}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0013182567385564077}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0014454397707459273}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0015848931924611123}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0017378008287493752}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001905460717963248}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.002089296130854039}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.002290867652767771}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0010964781961431843}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0012022644346174128}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0013182567385564077}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0014454397707459273}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0015848931924611123}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0017378008287493752}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001905460717963248}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.002089296130854039}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.002290867652767771}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0010964781961431843}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0012022644346174128}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0013182567385564077}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0014454397707459273}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0015848931924611123}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0017378008287493752}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001905460717963248}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.002089296130854039}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.002290867652767771}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0010964781961431843}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0012022644346174128}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0013182567385564077}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0014454397707459273}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0015848931924611123}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0017378008287493752}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001905460717963248}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.002089296130854039}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.002290867652767771}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.680 (+/-0.295) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.680 (+/-0.295) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0010964781961431843}
0.685 (+/-0.297) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0012022644346174128}
0.660 (+/-0.219) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0013182567385564077}
0.665 (+/-0.225) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0014454397707459273}
0.665 (+/-0.225) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0015848931924611123}
0.665 (+/-0.225) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0017378008287493752}
0.665 (+/-0.225) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001905460717963248}
0.660 (+/-0.219) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.002089296130854039}
0.660 (+/-0.219) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.002290867652767771}
0.660 (+/-0.219) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.601 (+/-0.159) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.632 (+/-0.279) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0010964781961431843}
0.636 (+/-0.280) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0012022644346174128}
0.636 (+/-0.280) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0013182567385564077}
0.643 (+/-0.285) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0014454397707459273}
0.647 (+/-0.285) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0015848931924611123}
0.625 (+/-0.184) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0017378008287493752}
0.629 (+/-0.185) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001905460717963248}
0.625 (+/-0.184) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.002089296130854039}
0.619 (+/-0.181) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.002290867652767771}
0.623 (+/-0.183) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.658 (+/-0.302) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.655 (+/-0.305) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0010964781961431843}
0.655 (+/-0.306) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0012022644346174128}
0.646 (+/-0.300) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0013182567385564077}
0.646 (+/-0.301) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0014454397707459273}
0.646 (+/-0.301) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0015848931924611123}
0.628 (+/-0.314) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0017378008287493752}
0.627 (+/-0.314) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001905460717963248}
0.626 (+/-0.314) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.002089296130854039}
0.626 (+/-0.314) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.002290867652767771}
0.625 (+/-0.314) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.626 (+/-0.313) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.626 (+/-0.313) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0010964781961431843}
0.628 (+/-0.312) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0012022644346174128}
0.628 (+/-0.312) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0013182567385564077}
0.628 (+/-0.312) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0014454397707459273}
0.629 (+/-0.311) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0015848931924611123}
0.627 (+/-0.312) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0017378008287493752}
0.628 (+/-0.312) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001905460717963248}
0.628 (+/-0.312) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.002089296130854039}
0.628 (+/-0.312) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.002290867652767771}
0.629 (+/-0.311) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.622 (+/-0.319) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.623 (+/-0.319) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0010964781961431843}
0.622 (+/-0.319) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0012022644346174128}
0.621 (+/-0.320) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0013182567385564077}
0.624 (+/-0.318) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0014454397707459273}
0.619 (+/-0.322) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0015848931924611123}
0.620 (+/-0.321) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0017378008287493752}
0.618 (+/-0.322) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001905460717963248}
0.620 (+/-0.320) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.002089296130854039}
0.618 (+/-0.323) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.002290867652767771}
0.618 (+/-0.322) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.617 (+/-0.323) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0010964781961431843}
0.620 (+/-0.321) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0012022644346174128}
0.617 (+/-0.324) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0013182567385564077}
0.620 (+/-0.320) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0014454397707459273}
0.614 (+/-0.327) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0015848931924611123}
0.618 (+/-0.322) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0017378008287493752}
0.614 (+/-0.327) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001905460717963248}
0.614 (+/-0.327) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.002089296130854039}
0.614 (+/-0.327) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.002290867652767771}
0.614 (+/-0.327) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.798 (+/-0.437) for {'C': 0.1, 'kernel': 'linear'}
0.643 (+/-0.202) for {'C': 1.0, 'kernel': 'linear'}
0.650 (+/-0.295) for {'C': 10.0, 'kernel': 'linear'}
0.626 (+/-0.314) for {'C': 100.0, 'kernel': 'linear'}
0.632 (+/-0.309) for {'C': 1000.0, 'kernel': 'linear'}
0.622 (+/-0.319) for {'C': 10000.0, 'kernel': 'linear'}
0.618 (+/-0.322) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0010964781961431843}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0012022644346174128}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0013182567385564077}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0014454397707459273}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0015848931924611123}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0017378008287493752}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001905460717963248}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.002089296130854039}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.002290867652767771}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0010964781961431843}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0012022644346174128}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0013182567385564077}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0014454397707459273}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0015848931924611123}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0017378008287493752}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001905460717963248}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.002089296130854039}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.002290867652767771}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0010964781961431843}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0012022644346174128}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0013182567385564077}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0014454397707459273}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0015848931924611123}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0017378008287493752}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001905460717963248}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.002089296130854039}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.002290867652767771}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0010964781961431843}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0012022644346174128}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0013182567385564077}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0014454397707459273}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0015848931924611123}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0017378008287493752}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001905460717963248}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.002089296130854039}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.002290867652767771}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0010964781961431843}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0012022644346174128}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0013182567385564077}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0014454397707459273}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0015848931924611123}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0017378008287493752}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001905460717963248}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.002089296130854039}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.002290867652767771}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.698 (+/-0.306) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.306) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0010964781961431843}
0.698 (+/-0.306) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0012022644346174128}
0.698 (+/-0.306) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0013182567385564077}
0.698 (+/-0.307) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0014454397707459273}
0.698 (+/-0.307) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0015848931924611123}
0.698 (+/-0.307) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0017378008287493752}
0.698 (+/-0.307) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001905460717963248}
0.698 (+/-0.306) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.002089296130854039}
0.698 (+/-0.306) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.002290867652767771}
0.698 (+/-0.306) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.696 (+/-0.303) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.303) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0010964781961431843}
0.697 (+/-0.303) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0012022644346174128}
0.697 (+/-0.303) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0013182567385564077}
0.697 (+/-0.304) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0014454397707459273}
0.697 (+/-0.305) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0015848931924611123}
0.697 (+/-0.305) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0017378008287493752}
0.697 (+/-0.305) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001905460717963248}
0.697 (+/-0.305) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.002089296130854039}
0.697 (+/-0.305) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.002290867652767771}
0.697 (+/-0.305) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.695 (+/-0.307) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.695 (+/-0.307) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0010964781961431843}
0.694 (+/-0.307) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0012022644346174128}
0.678 (+/-0.294) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0013182567385564077}
0.677 (+/-0.295) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0014454397707459273}
0.676 (+/-0.296) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0015848931924611123}
0.659 (+/-0.315) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0017378008287493752}
0.659 (+/-0.314) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001905460717963248}
0.659 (+/-0.313) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.002089296130854039}
0.659 (+/-0.313) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.002290867652767771}
0.659 (+/-0.312) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.660 (+/-0.313) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.659 (+/-0.313) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0010964781961431843}
0.660 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0012022644346174128}
0.660 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0013182567385564077}
0.660 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0014454397707459273}
0.660 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0015848931924611123}
0.660 (+/-0.313) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0017378008287493752}
0.660 (+/-0.313) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001905460717963248}
0.660 (+/-0.314) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.002089296130854039}
0.660 (+/-0.314) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.002290867652767771}
0.661 (+/-0.313) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.636 (+/-0.323) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.323) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0010964781961431843}
0.637 (+/-0.322) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0012022644346174128}
0.636 (+/-0.321) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0013182567385564077}
0.637 (+/-0.324) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0014454397707459273}
0.612 (+/-0.240) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0015848931924611123}
0.612 (+/-0.241) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0017378008287493752}
0.611 (+/-0.239) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001905460717963248}
0.612 (+/-0.240) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.002089296130854039}
0.611 (+/-0.238) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.002290867652767771}
0.611 (+/-0.239) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.611 (+/-0.237) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.587 (+/-0.234) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0010964781961431843}
0.636 (+/-0.318) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0012022644346174128}
0.610 (+/-0.238) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0013182567385564077}
0.612 (+/-0.240) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0014454397707459273}
0.587 (+/-0.234) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0015848931924611123}
0.612 (+/-0.239) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0017378008287493752}
0.587 (+/-0.234) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001905460717963248}
0.587 (+/-0.234) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.002089296130854039}
0.587 (+/-0.234) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.002290867652767771}
0.587 (+/-0.234) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.683 (+/-0.296) for {'C': 0.1, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 1.0, 'kernel': 'linear'}
0.696 (+/-0.304) for {'C': 10.0, 'kernel': 'linear'}
0.659 (+/-0.314) for {'C': 100.0, 'kernel': 'linear'}
0.661 (+/-0.314) for {'C': 1000.0, 'kernel': 'linear'}
0.636 (+/-0.322) for {'C': 10000.0, 'kernel': 'linear'}
0.611 (+/-0.239) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0012022644346174128}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6983958900156649
第0轮loop中，tunemodel函数得到的最优参数是： ([{'C': array([  10.        ,   15.84893192,   25.11886432,   39.81071706,
         63.09573445,  100.        ,  158.48931925,  251.18864315,
        398.10717055,  630.95734448, 1000.        ]), 'kernel': ['rbf'], 'gamma': array([0.00144544, 0.00147231, 0.00149968, 0.00152757, 0.00155597,
       0.00158489, 0.00161436, 0.00164437, 0.00167494, 0.00170608,
       0.0017378 ])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}], {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0015848931924611123}, 0.6990374309506142)
这是第0次迭代微调C和gamma。
第0次迭代，得到delta： [2.84217094e-14 1.95156391e-18]
SVC模型clf_op_iter的参数是： {'kernel': 'rbf', 'decision_function_shape': 'ovr', 'class_weight': {-1: 15}, 'tol': 0.001, 'gamma': 0.0015848931924611123, 'random_state': 0, 'cache_size': 200, 'verbose': False, 'degree': 3, 'C': 100.0, 'probability': False, 'shrinking': True, 'coef0': 0.0, 'max_iter': -1}

训练模型SVC对训练样本的预测准确率： 0.9355067328136074

测试集中，预测为舞弊样本的有： (array([ 370, 1246, 1247, 1248, 1251, 1252, 1255, 1256], dtype=int64),) 
测试集中，实际为舞弊样本的有： (array([1246, 1247, 1248, 1249, 1250, 1251, 1252, 1253, 1254, 1255, 1256],
      dtype=int64),) 
预测的舞弊样本数目： 8

训练模型SVC对测试样本的预测准确率： 0.9567682494684621
以上是第34次特征筛选。
第34次特征筛选，AUC值是： 0.8177805340726688
X_train_iter_svc.shape is: (1257, 18)
X_test_iter_svc.shape is: (1257, 18)
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.646 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.690 (+/-0.299) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.613 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.699 (+/-0.307) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.17      0.17      0.17         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6985566617198451
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.651 (+/-0.211) for {'C': 1.0, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.627 (+/-0.313) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.17      0.17      0.17         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.698 (+/-0.306) for {'C': 1.0, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.660 (+/-0.313) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.17      0.17      0.17         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6980753771951521
RBF的粗grid search最佳超参： {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001} RBF的粗grid search最高分值： 0.6985566617198451
linear的粗grid search最佳超参： {'C': 1.0, 'kernel': 'linear'} linear的粗grid search最高分值： 0.6980753771951521
粗grid search得到的parameter是：
 [{'C': array([1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03, 1.e+04, 1.e+05,
       1.e+06, 1.e+07, 1.e+08]), 'kernel': ['rbf'], 'gamma': array([1.e-08, 1.e-07, 1.e-06, 1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01,
       1.e+00, 1.e+01, 1.e+02])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.848 (+/-0.459) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.848 (+/-0.459) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.715 (+/-0.352) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.631 (+/-0.319) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.848 (+/-0.459) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.690 (+/-0.299) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.715 (+/-0.352) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.646 (+/-0.388) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.848 (+/-0.459) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.690 (+/-0.299) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.310) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.646 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.848 (+/-0.459) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.690 (+/-0.299) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.698 (+/-0.310) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.638 (+/-0.315) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.620 (+/-0.321) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.646 (+/-0.388) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.848 (+/-0.459) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.665 (+/-0.225) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.715 (+/-0.352) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.638 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.619 (+/-0.322) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.620 (+/-0.320) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.646 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.773 (+/-0.423) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.710 (+/-0.351) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.672 (+/-0.295) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.635 (+/-0.313) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.622 (+/-0.319) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.620 (+/-0.320) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.646 (+/-0.388) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.848 (+/-0.460) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.777 (+/-0.463) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.650 (+/-0.361) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.632 (+/-0.310) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.629 (+/-0.311) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.619 (+/-0.321) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.620 (+/-0.320) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.646 (+/-0.388) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.755 (+/-0.427) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.723 (+/-0.471) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.648 (+/-0.296) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.627 (+/-0.313) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.622 (+/-0.319) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.614 (+/-0.327) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.620 (+/-0.320) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.646 (+/-0.388) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.690 (+/-0.300) for {'C': 1.0, 'kernel': 'linear'}
0.673 (+/-0.294) for {'C': 10.0, 'kernel': 'linear'}
0.648 (+/-0.315) for {'C': 100.0, 'kernel': 'linear'}
0.627 (+/-0.313) for {'C': 1000.0, 'kernel': 'linear'}
0.622 (+/-0.319) for {'C': 10000.0, 'kernel': 'linear'}
0.621 (+/-0.320) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      1.00       623

avg / total       0.98      0.99      0.99       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.658 (+/-0.217) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.002) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.658 (+/-0.217) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.699 (+/-0.307) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.236) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.007) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.658 (+/-0.217) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.699 (+/-0.307) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.699 (+/-0.308) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.612 (+/-0.241) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.009) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.658 (+/-0.217) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.699 (+/-0.307) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.699 (+/-0.307) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.587 (+/-0.234) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.009) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.658 (+/-0.217) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.699 (+/-0.307) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.699 (+/-0.308) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.662 (+/-0.316) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.241) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.009) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.658 (+/-0.217) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.698 (+/-0.307) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.699 (+/-0.308) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.662 (+/-0.316) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.610 (+/-0.242) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.009) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.699 (+/-0.309) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.699 (+/-0.306) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.698 (+/-0.306) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.661 (+/-0.316) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.635 (+/-0.324) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.587 (+/-0.234) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.239) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.009) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.683 (+/-0.296) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.679 (+/-0.282) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.696 (+/-0.302) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.662 (+/-0.313) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.659 (+/-0.316) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.611 (+/-0.241) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.587 (+/-0.234) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.239) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.009) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.699 (+/-0.306) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.646 (+/-0.247) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.696 (+/-0.302) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.661 (+/-0.312) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.634 (+/-0.328) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.586 (+/-0.235) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.587 (+/-0.234) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.239) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.009) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.699 (+/-0.307) for {'C': 1.0, 'kernel': 'linear'}
0.698 (+/-0.307) for {'C': 10.0, 'kernel': 'linear'}
0.664 (+/-0.315) for {'C': 100.0, 'kernel': 'linear'}
0.660 (+/-0.312) for {'C': 1000.0, 'kernel': 'linear'}
0.636 (+/-0.323) for {'C': 10000.0, 'kernel': 'linear'}
0.635 (+/-0.323) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6993584590650507
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.602 (+/-0.197) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.671 (+/-0.291) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.379) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.680 (+/-0.295) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.647 (+/-0.285) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.619 (+/-0.322) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.680 (+/-0.295) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.645 (+/-0.286) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.304) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.646 (+/-0.388) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.680 (+/-0.295) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.645 (+/-0.286) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.658 (+/-0.302) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.646 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.680 (+/-0.295) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.645 (+/-0.286) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.657 (+/-0.303) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.626 (+/-0.314) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.620 (+/-0.321) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.646 (+/-0.388) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.747 (+/-0.502) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.667 (+/-0.292) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.620 (+/-0.184) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.656 (+/-0.304) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.626 (+/-0.314) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.619 (+/-0.322) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.620 (+/-0.320) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.646 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.647 (+/-0.460) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.668 (+/-0.291) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.662 (+/-0.357) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.655 (+/-0.305) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.628 (+/-0.313) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.622 (+/-0.319) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.620 (+/-0.320) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.646 (+/-0.388) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.848 (+/-0.460) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.547 (+/-0.141) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.602 (+/-0.280) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.625 (+/-0.315) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.629 (+/-0.311) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.619 (+/-0.321) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.620 (+/-0.320) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.646 (+/-0.388) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.706 (+/-0.418) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.556 (+/-0.294) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.626 (+/-0.296) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.624 (+/-0.315) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.622 (+/-0.319) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.614 (+/-0.327) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.620 (+/-0.320) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.646 (+/-0.388) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.798 (+/-0.437) for {'C': 0.1, 'kernel': 'linear'}
0.651 (+/-0.211) for {'C': 1.0, 'kernel': 'linear'}
0.641 (+/-0.288) for {'C': 10.0, 'kernel': 'linear'}
0.626 (+/-0.314) for {'C': 100.0, 'kernel': 'linear'}
0.627 (+/-0.313) for {'C': 1000.0, 'kernel': 'linear'}
0.622 (+/-0.319) for {'C': 10000.0, 'kernel': 'linear'}
0.621 (+/-0.320) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.314) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.698 (+/-0.306) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.011) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.306) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.304) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.611 (+/-0.240) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.007) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.698 (+/-0.306) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.303) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.695 (+/-0.308) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.612 (+/-0.241) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.009) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.698 (+/-0.306) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.303) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.695 (+/-0.307) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.585 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.009) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.698 (+/-0.306) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.303) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.695 (+/-0.307) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.659 (+/-0.314) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.241) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.009) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.617 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.698 (+/-0.306) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.697 (+/-0.303) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.695 (+/-0.307) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.659 (+/-0.313) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.610 (+/-0.242) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.009) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.575 (+/-0.229) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.698 (+/-0.308) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.696 (+/-0.300) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.695 (+/-0.307) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.659 (+/-0.316) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.635 (+/-0.324) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.587 (+/-0.234) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.239) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.009) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.700 (+/-0.309) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.698 (+/-0.254) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.692 (+/-0.291) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.658 (+/-0.315) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.659 (+/-0.316) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.611 (+/-0.241) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.587 (+/-0.234) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.239) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.009) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.674 (+/-0.325) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.647 (+/-0.213) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.692 (+/-0.291) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.657 (+/-0.315) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.634 (+/-0.328) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.586 (+/-0.235) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.587 (+/-0.234) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.239) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.009) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.700 (+/-0.309) for {'C': 0.1, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 1.0, 'kernel': 'linear'}
0.696 (+/-0.304) for {'C': 10.0, 'kernel': 'linear'}
0.658 (+/-0.315) for {'C': 100.0, 'kernel': 'linear'}
0.660 (+/-0.313) for {'C': 1000.0, 'kernel': 'linear'}
0.636 (+/-0.323) for {'C': 10000.0, 'kernel': 'linear'}
0.635 (+/-0.323) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.7
发现最优参数gamma为原先的最大/最小值，直接重新设置超参。
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-09}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-08}
0.773 (+/-0.423) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-07}
0.710 (+/-0.351) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-06}
0.672 (+/-0.295) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-05}
0.635 (+/-0.313) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.0001}
0.622 (+/-0.319) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-09}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-08}
0.723 (+/-0.359) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-07}
0.665 (+/-0.290) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-06}
0.666 (+/-0.295) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-05}
0.635 (+/-0.312) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.0001}
0.622 (+/-0.319) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-09}
0.747 (+/-0.502) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-08}
0.748 (+/-0.389) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-07}
0.662 (+/-0.290) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-06}
0.655 (+/-0.295) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-05}
0.634 (+/-0.311) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.0001}
0.618 (+/-0.322) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-09}
0.747 (+/-0.502) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-08}
0.773 (+/-0.424) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-07}
0.675 (+/-0.358) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-06}
0.636 (+/-0.309) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-05}
0.628 (+/-0.312) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 0.0001}
0.618 (+/-0.323) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-09}
0.797 (+/-0.492) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-08}
0.773 (+/-0.417) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-07}
0.660 (+/-0.362) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-06}
0.633 (+/-0.310) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-05}
0.627 (+/-0.313) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.323) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-09}
0.848 (+/-0.460) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-08}
0.726 (+/-0.467) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-07}
0.650 (+/-0.361) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-06}
0.632 (+/-0.310) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-05}
0.629 (+/-0.311) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 0.0001}
0.619 (+/-0.321) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-09}
0.823 (+/-0.451) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-08}
0.749 (+/-0.498) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-07}
0.657 (+/-0.359) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-06}
0.630 (+/-0.311) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-05}
0.628 (+/-0.312) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 0.0001}
0.619 (+/-0.322) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-10}
0.747 (+/-0.502) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-09}
0.806 (+/-0.436) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-08}
0.749 (+/-0.499) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-07}
0.638 (+/-0.291) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-06}
0.629 (+/-0.312) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-05}
0.622 (+/-0.319) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.0001}
0.614 (+/-0.327) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-10}
0.747 (+/-0.502) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-09}
0.765 (+/-0.421) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-08}
0.748 (+/-0.499) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-07}
0.647 (+/-0.297) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-06}
0.627 (+/-0.313) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-05}
0.622 (+/-0.319) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 0.0001}
0.614 (+/-0.327) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-10}
0.797 (+/-0.492) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-09}
0.760 (+/-0.423) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-08}
0.723 (+/-0.471) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-07}
0.649 (+/-0.296) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-06}
0.628 (+/-0.313) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-05}
0.622 (+/-0.319) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 0.0001}
0.614 (+/-0.327) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-10}
0.848 (+/-0.460) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-09}
0.755 (+/-0.427) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-08}
0.723 (+/-0.471) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-07}
0.648 (+/-0.296) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-06}
0.627 (+/-0.313) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-05}
0.622 (+/-0.319) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 0.0001}
0.614 (+/-0.327) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.690 (+/-0.300) for {'C': 1.0, 'kernel': 'linear'}
0.673 (+/-0.294) for {'C': 10.0, 'kernel': 'linear'}
0.648 (+/-0.315) for {'C': 100.0, 'kernel': 'linear'}
0.627 (+/-0.313) for {'C': 1000.0, 'kernel': 'linear'}
0.622 (+/-0.319) for {'C': 10000.0, 'kernel': 'linear'}
0.621 (+/-0.320) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      1.00       623

avg / total       0.98      0.99      0.99       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-09}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-08}
0.699 (+/-0.309) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-07}
0.699 (+/-0.306) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-06}
0.698 (+/-0.306) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-05}
0.661 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.0001}
0.635 (+/-0.324) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-09}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-08}
0.699 (+/-0.308) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-07}
0.698 (+/-0.306) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-06}
0.697 (+/-0.306) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-05}
0.661 (+/-0.317) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.0001}
0.636 (+/-0.324) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-09}
0.617 (+/-0.238) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-08}
0.699 (+/-0.308) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-07}
0.698 (+/-0.305) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-06}
0.680 (+/-0.293) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-05}
0.661 (+/-0.318) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.0001}
0.610 (+/-0.241) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-09}
0.617 (+/-0.238) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-08}
0.699 (+/-0.309) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-07}
0.697 (+/-0.304) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-06}
0.663 (+/-0.314) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-05}
0.660 (+/-0.316) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 0.0001}
0.610 (+/-0.240) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-09}
0.642 (+/-0.236) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-08}
0.699 (+/-0.308) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-07}
0.696 (+/-0.303) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-06}
0.662 (+/-0.313) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-05}
0.659 (+/-0.315) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 0.0001}
0.610 (+/-0.240) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-09}
0.683 (+/-0.296) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-08}
0.662 (+/-0.299) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-07}
0.696 (+/-0.302) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-06}
0.662 (+/-0.313) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-05}
0.659 (+/-0.316) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 0.0001}
0.611 (+/-0.241) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-09}
0.683 (+/-0.296) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-08}
0.655 (+/-0.274) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-07}
0.696 (+/-0.302) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-06}
0.662 (+/-0.313) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-05}
0.659 (+/-0.315) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 0.0001}
0.611 (+/-0.242) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-10}
0.617 (+/-0.238) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-09}
0.700 (+/-0.308) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-08}
0.651 (+/-0.260) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-07}
0.696 (+/-0.301) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-06}
0.661 (+/-0.313) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-05}
0.634 (+/-0.327) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.0001}
0.586 (+/-0.236) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-10}
0.617 (+/-0.238) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-09}
0.699 (+/-0.307) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-08}
0.649 (+/-0.254) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-07}
0.696 (+/-0.302) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-06}
0.661 (+/-0.312) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-05}
0.634 (+/-0.327) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 0.0001}
0.586 (+/-0.235) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-10}
0.642 (+/-0.236) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-09}
0.699 (+/-0.307) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-08}
0.646 (+/-0.249) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-07}
0.696 (+/-0.302) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-06}
0.661 (+/-0.313) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-05}
0.634 (+/-0.327) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 0.0001}
0.586 (+/-0.235) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-10}
0.683 (+/-0.296) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-09}
0.699 (+/-0.306) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-08}
0.646 (+/-0.247) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-07}
0.696 (+/-0.302) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-06}
0.661 (+/-0.312) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-05}
0.634 (+/-0.328) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 0.0001}
0.586 (+/-0.235) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.699 (+/-0.307) for {'C': 1.0, 'kernel': 'linear'}
0.698 (+/-0.307) for {'C': 10.0, 'kernel': 'linear'}
0.664 (+/-0.315) for {'C': 100.0, 'kernel': 'linear'}
0.660 (+/-0.312) for {'C': 1000.0, 'kernel': 'linear'}
0.636 (+/-0.323) for {'C': 10000.0, 'kernel': 'linear'}
0.635 (+/-0.323) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-08}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6995187154753071
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-09}
0.647 (+/-0.460) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-08}
0.668 (+/-0.291) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-07}
0.662 (+/-0.357) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-06}
0.655 (+/-0.305) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-05}
0.628 (+/-0.313) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.0001}
0.622 (+/-0.319) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-09}
0.747 (+/-0.502) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-08}
0.669 (+/-0.296) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-07}
0.592 (+/-0.176) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-06}
0.652 (+/-0.309) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-05}
0.628 (+/-0.313) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.0001}
0.622 (+/-0.319) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-09}
0.747 (+/-0.502) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-08}
0.694 (+/-0.355) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-07}
0.572 (+/-0.144) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-06}
0.641 (+/-0.305) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-05}
0.628 (+/-0.312) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.0001}
0.618 (+/-0.322) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-09}
0.848 (+/-0.459) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-08}
0.625 (+/-0.292) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-07}
0.603 (+/-0.282) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-06}
0.624 (+/-0.316) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-05}
0.628 (+/-0.312) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 0.0001}
0.618 (+/-0.323) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-09}
0.848 (+/-0.460) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-08}
0.576 (+/-0.180) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-07}
0.599 (+/-0.280) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-06}
0.624 (+/-0.315) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-05}
0.627 (+/-0.313) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.323) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-10}
0.697 (+/-0.492) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-09}
0.848 (+/-0.460) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-08}
0.547 (+/-0.141) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-07}
0.602 (+/-0.280) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-06}
0.625 (+/-0.315) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-05}
0.629 (+/-0.311) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 0.0001}
0.619 (+/-0.321) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-10}
0.747 (+/-0.502) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-09}
0.806 (+/-0.436) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-08}
0.565 (+/-0.290) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-07}
0.610 (+/-0.282) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-06}
0.624 (+/-0.315) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-05}
0.628 (+/-0.312) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 0.0001}
0.619 (+/-0.322) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-10}
0.747 (+/-0.502) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-09}
0.748 (+/-0.447) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-08}
0.561 (+/-0.292) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-07}
0.616 (+/-0.288) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-06}
0.624 (+/-0.315) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-05}
0.622 (+/-0.319) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.0001}
0.614 (+/-0.327) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-10}
0.848 (+/-0.459) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-09}
0.710 (+/-0.415) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-08}
0.559 (+/-0.293) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-07}
0.625 (+/-0.296) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-06}
0.624 (+/-0.315) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-05}
0.622 (+/-0.319) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 0.0001}
0.614 (+/-0.327) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-10}
0.848 (+/-0.460) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-09}
0.706 (+/-0.418) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-08}
0.556 (+/-0.294) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-07}
0.626 (+/-0.296) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-06}
0.624 (+/-0.315) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-05}
0.622 (+/-0.319) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 0.0001}
0.614 (+/-0.327) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-11}
0.697 (+/-0.492) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-10}
0.848 (+/-0.460) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-09}
0.706 (+/-0.418) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-08}
0.556 (+/-0.294) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-07}
0.626 (+/-0.296) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-06}
0.624 (+/-0.315) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-05}
0.622 (+/-0.319) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 0.0001}
0.614 (+/-0.327) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.798 (+/-0.437) for {'C': 0.1, 'kernel': 'linear'}
0.651 (+/-0.211) for {'C': 1.0, 'kernel': 'linear'}
0.641 (+/-0.288) for {'C': 10.0, 'kernel': 'linear'}
0.626 (+/-0.314) for {'C': 100.0, 'kernel': 'linear'}
0.627 (+/-0.313) for {'C': 1000.0, 'kernel': 'linear'}
0.622 (+/-0.319) for {'C': 10000.0, 'kernel': 'linear'}
0.621 (+/-0.320) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-09}
0.575 (+/-0.229) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-08}
0.698 (+/-0.308) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-07}
0.696 (+/-0.300) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-06}
0.695 (+/-0.307) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-05}
0.659 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.0001}
0.635 (+/-0.324) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-09}
0.617 (+/-0.238) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-08}
0.698 (+/-0.309) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-07}
0.694 (+/-0.296) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-06}
0.692 (+/-0.309) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-05}
0.660 (+/-0.315) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.0001}
0.636 (+/-0.324) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-09}
0.617 (+/-0.238) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-08}
0.698 (+/-0.310) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-07}
0.692 (+/-0.293) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-06}
0.674 (+/-0.293) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-05}
0.660 (+/-0.315) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.0001}
0.610 (+/-0.241) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-09}
0.658 (+/-0.217) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-08}
0.695 (+/-0.305) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-07}
0.692 (+/-0.292) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-06}
0.658 (+/-0.313) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-05}
0.660 (+/-0.316) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 0.0001}
0.610 (+/-0.240) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-09}
0.683 (+/-0.296) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-08}
0.688 (+/-0.301) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-07}
0.691 (+/-0.290) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-06}
0.658 (+/-0.313) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-05}
0.659 (+/-0.315) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 0.0001}
0.610 (+/-0.240) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-10}
0.600 (+/-0.245) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-09}
0.700 (+/-0.309) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-08}
0.698 (+/-0.254) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-07}
0.692 (+/-0.291) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-06}
0.658 (+/-0.315) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-05}
0.659 (+/-0.316) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 0.0001}
0.611 (+/-0.241) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-10}
0.617 (+/-0.238) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-09}
0.700 (+/-0.308) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-08}
0.700 (+/-0.260) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-07}
0.692 (+/-0.290) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-06}
0.657 (+/-0.314) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-05}
0.659 (+/-0.315) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 0.0001}
0.611 (+/-0.242) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-10}
0.617 (+/-0.238) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-09}
0.675 (+/-0.325) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-08}
0.679 (+/-0.238) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-07}
0.692 (+/-0.289) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-06}
0.657 (+/-0.315) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-05}
0.634 (+/-0.327) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.0001}
0.586 (+/-0.236) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-10}
0.658 (+/-0.217) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-09}
0.674 (+/-0.325) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-08}
0.665 (+/-0.221) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-07}
0.692 (+/-0.290) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-06}
0.657 (+/-0.316) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-05}
0.634 (+/-0.327) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 0.0001}
0.586 (+/-0.235) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-10}
0.683 (+/-0.296) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-09}
0.674 (+/-0.325) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-08}
0.653 (+/-0.215) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-07}
0.692 (+/-0.290) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-06}
0.657 (+/-0.315) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-05}
0.634 (+/-0.327) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 0.0001}
0.586 (+/-0.235) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-11}
0.600 (+/-0.245) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-10}
0.700 (+/-0.309) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-09}
0.674 (+/-0.325) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-08}
0.647 (+/-0.213) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-07}
0.692 (+/-0.291) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-06}
0.657 (+/-0.315) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-05}
0.634 (+/-0.328) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 0.0001}
0.586 (+/-0.235) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.700 (+/-0.309) for {'C': 0.1, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 1.0, 'kernel': 'linear'}
0.696 (+/-0.304) for {'C': 10.0, 'kernel': 'linear'}
0.658 (+/-0.315) for {'C': 100.0, 'kernel': 'linear'}
0.660 (+/-0.313) for {'C': 1000.0, 'kernel': 'linear'}
0.636 (+/-0.323) for {'C': 10000.0, 'kernel': 'linear'}
0.635 (+/-0.323) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-08}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.7
循环迭代之前，delta is: [5.58793545e-09 0.00000000e+00]
SVC模型clf_op_iter的参数是： {'kernel': 'rbf', 'decision_function_shape': 'ovr', 'class_weight': {-1: 15}, 'tol': 0.001, 'gamma': 1e-08, 'random_state': 0, 'cache_size': 200, 'verbose': False, 'degree': 3, 'C': 9999999.999999994, 'probability': False, 'shrinking': True, 'coef0': 0.0, 'max_iter': -1}

训练模型SVC对训练样本的预测准确率： 0.8936924167257264

测试集中，预测为舞弊样本的有： (array([1248], dtype=int64),) 
测试集中，实际为舞弊样本的有： (array([1246, 1247, 1248, 1249, 1250, 1251, 1252, 1253, 1254, 1255, 1256],
      dtype=int64),) 
预测的舞弊样本数目： 1

训练模型SVC对测试样本的预测准确率： 0.8936924167257264
以上是第35次特征筛选。
第35次特征筛选，AUC值是： 0.5454545454545454
X_train_iter_svc.shape is: (1257, 17)
X_test_iter_svc.shape is: (1257, 17)
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.621 (+/-0.320) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.715 (+/-0.352) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.613 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.699 (+/-0.307) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.17      0.17      0.17         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6987169181301014
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.651 (+/-0.211) for {'C': 1.0, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.629 (+/-0.312) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.17      0.17      0.17         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.698 (+/-0.306) for {'C': 1.0, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.659 (+/-0.316) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.17      0.17      0.17         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6980753771951521
RBF的粗grid search最佳超参： {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001} RBF的粗grid search最高分值： 0.6987169181301014
linear的粗grid search最佳超参： {'C': 1.0, 'kernel': 'linear'} linear的粗grid search最高分值： 0.6980753771951521
粗grid search得到的parameter是：
 [{'C': array([1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03, 1.e+04, 1.e+05,
       1.e+06, 1.e+07, 1.e+08]), 'kernel': ['rbf'], 'gamma': array([1.e-08, 1.e-07, 1.e-06, 1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01,
       1.e+00, 1.e+01, 1.e+02])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.848 (+/-0.459) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.848 (+/-0.459) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.715 (+/-0.352) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.622 (+/-0.310) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.848 (+/-0.459) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.715 (+/-0.352) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.698 (+/-0.310) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.620 (+/-0.321) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.848 (+/-0.459) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.715 (+/-0.352) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.690 (+/-0.299) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.621 (+/-0.320) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.848 (+/-0.459) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.715 (+/-0.352) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.690 (+/-0.299) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.643 (+/-0.320) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.619 (+/-0.321) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.621 (+/-0.320) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.848 (+/-0.459) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.690 (+/-0.300) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.698 (+/-0.310) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.638 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.619 (+/-0.322) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.619 (+/-0.321) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.621 (+/-0.320) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.773 (+/-0.423) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.685 (+/-0.297) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.677 (+/-0.299) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.638 (+/-0.315) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.621 (+/-0.320) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.619 (+/-0.321) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.621 (+/-0.320) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.848 (+/-0.460) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.773 (+/-0.417) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.646 (+/-0.364) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.634 (+/-0.309) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.630 (+/-0.312) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.324) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.619 (+/-0.321) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.621 (+/-0.320) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.793 (+/-0.440) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.687 (+/-0.435) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.626 (+/-0.292) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.629 (+/-0.312) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.622 (+/-0.319) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.618 (+/-0.322) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.619 (+/-0.321) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.621 (+/-0.320) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.690 (+/-0.300) for {'C': 1.0, 'kernel': 'linear'}
0.659 (+/-0.287) for {'C': 10.0, 'kernel': 'linear'}
0.656 (+/-0.330) for {'C': 100.0, 'kernel': 'linear'}
0.633 (+/-0.312) for {'C': 1000.0, 'kernel': 'linear'}
0.619 (+/-0.321) for {'C': 10000.0, 'kernel': 'linear'}
0.620 (+/-0.321) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      1.00       623

avg / total       0.98      0.99      0.99       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.658 (+/-0.217) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.002) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.658 (+/-0.217) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.699 (+/-0.307) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.236) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.008) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.658 (+/-0.217) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.699 (+/-0.307) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.699 (+/-0.307) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.611 (+/-0.241) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.008) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.658 (+/-0.217) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.699 (+/-0.307) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.699 (+/-0.307) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.587 (+/-0.234) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.008) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.658 (+/-0.217) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.699 (+/-0.307) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.698 (+/-0.307) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.662 (+/-0.317) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.008) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.658 (+/-0.217) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.699 (+/-0.307) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.699 (+/-0.308) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.662 (+/-0.316) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.610 (+/-0.242) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.008) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.699 (+/-0.309) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.698 (+/-0.306) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.698 (+/-0.307) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.662 (+/-0.316) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.635 (+/-0.323) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.586 (+/-0.236) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.238) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.238) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.008) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.683 (+/-0.296) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.699 (+/-0.308) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.695 (+/-0.297) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.662 (+/-0.314) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.659 (+/-0.317) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.610 (+/-0.238) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.586 (+/-0.236) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.238) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.238) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.008) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.699 (+/-0.307) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.656 (+/-0.279) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.694 (+/-0.296) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.661 (+/-0.314) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.633 (+/-0.328) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.611 (+/-0.240) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.586 (+/-0.236) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.238) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.238) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.008) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.699 (+/-0.307) for {'C': 1.0, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 10.0, 'kernel': 'linear'}
0.664 (+/-0.316) for {'C': 100.0, 'kernel': 'linear'}
0.661 (+/-0.317) for {'C': 1000.0, 'kernel': 'linear'}
0.634 (+/-0.319) for {'C': 10000.0, 'kernel': 'linear'}
0.635 (+/-0.321) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6993584590650507
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.627 (+/-0.227) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.671 (+/-0.291) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.622 (+/-0.317) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.680 (+/-0.295) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.658 (+/-0.290) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.610 (+/-0.312) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.680 (+/-0.295) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.651 (+/-0.286) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.655 (+/-0.305) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.620 (+/-0.321) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.680 (+/-0.295) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.651 (+/-0.286) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.655 (+/-0.305) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.618 (+/-0.322) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.621 (+/-0.320) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.680 (+/-0.295) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.651 (+/-0.286) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.656 (+/-0.303) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.625 (+/-0.314) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.619 (+/-0.321) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.621 (+/-0.320) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.747 (+/-0.502) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.667 (+/-0.292) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.620 (+/-0.186) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.657 (+/-0.303) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.625 (+/-0.314) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.618 (+/-0.323) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.619 (+/-0.321) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.621 (+/-0.320) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.647 (+/-0.460) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.668 (+/-0.291) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.623 (+/-0.289) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.655 (+/-0.304) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.626 (+/-0.314) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.620 (+/-0.321) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.619 (+/-0.321) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.621 (+/-0.320) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.848 (+/-0.460) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.543 (+/-0.142) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.603 (+/-0.280) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.624 (+/-0.315) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.626 (+/-0.314) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.324) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.619 (+/-0.321) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.621 (+/-0.320) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.793 (+/-0.440) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.557 (+/-0.294) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.610 (+/-0.283) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.624 (+/-0.315) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.621 (+/-0.320) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.618 (+/-0.322) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.619 (+/-0.321) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.621 (+/-0.320) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.798 (+/-0.437) for {'C': 0.1, 'kernel': 'linear'}
0.651 (+/-0.211) for {'C': 1.0, 'kernel': 'linear'}
0.632 (+/-0.282) for {'C': 10.0, 'kernel': 'linear'}
0.626 (+/-0.314) for {'C': 100.0, 'kernel': 'linear'}
0.629 (+/-0.312) for {'C': 1000.0, 'kernel': 'linear'}
0.619 (+/-0.321) for {'C': 10000.0, 'kernel': 'linear'}
0.620 (+/-0.321) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.665 (+/-0.314) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.698 (+/-0.306) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.495 (+/-0.011) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.306) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.698 (+/-0.305) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.611 (+/-0.240) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.008) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.698 (+/-0.306) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.304) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.695 (+/-0.307) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.611 (+/-0.241) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.008) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.698 (+/-0.306) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.304) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.695 (+/-0.307) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.609 (+/-0.243) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.008) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.698 (+/-0.306) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.304) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.695 (+/-0.307) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.658 (+/-0.313) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.008) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.617 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.698 (+/-0.306) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.697 (+/-0.304) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.695 (+/-0.307) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.658 (+/-0.313) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.610 (+/-0.241) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.008) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.575 (+/-0.229) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.698 (+/-0.308) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.695 (+/-0.299) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.695 (+/-0.307) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.658 (+/-0.315) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.634 (+/-0.322) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.586 (+/-0.236) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.238) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.238) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.008) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.700 (+/-0.309) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.672 (+/-0.283) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.691 (+/-0.287) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.657 (+/-0.316) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.658 (+/-0.315) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.610 (+/-0.238) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.586 (+/-0.236) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.238) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.238) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.008) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.699 (+/-0.307) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.648 (+/-0.236) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.691 (+/-0.288) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.657 (+/-0.316) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.633 (+/-0.327) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.611 (+/-0.240) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.586 (+/-0.236) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.238) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.238) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.008) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.700 (+/-0.309) for {'C': 0.1, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 1.0, 'kernel': 'linear'}
0.696 (+/-0.303) for {'C': 10.0, 'kernel': 'linear'}
0.658 (+/-0.315) for {'C': 100.0, 'kernel': 'linear'}
0.659 (+/-0.316) for {'C': 1000.0, 'kernel': 'linear'}
0.634 (+/-0.319) for {'C': 10000.0, 'kernel': 'linear'}
0.635 (+/-0.321) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.7
发现最优参数gamma为原先的最大/最小值，直接重新设置超参。
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-09}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-08}
0.773 (+/-0.423) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-07}
0.685 (+/-0.297) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-06}
0.677 (+/-0.299) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-05}
0.638 (+/-0.315) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.0001}
0.627 (+/-0.312) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-09}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-08}
0.748 (+/-0.389) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-07}
0.687 (+/-0.351) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-06}
0.672 (+/-0.295) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-05}
0.633 (+/-0.312) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.0001}
0.619 (+/-0.321) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-09}
0.747 (+/-0.502) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-08}
0.748 (+/-0.396) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-07}
0.680 (+/-0.350) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-06}
0.656 (+/-0.293) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-05}
0.634 (+/-0.311) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.0001}
0.620 (+/-0.321) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-09}
0.747 (+/-0.502) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-08}
0.740 (+/-0.392) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-07}
0.638 (+/-0.287) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-06}
0.636 (+/-0.309) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-05}
0.633 (+/-0.312) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 0.0001}
0.619 (+/-0.321) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-09}
0.797 (+/-0.492) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-08}
0.760 (+/-0.427) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-07}
0.652 (+/-0.365) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-06}
0.636 (+/-0.309) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-05}
0.630 (+/-0.312) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.324) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-09}
0.848 (+/-0.460) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-08}
0.773 (+/-0.417) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-07}
0.646 (+/-0.364) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-06}
0.634 (+/-0.309) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-05}
0.630 (+/-0.312) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.324) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-09}
0.823 (+/-0.451) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-08}
0.787 (+/-0.446) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-07}
0.652 (+/-0.363) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-06}
0.631 (+/-0.311) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-05}
0.625 (+/-0.318) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.323) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-10}
0.747 (+/-0.502) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-09}
0.806 (+/-0.436) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-08}
0.703 (+/-0.429) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-07}
0.650 (+/-0.364) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-06}
0.631 (+/-0.311) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-05}
0.625 (+/-0.318) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.0001}
0.618 (+/-0.322) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-10}
0.747 (+/-0.502) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-09}
0.798 (+/-0.437) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-08}
0.712 (+/-0.472) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-07}
0.652 (+/-0.363) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-06}
0.629 (+/-0.312) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-05}
0.625 (+/-0.318) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 0.0001}
0.618 (+/-0.322) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-10}
0.797 (+/-0.492) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-09}
0.793 (+/-0.440) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-08}
0.687 (+/-0.434) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-07}
0.627 (+/-0.291) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-06}
0.629 (+/-0.312) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-05}
0.624 (+/-0.318) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 0.0001}
0.618 (+/-0.322) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-10}
0.848 (+/-0.460) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-09}
0.793 (+/-0.440) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-08}
0.687 (+/-0.435) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-07}
0.626 (+/-0.292) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-06}
0.629 (+/-0.312) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-05}
0.622 (+/-0.319) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 0.0001}
0.618 (+/-0.322) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.690 (+/-0.300) for {'C': 1.0, 'kernel': 'linear'}
0.659 (+/-0.287) for {'C': 10.0, 'kernel': 'linear'}
0.656 (+/-0.330) for {'C': 100.0, 'kernel': 'linear'}
0.633 (+/-0.312) for {'C': 1000.0, 'kernel': 'linear'}
0.619 (+/-0.321) for {'C': 10000.0, 'kernel': 'linear'}
0.620 (+/-0.321) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      1.00       623

avg / total       0.98      0.99      0.99       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-09}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-08}
0.699 (+/-0.309) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-07}
0.698 (+/-0.306) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-06}
0.698 (+/-0.307) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-05}
0.662 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.0001}
0.660 (+/-0.313) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-09}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-08}
0.699 (+/-0.308) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-07}
0.698 (+/-0.305) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-06}
0.698 (+/-0.307) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-05}
0.661 (+/-0.315) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.0001}
0.634 (+/-0.320) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-09}
0.617 (+/-0.238) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-08}
0.699 (+/-0.309) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-07}
0.698 (+/-0.304) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-06}
0.680 (+/-0.294) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-05}
0.661 (+/-0.317) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.0001}
0.634 (+/-0.320) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-09}
0.617 (+/-0.238) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-08}
0.699 (+/-0.308) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-07}
0.696 (+/-0.301) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-06}
0.663 (+/-0.313) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-05}
0.660 (+/-0.317) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 0.0001}
0.634 (+/-0.319) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-09}
0.642 (+/-0.236) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-08}
0.699 (+/-0.308) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-07}
0.695 (+/-0.299) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-06}
0.663 (+/-0.314) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-05}
0.659 (+/-0.317) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 0.0001}
0.610 (+/-0.239) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-09}
0.683 (+/-0.296) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-08}
0.699 (+/-0.308) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-07}
0.695 (+/-0.297) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-06}
0.662 (+/-0.314) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-05}
0.659 (+/-0.317) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 0.0001}
0.610 (+/-0.238) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-09}
0.683 (+/-0.296) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-08}
0.682 (+/-0.294) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-07}
0.695 (+/-0.297) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-06}
0.662 (+/-0.314) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-05}
0.634 (+/-0.329) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 0.0001}
0.611 (+/-0.239) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-10}
0.617 (+/-0.238) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-09}
0.700 (+/-0.308) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-08}
0.663 (+/-0.305) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-07}
0.695 (+/-0.298) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-06}
0.661 (+/-0.315) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-05}
0.634 (+/-0.330) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.0001}
0.611 (+/-0.240) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-10}
0.617 (+/-0.238) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-09}
0.699 (+/-0.307) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-08}
0.659 (+/-0.290) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-07}
0.695 (+/-0.298) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-06}
0.661 (+/-0.314) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-05}
0.634 (+/-0.330) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 0.0001}
0.611 (+/-0.239) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-10}
0.642 (+/-0.236) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-09}
0.699 (+/-0.307) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-08}
0.657 (+/-0.283) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-07}
0.694 (+/-0.298) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-06}
0.661 (+/-0.314) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-05}
0.634 (+/-0.330) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 0.0001}
0.611 (+/-0.239) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-10}
0.683 (+/-0.296) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-09}
0.699 (+/-0.307) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-08}
0.656 (+/-0.279) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-07}
0.694 (+/-0.296) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-06}
0.661 (+/-0.314) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-05}
0.633 (+/-0.328) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 0.0001}
0.611 (+/-0.240) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.699 (+/-0.307) for {'C': 1.0, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 10.0, 'kernel': 'linear'}
0.664 (+/-0.316) for {'C': 100.0, 'kernel': 'linear'}
0.661 (+/-0.317) for {'C': 1000.0, 'kernel': 'linear'}
0.634 (+/-0.319) for {'C': 10000.0, 'kernel': 'linear'}
0.635 (+/-0.321) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-08}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6995187154753071
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-09}
0.647 (+/-0.460) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-08}
0.668 (+/-0.291) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-07}
0.623 (+/-0.289) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-06}
0.655 (+/-0.304) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-05}
0.626 (+/-0.314) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.0001}
0.626 (+/-0.313) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-09}
0.747 (+/-0.502) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-08}
0.669 (+/-0.296) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-07}
0.636 (+/-0.369) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-06}
0.652 (+/-0.308) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-05}
0.626 (+/-0.314) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.0001}
0.619 (+/-0.321) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-09}
0.747 (+/-0.502) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-08}
0.684 (+/-0.362) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-07}
0.598 (+/-0.284) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-06}
0.641 (+/-0.304) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-05}
0.627 (+/-0.313) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.0001}
0.620 (+/-0.321) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-09}
0.848 (+/-0.459) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-08}
0.610 (+/-0.220) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-07}
0.599 (+/-0.284) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-06}
0.624 (+/-0.316) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-05}
0.626 (+/-0.313) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 0.0001}
0.619 (+/-0.321) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-09}
0.848 (+/-0.460) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-08}
0.564 (+/-0.146) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-07}
0.599 (+/-0.280) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-06}
0.624 (+/-0.315) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-05}
0.627 (+/-0.313) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.324) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-10}
0.697 (+/-0.492) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-09}
0.848 (+/-0.460) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-08}
0.543 (+/-0.142) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-07}
0.603 (+/-0.280) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-06}
0.624 (+/-0.315) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-05}
0.626 (+/-0.314) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.324) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-10}
0.747 (+/-0.502) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-09}
0.806 (+/-0.436) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-08}
0.538 (+/-0.143) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-07}
0.610 (+/-0.283) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-06}
0.624 (+/-0.315) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-05}
0.622 (+/-0.319) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.323) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-10}
0.747 (+/-0.502) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-09}
0.798 (+/-0.437) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-08}
0.534 (+/-0.144) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-07}
0.607 (+/-0.283) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-06}
0.624 (+/-0.315) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-05}
0.621 (+/-0.320) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.0001}
0.618 (+/-0.322) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-10}
0.848 (+/-0.459) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-09}
0.798 (+/-0.437) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-08}
0.557 (+/-0.294) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-07}
0.609 (+/-0.283) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-06}
0.624 (+/-0.315) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-05}
0.621 (+/-0.320) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 0.0001}
0.618 (+/-0.322) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-10}
0.848 (+/-0.460) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-09}
0.793 (+/-0.440) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-08}
0.557 (+/-0.294) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-07}
0.609 (+/-0.283) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-06}
0.624 (+/-0.315) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-05}
0.621 (+/-0.320) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 0.0001}
0.618 (+/-0.322) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-11}
0.697 (+/-0.492) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-10}
0.848 (+/-0.460) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-09}
0.793 (+/-0.440) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-08}
0.557 (+/-0.294) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-07}
0.610 (+/-0.283) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-06}
0.624 (+/-0.315) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-05}
0.621 (+/-0.320) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 0.0001}
0.618 (+/-0.322) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.798 (+/-0.437) for {'C': 0.1, 'kernel': 'linear'}
0.651 (+/-0.211) for {'C': 1.0, 'kernel': 'linear'}
0.632 (+/-0.282) for {'C': 10.0, 'kernel': 'linear'}
0.626 (+/-0.314) for {'C': 100.0, 'kernel': 'linear'}
0.629 (+/-0.312) for {'C': 1000.0, 'kernel': 'linear'}
0.619 (+/-0.321) for {'C': 10000.0, 'kernel': 'linear'}
0.620 (+/-0.321) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-09}
0.575 (+/-0.229) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-08}
0.698 (+/-0.308) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-07}
0.695 (+/-0.299) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-06}
0.695 (+/-0.307) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-05}
0.658 (+/-0.315) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.0001}
0.659 (+/-0.311) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-09}
0.617 (+/-0.238) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-08}
0.698 (+/-0.309) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-07}
0.693 (+/-0.293) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-06}
0.693 (+/-0.308) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-05}
0.659 (+/-0.315) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.0001}
0.634 (+/-0.320) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-09}
0.617 (+/-0.238) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-08}
0.681 (+/-0.296) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-07}
0.691 (+/-0.290) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-06}
0.675 (+/-0.294) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-05}
0.659 (+/-0.314) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.0001}
0.634 (+/-0.320) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-09}
0.658 (+/-0.217) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-08}
0.694 (+/-0.307) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-07}
0.690 (+/-0.287) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-06}
0.658 (+/-0.313) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-05}
0.659 (+/-0.315) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 0.0001}
0.634 (+/-0.319) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-09}
0.683 (+/-0.296) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-08}
0.687 (+/-0.298) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-07}
0.691 (+/-0.290) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-06}
0.657 (+/-0.315) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-05}
0.658 (+/-0.317) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 0.0001}
0.610 (+/-0.239) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-10}
0.600 (+/-0.245) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-09}
0.700 (+/-0.309) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-08}
0.672 (+/-0.284) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-07}
0.691 (+/-0.287) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-06}
0.657 (+/-0.316) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-05}
0.658 (+/-0.315) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 0.0001}
0.610 (+/-0.238) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-10}
0.617 (+/-0.238) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-09}
0.700 (+/-0.308) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-08}
0.669 (+/-0.247) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-07}
0.691 (+/-0.287) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-06}
0.657 (+/-0.316) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-05}
0.633 (+/-0.327) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 0.0001}
0.611 (+/-0.239) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-10}
0.617 (+/-0.238) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-09}
0.699 (+/-0.307) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-08}
0.671 (+/-0.269) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-07}
0.691 (+/-0.288) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-06}
0.657 (+/-0.315) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-05}
0.633 (+/-0.326) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.0001}
0.611 (+/-0.240) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-10}
0.658 (+/-0.217) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-09}
0.699 (+/-0.307) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-08}
0.660 (+/-0.251) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-07}
0.691 (+/-0.288) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-06}
0.657 (+/-0.315) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-05}
0.633 (+/-0.326) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 0.0001}
0.611 (+/-0.239) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-10}
0.683 (+/-0.296) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-09}
0.699 (+/-0.307) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-08}
0.653 (+/-0.246) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-07}
0.691 (+/-0.288) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-06}
0.657 (+/-0.315) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-05}
0.633 (+/-0.326) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 0.0001}
0.611 (+/-0.239) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-11}
0.600 (+/-0.245) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-10}
0.700 (+/-0.309) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-09}
0.699 (+/-0.307) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-08}
0.648 (+/-0.236) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-07}
0.691 (+/-0.288) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-06}
0.657 (+/-0.316) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-05}
0.633 (+/-0.327) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 0.0001}
0.611 (+/-0.240) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.700 (+/-0.309) for {'C': 0.1, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 1.0, 'kernel': 'linear'}
0.696 (+/-0.303) for {'C': 10.0, 'kernel': 'linear'}
0.658 (+/-0.315) for {'C': 100.0, 'kernel': 'linear'}
0.659 (+/-0.316) for {'C': 1000.0, 'kernel': 'linear'}
0.634 (+/-0.319) for {'C': 10000.0, 'kernel': 'linear'}
0.635 (+/-0.321) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-08}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.7
循环迭代之前，delta is: [5.58793545e-09 0.00000000e+00]
SVC模型clf_op_iter的参数是： {'kernel': 'rbf', 'decision_function_shape': 'ovr', 'class_weight': {-1: 15}, 'tol': 0.001, 'gamma': 1e-08, 'random_state': 0, 'cache_size': 200, 'verbose': False, 'degree': 3, 'C': 9999999.999999994, 'probability': False, 'shrinking': True, 'coef0': 0.0, 'max_iter': -1}

训练模型SVC对训练样本的预测准确率： 0.8936924167257264

测试集中，预测为舞弊样本的有： (array([1248], dtype=int64),) 
测试集中，实际为舞弊样本的有： (array([1246, 1247, 1248, 1249, 1250, 1251, 1252, 1253, 1254, 1255, 1256],
      dtype=int64),) 
预测的舞弊样本数目： 1

训练模型SVC对测试样本的预测准确率： 0.8936924167257264
以上是第36次特征筛选。
第36次特征筛选，AUC值是： 0.5454545454545454
X_train_iter_svc.shape is: (1257, 16)
X_test_iter_svc.shape is: (1257, 16)
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.722 (+/-0.473) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.621 (+/-0.320) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.665 (+/-0.225) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.613 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.698 (+/-0.307) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.17      0.17      0.17         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6983958900156649
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.643 (+/-0.202) for {'C': 1.0, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.628 (+/-0.313) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.17      0.17      0.17         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.698 (+/-0.306) for {'C': 1.0, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.660 (+/-0.315) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.17      0.17      0.17         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6979151207848958
RBF的粗grid search最佳超参： {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001} RBF的粗grid search最高分值： 0.6983958900156649
linear的粗grid search最佳超参： {'C': 1.0, 'kernel': 'linear'} linear的粗grid search最高分值： 0.6979151207848958
粗grid search得到的parameter是：
 [{'C': array([1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03, 1.e+04, 1.e+05,
       1.e+06, 1.e+07, 1.e+08]), 'kernel': ['rbf'], 'gamma': array([1.e-08, 1.e-07, 1.e-06, 1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01,
       1.e+00, 1.e+01, 1.e+02])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.848 (+/-0.459) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.848 (+/-0.459) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.690 (+/-0.300) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.622 (+/-0.310) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.848 (+/-0.459) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.690 (+/-0.300) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.698 (+/-0.310) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.620 (+/-0.321) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.848 (+/-0.459) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.665 (+/-0.225) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.690 (+/-0.299) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.621 (+/-0.320) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.848 (+/-0.459) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.665 (+/-0.225) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.690 (+/-0.299) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.635 (+/-0.313) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.619 (+/-0.321) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.621 (+/-0.320) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.848 (+/-0.459) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.665 (+/-0.225) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.690 (+/-0.299) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.643 (+/-0.320) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.618 (+/-0.322) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.619 (+/-0.321) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.621 (+/-0.320) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.748 (+/-0.396) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.723 (+/-0.359) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.669 (+/-0.295) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.638 (+/-0.315) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.625 (+/-0.314) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.619 (+/-0.321) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.621 (+/-0.320) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.848 (+/-0.460) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.781 (+/-0.418) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.655 (+/-0.362) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.634 (+/-0.311) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.630 (+/-0.311) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.619 (+/-0.322) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.619 (+/-0.321) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.621 (+/-0.320) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.793 (+/-0.440) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.687 (+/-0.434) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.613 (+/-0.310) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.628 (+/-0.313) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.624 (+/-0.318) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.620 (+/-0.320) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.619 (+/-0.321) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.621 (+/-0.320) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.690 (+/-0.300) for {'C': 1.0, 'kernel': 'linear'}
0.659 (+/-0.287) for {'C': 10.0, 'kernel': 'linear'}
0.648 (+/-0.315) for {'C': 100.0, 'kernel': 'linear'}
0.631 (+/-0.312) for {'C': 1000.0, 'kernel': 'linear'}
0.619 (+/-0.321) for {'C': 10000.0, 'kernel': 'linear'}
0.620 (+/-0.321) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      1.00       623

avg / total       0.98      0.99      0.99       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.658 (+/-0.217) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.002) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.658 (+/-0.217) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.699 (+/-0.307) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.236) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.008) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.658 (+/-0.217) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.699 (+/-0.307) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.699 (+/-0.307) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.612 (+/-0.241) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.008) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.658 (+/-0.217) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.698 (+/-0.307) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.699 (+/-0.307) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.587 (+/-0.234) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.008) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.658 (+/-0.217) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.698 (+/-0.307) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.698 (+/-0.307) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.662 (+/-0.315) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.008) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.658 (+/-0.217) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.698 (+/-0.307) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.698 (+/-0.307) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.662 (+/-0.317) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.610 (+/-0.241) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.008) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.699 (+/-0.308) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.699 (+/-0.307) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.698 (+/-0.306) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.662 (+/-0.316) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.659 (+/-0.313) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.586 (+/-0.236) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.238) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.238) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.008) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.683 (+/-0.296) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.699 (+/-0.309) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.696 (+/-0.299) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.662 (+/-0.314) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.660 (+/-0.317) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.634 (+/-0.317) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.585 (+/-0.237) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.238) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.238) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.008) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.699 (+/-0.307) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.657 (+/-0.283) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.638 (+/-0.318) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.660 (+/-0.314) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.634 (+/-0.328) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.636 (+/-0.320) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.585 (+/-0.237) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.238) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.238) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.008) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.699 (+/-0.307) for {'C': 1.0, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 10.0, 'kernel': 'linear'}
0.664 (+/-0.315) for {'C': 100.0, 'kernel': 'linear'}
0.661 (+/-0.316) for {'C': 1000.0, 'kernel': 'linear'}
0.634 (+/-0.319) for {'C': 10000.0, 'kernel': 'linear'}
0.635 (+/-0.321) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6991982026547944
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.624 (+/-0.227) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.671 (+/-0.291) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.622 (+/-0.317) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.671 (+/-0.291) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.633 (+/-0.197) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.610 (+/-0.312) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.671 (+/-0.291) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.622 (+/-0.186) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.655 (+/-0.305) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.620 (+/-0.321) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.671 (+/-0.291) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.622 (+/-0.186) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.655 (+/-0.305) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.323) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.621 (+/-0.320) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.671 (+/-0.291) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.622 (+/-0.186) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.655 (+/-0.304) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.625 (+/-0.314) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.619 (+/-0.321) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.621 (+/-0.320) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.747 (+/-0.502) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.680 (+/-0.295) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.620 (+/-0.186) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.656 (+/-0.303) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.625 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.618 (+/-0.323) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.619 (+/-0.321) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.621 (+/-0.320) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.647 (+/-0.460) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.668 (+/-0.291) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.613 (+/-0.189) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.656 (+/-0.303) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.626 (+/-0.314) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.625 (+/-0.315) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.619 (+/-0.321) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.621 (+/-0.320) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.848 (+/-0.460) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.543 (+/-0.142) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.615 (+/-0.283) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.625 (+/-0.315) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.627 (+/-0.313) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.619 (+/-0.322) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.619 (+/-0.321) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.621 (+/-0.320) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.793 (+/-0.440) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.557 (+/-0.294) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.595 (+/-0.298) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.625 (+/-0.315) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.623 (+/-0.319) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.620 (+/-0.320) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.619 (+/-0.321) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.621 (+/-0.320) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.798 (+/-0.437) for {'C': 0.1, 'kernel': 'linear'}
0.643 (+/-0.202) for {'C': 1.0, 'kernel': 'linear'}
0.632 (+/-0.282) for {'C': 10.0, 'kernel': 'linear'}
0.625 (+/-0.314) for {'C': 100.0, 'kernel': 'linear'}
0.628 (+/-0.313) for {'C': 1000.0, 'kernel': 'linear'}
0.619 (+/-0.321) for {'C': 10000.0, 'kernel': 'linear'}
0.620 (+/-0.321) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.665 (+/-0.314) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.698 (+/-0.306) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.011) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.306) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.305) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.611 (+/-0.240) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.008) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.698 (+/-0.306) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.304) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.695 (+/-0.307) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.612 (+/-0.241) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.008) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.698 (+/-0.306) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.304) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.695 (+/-0.307) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.609 (+/-0.243) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.008) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.698 (+/-0.306) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.304) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.695 (+/-0.307) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.658 (+/-0.314) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.008) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.617 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.698 (+/-0.306) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.697 (+/-0.304) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.695 (+/-0.307) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.658 (+/-0.313) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.610 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.008) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.575 (+/-0.229) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.698 (+/-0.308) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.696 (+/-0.302) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.695 (+/-0.307) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.659 (+/-0.315) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.658 (+/-0.312) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.586 (+/-0.236) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.238) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.238) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.008) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.700 (+/-0.309) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.672 (+/-0.283) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.694 (+/-0.296) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.657 (+/-0.316) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.659 (+/-0.315) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.634 (+/-0.317) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.585 (+/-0.237) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.238) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.238) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.008) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.699 (+/-0.307) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.648 (+/-0.236) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.636 (+/-0.312) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.657 (+/-0.317) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.634 (+/-0.328) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.636 (+/-0.320) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.585 (+/-0.237) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.238) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.238) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.008) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.700 (+/-0.309) for {'C': 0.1, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 1.0, 'kernel': 'linear'}
0.696 (+/-0.303) for {'C': 10.0, 'kernel': 'linear'}
0.658 (+/-0.314) for {'C': 100.0, 'kernel': 'linear'}
0.660 (+/-0.315) for {'C': 1000.0, 'kernel': 'linear'}
0.634 (+/-0.319) for {'C': 10000.0, 'kernel': 'linear'}
0.635 (+/-0.321) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.7
发现最优参数gamma为原先的最大/最小值，直接重新设置超参。
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-09}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-08}
0.748 (+/-0.396) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-07}
0.723 (+/-0.359) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-06}
0.669 (+/-0.295) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-05}
0.638 (+/-0.315) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.0001}
0.625 (+/-0.314) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-09}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-08}
0.748 (+/-0.389) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-07}
0.715 (+/-0.352) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-06}
0.669 (+/-0.295) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-05}
0.636 (+/-0.311) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.0001}
0.618 (+/-0.322) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-09}
0.747 (+/-0.502) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-08}
0.748 (+/-0.396) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-07}
0.710 (+/-0.351) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-06}
0.666 (+/-0.297) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-05}
0.632 (+/-0.310) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.0001}
0.619 (+/-0.322) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-09}
0.747 (+/-0.502) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-08}
0.740 (+/-0.392) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-07}
0.675 (+/-0.360) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-06}
0.637 (+/-0.308) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-05}
0.632 (+/-0.311) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 0.0001}
0.618 (+/-0.322) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-09}
0.797 (+/-0.492) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-08}
0.760 (+/-0.427) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-07}
0.657 (+/-0.360) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-06}
0.635 (+/-0.310) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-05}
0.631 (+/-0.311) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 0.0001}
0.618 (+/-0.322) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-09}
0.848 (+/-0.460) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-08}
0.781 (+/-0.418) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-07}
0.655 (+/-0.362) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-06}
0.634 (+/-0.311) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-05}
0.630 (+/-0.311) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 0.0001}
0.619 (+/-0.322) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-09}
0.823 (+/-0.451) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-08}
0.787 (+/-0.446) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-07}
0.660 (+/-0.359) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-06}
0.630 (+/-0.312) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-05}
0.623 (+/-0.319) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 0.0001}
0.620 (+/-0.321) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-10}
0.747 (+/-0.502) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-09}
0.806 (+/-0.436) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-08}
0.705 (+/-0.426) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-07}
0.643 (+/-0.372) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-06}
0.630 (+/-0.312) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-05}
0.623 (+/-0.319) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.0001}
0.620 (+/-0.321) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-10}
0.747 (+/-0.502) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-09}
0.798 (+/-0.437) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-08}
0.713 (+/-0.471) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-07}
0.651 (+/-0.371) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-06}
0.630 (+/-0.312) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-05}
0.623 (+/-0.319) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 0.0001}
0.620 (+/-0.320) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-10}
0.797 (+/-0.492) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-09}
0.793 (+/-0.440) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-08}
0.687 (+/-0.434) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-07}
0.609 (+/-0.308) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-06}
0.630 (+/-0.312) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-05}
0.623 (+/-0.319) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 0.0001}
0.620 (+/-0.320) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-10}
0.848 (+/-0.460) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-09}
0.793 (+/-0.440) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-08}
0.687 (+/-0.434) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-07}
0.613 (+/-0.310) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-06}
0.628 (+/-0.313) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-05}
0.624 (+/-0.318) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 0.0001}
0.620 (+/-0.320) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.690 (+/-0.300) for {'C': 1.0, 'kernel': 'linear'}
0.659 (+/-0.287) for {'C': 10.0, 'kernel': 'linear'}
0.648 (+/-0.315) for {'C': 100.0, 'kernel': 'linear'}
0.631 (+/-0.312) for {'C': 1000.0, 'kernel': 'linear'}
0.619 (+/-0.321) for {'C': 10000.0, 'kernel': 'linear'}
0.620 (+/-0.321) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      1.00       623

avg / total       0.98      0.99      0.99       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-09}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-08}
0.699 (+/-0.308) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-07}
0.699 (+/-0.307) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-06}
0.698 (+/-0.306) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-05}
0.662 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.0001}
0.659 (+/-0.313) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-09}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-08}
0.699 (+/-0.308) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-07}
0.699 (+/-0.307) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-06}
0.698 (+/-0.306) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-05}
0.662 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.0001}
0.633 (+/-0.319) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-09}
0.617 (+/-0.238) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-08}
0.699 (+/-0.309) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-07}
0.699 (+/-0.306) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-06}
0.697 (+/-0.307) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-05}
0.661 (+/-0.316) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.0001}
0.633 (+/-0.319) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-09}
0.617 (+/-0.238) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-08}
0.699 (+/-0.308) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-07}
0.697 (+/-0.303) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-06}
0.663 (+/-0.313) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-05}
0.660 (+/-0.317) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 0.0001}
0.633 (+/-0.318) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-09}
0.642 (+/-0.236) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-08}
0.699 (+/-0.308) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-07}
0.696 (+/-0.300) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-06}
0.663 (+/-0.314) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-05}
0.660 (+/-0.317) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 0.0001}
0.633 (+/-0.316) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-09}
0.683 (+/-0.296) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-08}
0.699 (+/-0.309) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-07}
0.696 (+/-0.299) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-06}
0.662 (+/-0.314) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-05}
0.660 (+/-0.317) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 0.0001}
0.634 (+/-0.317) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-09}
0.683 (+/-0.296) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-08}
0.682 (+/-0.294) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-07}
0.680 (+/-0.288) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-06}
0.661 (+/-0.313) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-05}
0.635 (+/-0.327) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 0.0001}
0.635 (+/-0.320) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-10}
0.617 (+/-0.238) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-09}
0.700 (+/-0.308) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-08}
0.664 (+/-0.309) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-07}
0.663 (+/-0.309) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-06}
0.661 (+/-0.314) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-05}
0.635 (+/-0.327) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.0001}
0.635 (+/-0.320) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-10}
0.617 (+/-0.238) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-09}
0.699 (+/-0.307) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-08}
0.661 (+/-0.297) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-07}
0.663 (+/-0.308) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-06}
0.661 (+/-0.314) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-05}
0.634 (+/-0.327) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 0.0001}
0.636 (+/-0.320) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-10}
0.642 (+/-0.236) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-09}
0.699 (+/-0.307) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-08}
0.658 (+/-0.287) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-07}
0.638 (+/-0.317) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-06}
0.661 (+/-0.314) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-05}
0.634 (+/-0.327) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 0.0001}
0.636 (+/-0.320) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-10}
0.683 (+/-0.296) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-09}
0.699 (+/-0.307) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-08}
0.657 (+/-0.283) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-07}
0.638 (+/-0.318) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-06}
0.660 (+/-0.314) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-05}
0.634 (+/-0.329) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 0.0001}
0.636 (+/-0.320) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.699 (+/-0.307) for {'C': 1.0, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 10.0, 'kernel': 'linear'}
0.664 (+/-0.315) for {'C': 100.0, 'kernel': 'linear'}
0.661 (+/-0.316) for {'C': 1000.0, 'kernel': 'linear'}
0.634 (+/-0.319) for {'C': 10000.0, 'kernel': 'linear'}
0.635 (+/-0.321) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-08}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6995187154753071
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-09}
0.647 (+/-0.460) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-08}
0.668 (+/-0.291) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-07}
0.613 (+/-0.189) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-06}
0.656 (+/-0.303) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-05}
0.626 (+/-0.314) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.0001}
0.625 (+/-0.315) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-09}
0.747 (+/-0.502) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-08}
0.669 (+/-0.296) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-07}
0.646 (+/-0.367) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-06}
0.652 (+/-0.308) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-05}
0.627 (+/-0.312) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.0001}
0.618 (+/-0.322) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-09}
0.747 (+/-0.502) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-08}
0.684 (+/-0.362) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-07}
0.610 (+/-0.283) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-06}
0.650 (+/-0.310) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-05}
0.627 (+/-0.313) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.0001}
0.619 (+/-0.322) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-09}
0.848 (+/-0.459) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-08}
0.608 (+/-0.220) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-07}
0.615 (+/-0.280) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-06}
0.624 (+/-0.315) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-05}
0.627 (+/-0.313) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 0.0001}
0.618 (+/-0.322) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-09}
0.848 (+/-0.460) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-08}
0.564 (+/-0.146) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-07}
0.612 (+/-0.281) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-06}
0.624 (+/-0.315) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-05}
0.627 (+/-0.312) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 0.0001}
0.618 (+/-0.322) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-10}
0.697 (+/-0.492) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-09}
0.848 (+/-0.460) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-08}
0.543 (+/-0.142) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-07}
0.615 (+/-0.283) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-06}
0.625 (+/-0.315) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-05}
0.627 (+/-0.313) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 0.0001}
0.619 (+/-0.322) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-10}
0.747 (+/-0.502) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-09}
0.806 (+/-0.436) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-08}
0.538 (+/-0.143) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-07}
0.617 (+/-0.282) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-06}
0.625 (+/-0.314) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-05}
0.622 (+/-0.319) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 0.0001}
0.620 (+/-0.321) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-10}
0.747 (+/-0.502) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-09}
0.798 (+/-0.437) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-08}
0.533 (+/-0.145) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-07}
0.600 (+/-0.288) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-06}
0.625 (+/-0.314) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-05}
0.622 (+/-0.319) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.0001}
0.620 (+/-0.321) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-10}
0.848 (+/-0.459) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-09}
0.798 (+/-0.437) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-08}
0.557 (+/-0.294) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-07}
0.608 (+/-0.291) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-06}
0.625 (+/-0.315) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-05}
0.622 (+/-0.319) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 0.0001}
0.620 (+/-0.320) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-10}
0.848 (+/-0.460) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-09}
0.793 (+/-0.440) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-08}
0.557 (+/-0.294) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-07}
0.591 (+/-0.295) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-06}
0.625 (+/-0.315) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-05}
0.622 (+/-0.319) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 0.0001}
0.620 (+/-0.320) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-11}
0.697 (+/-0.492) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-10}
0.848 (+/-0.460) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-09}
0.793 (+/-0.440) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-08}
0.557 (+/-0.294) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-07}
0.595 (+/-0.298) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-06}
0.625 (+/-0.315) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-05}
0.623 (+/-0.319) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 0.0001}
0.620 (+/-0.320) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.798 (+/-0.437) for {'C': 0.1, 'kernel': 'linear'}
0.643 (+/-0.202) for {'C': 1.0, 'kernel': 'linear'}
0.632 (+/-0.282) for {'C': 10.0, 'kernel': 'linear'}
0.625 (+/-0.314) for {'C': 100.0, 'kernel': 'linear'}
0.628 (+/-0.313) for {'C': 1000.0, 'kernel': 'linear'}
0.619 (+/-0.321) for {'C': 10000.0, 'kernel': 'linear'}
0.620 (+/-0.321) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-09}
0.575 (+/-0.229) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-08}
0.698 (+/-0.308) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-07}
0.696 (+/-0.302) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-06}
0.695 (+/-0.307) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-05}
0.659 (+/-0.315) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.0001}
0.658 (+/-0.312) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-09}
0.617 (+/-0.238) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-08}
0.698 (+/-0.309) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-07}
0.694 (+/-0.295) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-06}
0.693 (+/-0.308) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-05}
0.660 (+/-0.314) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.0001}
0.633 (+/-0.319) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-09}
0.617 (+/-0.238) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-08}
0.681 (+/-0.296) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-07}
0.693 (+/-0.294) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-06}
0.691 (+/-0.309) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-05}
0.660 (+/-0.313) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.0001}
0.633 (+/-0.319) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-09}
0.658 (+/-0.217) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-08}
0.694 (+/-0.306) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-07}
0.694 (+/-0.294) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-06}
0.658 (+/-0.314) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-05}
0.659 (+/-0.315) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 0.0001}
0.633 (+/-0.318) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-09}
0.683 (+/-0.296) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-08}
0.687 (+/-0.298) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-07}
0.693 (+/-0.294) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-06}
0.657 (+/-0.315) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-05}
0.659 (+/-0.315) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 0.0001}
0.633 (+/-0.316) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-10}
0.600 (+/-0.245) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-09}
0.700 (+/-0.309) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-08}
0.672 (+/-0.284) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-07}
0.694 (+/-0.296) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-06}
0.657 (+/-0.316) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-05}
0.659 (+/-0.315) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 0.0001}
0.634 (+/-0.317) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-10}
0.617 (+/-0.238) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-09}
0.700 (+/-0.308) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-08}
0.670 (+/-0.247) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-07}
0.678 (+/-0.283) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-06}
0.657 (+/-0.316) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-05}
0.634 (+/-0.326) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 0.0001}
0.635 (+/-0.320) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-10}
0.617 (+/-0.238) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-09}
0.699 (+/-0.307) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-08}
0.646 (+/-0.236) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-07}
0.661 (+/-0.305) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-06}
0.657 (+/-0.317) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-05}
0.634 (+/-0.327) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.0001}
0.635 (+/-0.320) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-10}
0.658 (+/-0.217) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-09}
0.699 (+/-0.307) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-08}
0.660 (+/-0.251) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-07}
0.661 (+/-0.304) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-06}
0.657 (+/-0.317) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-05}
0.634 (+/-0.327) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 0.0001}
0.636 (+/-0.320) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-10}
0.683 (+/-0.296) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-09}
0.699 (+/-0.307) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-08}
0.653 (+/-0.246) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-07}
0.636 (+/-0.312) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-06}
0.656 (+/-0.318) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-05}
0.634 (+/-0.327) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 0.0001}
0.636 (+/-0.320) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-11}
0.600 (+/-0.245) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-10}
0.700 (+/-0.309) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-09}
0.699 (+/-0.307) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-08}
0.648 (+/-0.236) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-07}
0.636 (+/-0.312) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-06}
0.657 (+/-0.317) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-05}
0.634 (+/-0.328) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 0.0001}
0.636 (+/-0.320) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.700 (+/-0.309) for {'C': 0.1, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 1.0, 'kernel': 'linear'}
0.696 (+/-0.303) for {'C': 10.0, 'kernel': 'linear'}
0.658 (+/-0.314) for {'C': 100.0, 'kernel': 'linear'}
0.660 (+/-0.315) for {'C': 1000.0, 'kernel': 'linear'}
0.634 (+/-0.319) for {'C': 10000.0, 'kernel': 'linear'}
0.635 (+/-0.321) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-08}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.7
循环迭代之前，delta is: [5.58793545e-09 0.00000000e+00]
SVC模型clf_op_iter的参数是： {'kernel': 'rbf', 'decision_function_shape': 'ovr', 'class_weight': {-1: 15}, 'tol': 0.001, 'gamma': 1e-08, 'random_state': 0, 'cache_size': 200, 'verbose': False, 'degree': 3, 'C': 9999999.999999994, 'probability': False, 'shrinking': True, 'coef0': 0.0, 'max_iter': -1}

训练模型SVC对训练样本的预测准确率： 0.8936924167257264

测试集中，预测为舞弊样本的有： (array([1248], dtype=int64),) 
测试集中，实际为舞弊样本的有： (array([1246, 1247, 1248, 1249, 1250, 1251, 1252, 1253, 1254, 1255, 1256],
      dtype=int64),) 
预测的舞弊样本数目： 1

训练模型SVC对测试样本的预测准确率： 0.8936924167257264
以上是第37次特征筛选。
第37次特征筛选，AUC值是： 0.5454545454545454
X_train_iter_svc.shape is: (1257, 15)
X_test_iter_svc.shape is: (1257, 15)
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.638 (+/-0.382) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.665 (+/-0.225) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.613 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.698 (+/-0.307) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.17      0.17      0.17         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6983958900156649
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.647 (+/-0.212) for {'C': 1.0, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.616 (+/-0.302) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.17      0.17      0.17         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.698 (+/-0.306) for {'C': 1.0, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.659 (+/-0.316) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.17      0.17      0.17         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6979151207848958
RBF的粗grid search最佳超参： {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001} RBF的粗grid search最高分值： 0.6983958900156649
linear的粗grid search最佳超参： {'C': 1.0, 'kernel': 'linear'} linear的粗grid search最高分值： 0.6979151207848958
粗grid search得到的parameter是：
 [{'C': array([1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03, 1.e+04, 1.e+05,
       1.e+06, 1.e+07, 1.e+08]), 'kernel': ['rbf'], 'gamma': array([1.e-08, 1.e-07, 1.e-06, 1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01,
       1.e+00, 1.e+01, 1.e+02])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.848 (+/-0.459) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.848 (+/-0.459) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.690 (+/-0.300) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.622 (+/-0.310) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.848 (+/-0.459) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.665 (+/-0.225) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.677 (+/-0.308) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.636 (+/-0.383) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.848 (+/-0.459) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.665 (+/-0.225) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.660 (+/-0.290) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.601 (+/-0.315) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.638 (+/-0.382) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.848 (+/-0.459) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.665 (+/-0.225) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.655 (+/-0.286) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.619 (+/-0.300) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.608 (+/-0.309) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.638 (+/-0.382) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.848 (+/-0.459) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.665 (+/-0.225) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.655 (+/-0.286) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.619 (+/-0.300) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.605 (+/-0.311) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.609 (+/-0.309) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.638 (+/-0.382) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.748 (+/-0.396) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.660 (+/-0.219) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.655 (+/-0.286) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.618 (+/-0.300) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.612 (+/-0.304) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.604 (+/-0.312) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.609 (+/-0.309) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.638 (+/-0.382) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.848 (+/-0.460) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.715 (+/-0.416) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.648 (+/-0.362) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.610 (+/-0.289) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.615 (+/-0.302) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.607 (+/-0.310) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.605 (+/-0.311) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.609 (+/-0.309) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.638 (+/-0.382) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.793 (+/-0.440) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.700 (+/-0.421) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.638 (+/-0.372) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.608 (+/-0.291) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.613 (+/-0.303) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.607 (+/-0.309) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.605 (+/-0.311) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.609 (+/-0.309) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.638 (+/-0.382) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.690 (+/-0.300) for {'C': 1.0, 'kernel': 'linear'}
0.666 (+/-0.357) for {'C': 10.0, 'kernel': 'linear'}
0.629 (+/-0.298) for {'C': 100.0, 'kernel': 'linear'}
0.617 (+/-0.301) for {'C': 1000.0, 'kernel': 'linear'}
0.608 (+/-0.309) for {'C': 10000.0, 'kernel': 'linear'}
0.607 (+/-0.309) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      1.00       623

avg / total       0.98      0.99      0.99       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.658 (+/-0.217) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.658 (+/-0.217) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.699 (+/-0.307) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.008) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.658 (+/-0.217) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.307) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.698 (+/-0.307) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.612 (+/-0.240) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.007) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.658 (+/-0.217) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.698 (+/-0.307) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.306) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.587 (+/-0.233) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.007) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.658 (+/-0.217) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.698 (+/-0.307) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.306) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.662 (+/-0.313) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.007) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.658 (+/-0.217) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.698 (+/-0.307) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.306) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.661 (+/-0.314) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.610 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.007) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.699 (+/-0.308) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.698 (+/-0.306) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.697 (+/-0.306) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.662 (+/-0.312) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.658 (+/-0.310) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.609 (+/-0.238) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.240) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.239) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.007) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.683 (+/-0.296) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.676 (+/-0.243) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.695 (+/-0.302) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.661 (+/-0.312) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.660 (+/-0.313) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.632 (+/-0.321) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.609 (+/-0.241) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.240) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.239) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.007) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.699 (+/-0.307) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.654 (+/-0.243) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.662 (+/-0.311) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.660 (+/-0.314) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.659 (+/-0.312) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.633 (+/-0.323) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.609 (+/-0.241) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.240) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.239) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.007) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.699 (+/-0.307) for {'C': 1.0, 'kernel': 'linear'}
0.697 (+/-0.305) for {'C': 10.0, 'kernel': 'linear'}
0.663 (+/-0.314) for {'C': 100.0, 'kernel': 'linear'}
0.660 (+/-0.315) for {'C': 1000.0, 'kernel': 'linear'}
0.634 (+/-0.323) for {'C': 10000.0, 'kernel': 'linear'}
0.633 (+/-0.324) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6991982026547944
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.652 (+/-0.313) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.671 (+/-0.291) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.647 (+/-0.380) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.671 (+/-0.291) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.188) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.602 (+/-0.301) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.671 (+/-0.291) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.624 (+/-0.188) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.634 (+/-0.290) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.636 (+/-0.383) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.671 (+/-0.291) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.624 (+/-0.188) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.628 (+/-0.280) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.605 (+/-0.311) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.638 (+/-0.382) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.671 (+/-0.291) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.620 (+/-0.184) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.627 (+/-0.280) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.613 (+/-0.304) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.608 (+/-0.309) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.638 (+/-0.382) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.747 (+/-0.502) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.680 (+/-0.295) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.624 (+/-0.188) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.627 (+/-0.280) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.613 (+/-0.303) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.605 (+/-0.311) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.609 (+/-0.309) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.638 (+/-0.382) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.697 (+/-0.492) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.651 (+/-0.225) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.641 (+/-0.286) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.625 (+/-0.281) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.612 (+/-0.304) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.612 (+/-0.304) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.604 (+/-0.312) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.609 (+/-0.309) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.638 (+/-0.382) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.848 (+/-0.460) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.535 (+/-0.144) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.607 (+/-0.276) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.603 (+/-0.294) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.615 (+/-0.302) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.607 (+/-0.310) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.605 (+/-0.311) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.609 (+/-0.309) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.638 (+/-0.382) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.793 (+/-0.440) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.551 (+/-0.297) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.606 (+/-0.287) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.604 (+/-0.293) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.613 (+/-0.304) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.607 (+/-0.309) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.605 (+/-0.311) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.609 (+/-0.309) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.638 (+/-0.382) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.798 (+/-0.437) for {'C': 0.1, 'kernel': 'linear'}
0.647 (+/-0.212) for {'C': 1.0, 'kernel': 'linear'}
0.618 (+/-0.276) for {'C': 10.0, 'kernel': 'linear'}
0.612 (+/-0.304) for {'C': 100.0, 'kernel': 'linear'}
0.616 (+/-0.302) for {'C': 1000.0, 'kernel': 'linear'}
0.608 (+/-0.309) for {'C': 10000.0, 'kernel': 'linear'}
0.607 (+/-0.309) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.665 (+/-0.315) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.698 (+/-0.306) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.495 (+/-0.011) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.306) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.304) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.611 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.008) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.698 (+/-0.306) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.304) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.694 (+/-0.306) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.612 (+/-0.240) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.007) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.698 (+/-0.306) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.304) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.694 (+/-0.306) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.608 (+/-0.242) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.007) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.698 (+/-0.306) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.303) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.694 (+/-0.305) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.658 (+/-0.313) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.007) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.617 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.698 (+/-0.306) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.697 (+/-0.304) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.694 (+/-0.305) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.658 (+/-0.313) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.609 (+/-0.241) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.007) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.600 (+/-0.245) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.698 (+/-0.307) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.697 (+/-0.302) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.694 (+/-0.304) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.658 (+/-0.312) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.658 (+/-0.311) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.609 (+/-0.238) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.240) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.239) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.007) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.700 (+/-0.309) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.639 (+/-0.208) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.694 (+/-0.302) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.656 (+/-0.315) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.659 (+/-0.314) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.632 (+/-0.321) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.609 (+/-0.241) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.240) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.239) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.007) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.699 (+/-0.307) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.575 (+/-0.233) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.662 (+/-0.313) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.656 (+/-0.317) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.658 (+/-0.313) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.633 (+/-0.323) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.609 (+/-0.241) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.240) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.239) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.007) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.700 (+/-0.309) for {'C': 0.1, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 1.0, 'kernel': 'linear'}
0.695 (+/-0.303) for {'C': 10.0, 'kernel': 'linear'}
0.658 (+/-0.314) for {'C': 100.0, 'kernel': 'linear'}
0.659 (+/-0.316) for {'C': 1000.0, 'kernel': 'linear'}
0.634 (+/-0.324) for {'C': 10000.0, 'kernel': 'linear'}
0.633 (+/-0.324) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.7
发现最优参数gamma为原先的最大/最小值，直接重新设置超参。
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-09}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-08}
0.748 (+/-0.396) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-07}
0.660 (+/-0.219) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-06}
0.655 (+/-0.286) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-05}
0.618 (+/-0.300) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.0001}
0.612 (+/-0.304) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-09}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-08}
0.715 (+/-0.352) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-07}
0.692 (+/-0.352) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-06}
0.651 (+/-0.286) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.302) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.0001}
0.607 (+/-0.310) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-09}
0.747 (+/-0.502) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-08}
0.740 (+/-0.392) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-07}
0.680 (+/-0.350) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-06}
0.648 (+/-0.292) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.301) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.0001}
0.606 (+/-0.310) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-09}
0.747 (+/-0.502) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-08}
0.710 (+/-0.352) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-07}
0.672 (+/-0.355) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-06}
0.629 (+/-0.298) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.302) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 0.0001}
0.606 (+/-0.310) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-09}
0.797 (+/-0.492) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-08}
0.716 (+/-0.414) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-07}
0.657 (+/-0.359) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-06}
0.613 (+/-0.288) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-05}
0.615 (+/-0.302) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 0.0001}
0.606 (+/-0.310) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-09}
0.848 (+/-0.460) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-08}
0.740 (+/-0.449) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-07}
0.648 (+/-0.362) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-06}
0.610 (+/-0.289) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-05}
0.615 (+/-0.302) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 0.0001}
0.607 (+/-0.310) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-09}
0.823 (+/-0.451) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-08}
0.688 (+/-0.374) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-07}
0.650 (+/-0.361) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-06}
0.610 (+/-0.290) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-05}
0.615 (+/-0.302) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 0.0001}
0.607 (+/-0.309) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-10}
0.747 (+/-0.502) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-09}
0.806 (+/-0.436) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-08}
0.701 (+/-0.420) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-07}
0.647 (+/-0.363) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-06}
0.609 (+/-0.290) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-05}
0.615 (+/-0.302) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.0001}
0.607 (+/-0.310) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-10}
0.747 (+/-0.502) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-09}
0.798 (+/-0.437) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-08}
0.701 (+/-0.420) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-07}
0.636 (+/-0.373) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-06}
0.609 (+/-0.290) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-05}
0.614 (+/-0.303) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 0.0001}
0.607 (+/-0.309) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-10}
0.797 (+/-0.492) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-09}
0.793 (+/-0.440) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-08}
0.725 (+/-0.457) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-07}
0.637 (+/-0.373) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-06}
0.608 (+/-0.291) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-05}
0.614 (+/-0.303) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 0.0001}
0.607 (+/-0.309) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-10}
0.848 (+/-0.460) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-09}
0.793 (+/-0.440) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-08}
0.700 (+/-0.421) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-07}
0.638 (+/-0.372) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-06}
0.608 (+/-0.291) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-05}
0.613 (+/-0.303) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 0.0001}
0.607 (+/-0.309) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.690 (+/-0.300) for {'C': 1.0, 'kernel': 'linear'}
0.666 (+/-0.357) for {'C': 10.0, 'kernel': 'linear'}
0.629 (+/-0.298) for {'C': 100.0, 'kernel': 'linear'}
0.617 (+/-0.301) for {'C': 1000.0, 'kernel': 'linear'}
0.608 (+/-0.309) for {'C': 10000.0, 'kernel': 'linear'}
0.607 (+/-0.309) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      1.00       623

avg / total       0.98      0.99      0.99       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-09}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-08}
0.699 (+/-0.308) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-07}
0.698 (+/-0.306) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-06}
0.697 (+/-0.306) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-05}
0.662 (+/-0.312) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.0001}
0.658 (+/-0.310) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-09}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-08}
0.699 (+/-0.307) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-07}
0.698 (+/-0.306) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-06}
0.697 (+/-0.306) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-05}
0.661 (+/-0.313) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.0001}
0.633 (+/-0.321) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-09}
0.617 (+/-0.238) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-08}
0.699 (+/-0.308) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-07}
0.698 (+/-0.305) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-06}
0.681 (+/-0.294) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-05}
0.661 (+/-0.313) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.0001}
0.633 (+/-0.319) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-09}
0.617 (+/-0.238) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-08}
0.699 (+/-0.306) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-07}
0.697 (+/-0.304) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-06}
0.663 (+/-0.314) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-05}
0.660 (+/-0.313) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 0.0001}
0.633 (+/-0.320) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-09}
0.642 (+/-0.236) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-08}
0.690 (+/-0.276) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-07}
0.696 (+/-0.302) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-06}
0.662 (+/-0.312) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-05}
0.660 (+/-0.313) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 0.0001}
0.633 (+/-0.319) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-09}
0.683 (+/-0.296) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-08}
0.672 (+/-0.237) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-07}
0.695 (+/-0.302) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-06}
0.661 (+/-0.312) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-05}
0.660 (+/-0.313) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 0.0001}
0.632 (+/-0.321) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-09}
0.683 (+/-0.296) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-08}
0.663 (+/-0.233) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-07}
0.696 (+/-0.303) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-06}
0.661 (+/-0.312) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-05}
0.660 (+/-0.313) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 0.0001}
0.634 (+/-0.322) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-10}
0.617 (+/-0.238) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-09}
0.700 (+/-0.308) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-08}
0.658 (+/-0.238) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-07}
0.679 (+/-0.291) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-06}
0.661 (+/-0.313) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-05}
0.660 (+/-0.314) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.0001}
0.633 (+/-0.322) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-10}
0.617 (+/-0.238) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-09}
0.699 (+/-0.307) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-08}
0.658 (+/-0.239) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-07}
0.662 (+/-0.312) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-06}
0.661 (+/-0.313) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-05}
0.659 (+/-0.313) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 0.0001}
0.633 (+/-0.324) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-10}
0.642 (+/-0.236) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-09}
0.699 (+/-0.307) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-08}
0.653 (+/-0.244) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-07}
0.662 (+/-0.311) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-06}
0.660 (+/-0.314) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-05}
0.659 (+/-0.313) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 0.0001}
0.633 (+/-0.324) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-10}
0.683 (+/-0.296) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-09}
0.699 (+/-0.307) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-08}
0.652 (+/-0.245) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-07}
0.662 (+/-0.311) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-06}
0.660 (+/-0.314) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-05}
0.659 (+/-0.312) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 0.0001}
0.633 (+/-0.323) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.699 (+/-0.307) for {'C': 1.0, 'kernel': 'linear'}
0.697 (+/-0.305) for {'C': 10.0, 'kernel': 'linear'}
0.663 (+/-0.314) for {'C': 100.0, 'kernel': 'linear'}
0.660 (+/-0.315) for {'C': 1000.0, 'kernel': 'linear'}
0.634 (+/-0.323) for {'C': 10000.0, 'kernel': 'linear'}
0.633 (+/-0.324) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-08}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6995187154753071
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-09}
0.697 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-08}
0.651 (+/-0.225) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-07}
0.641 (+/-0.286) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-06}
0.625 (+/-0.281) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-05}
0.612 (+/-0.304) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.0001}
0.612 (+/-0.304) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-09}
0.747 (+/-0.502) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-08}
0.635 (+/-0.201) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-07}
0.641 (+/-0.365) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-06}
0.631 (+/-0.289) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-05}
0.613 (+/-0.303) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.0001}
0.607 (+/-0.310) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-09}
0.747 (+/-0.502) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-08}
0.618 (+/-0.188) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-07}
0.639 (+/-0.366) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-06}
0.625 (+/-0.288) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-05}
0.614 (+/-0.303) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.0001}
0.606 (+/-0.310) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-09}
0.848 (+/-0.459) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-08}
0.587 (+/-0.163) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-07}
0.607 (+/-0.277) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-06}
0.616 (+/-0.301) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-05}
0.614 (+/-0.302) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 0.0001}
0.606 (+/-0.310) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-09}
0.848 (+/-0.460) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-08}
0.553 (+/-0.147) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-07}
0.607 (+/-0.276) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-06}
0.606 (+/-0.291) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-05}
0.615 (+/-0.302) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 0.0001}
0.606 (+/-0.310) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-10}
0.697 (+/-0.492) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-09}
0.848 (+/-0.460) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-08}
0.535 (+/-0.144) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-07}
0.607 (+/-0.276) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-06}
0.603 (+/-0.294) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-05}
0.615 (+/-0.302) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 0.0001}
0.607 (+/-0.310) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-10}
0.747 (+/-0.502) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-09}
0.806 (+/-0.436) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-08}
0.530 (+/-0.146) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-07}
0.617 (+/-0.279) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-06}
0.603 (+/-0.294) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-05}
0.614 (+/-0.303) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 0.0001}
0.607 (+/-0.309) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-10}
0.747 (+/-0.502) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-09}
0.798 (+/-0.437) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-08}
0.528 (+/-0.147) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-07}
0.614 (+/-0.279) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-06}
0.603 (+/-0.293) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-05}
0.615 (+/-0.302) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.0001}
0.607 (+/-0.310) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-10}
0.848 (+/-0.459) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-09}
0.798 (+/-0.437) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-08}
0.551 (+/-0.297) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-07}
0.602 (+/-0.287) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-06}
0.604 (+/-0.293) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-05}
0.614 (+/-0.303) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 0.0001}
0.607 (+/-0.309) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-10}
0.848 (+/-0.460) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-09}
0.793 (+/-0.440) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-08}
0.550 (+/-0.298) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-07}
0.605 (+/-0.288) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-06}
0.604 (+/-0.293) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-05}
0.614 (+/-0.303) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 0.0001}
0.607 (+/-0.309) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-11}
0.697 (+/-0.492) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-10}
0.848 (+/-0.460) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-09}
0.793 (+/-0.440) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-08}
0.551 (+/-0.297) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-07}
0.606 (+/-0.287) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-06}
0.604 (+/-0.293) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-05}
0.613 (+/-0.304) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 0.0001}
0.607 (+/-0.309) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.798 (+/-0.437) for {'C': 0.1, 'kernel': 'linear'}
0.647 (+/-0.212) for {'C': 1.0, 'kernel': 'linear'}
0.618 (+/-0.276) for {'C': 10.0, 'kernel': 'linear'}
0.612 (+/-0.304) for {'C': 100.0, 'kernel': 'linear'}
0.616 (+/-0.302) for {'C': 1000.0, 'kernel': 'linear'}
0.608 (+/-0.309) for {'C': 10000.0, 'kernel': 'linear'}
0.607 (+/-0.309) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-09}
0.600 (+/-0.245) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-08}
0.698 (+/-0.307) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-07}
0.697 (+/-0.302) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-06}
0.694 (+/-0.304) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-05}
0.658 (+/-0.312) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.0001}
0.658 (+/-0.311) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-09}
0.617 (+/-0.238) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-08}
0.697 (+/-0.307) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-07}
0.695 (+/-0.300) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-06}
0.717 (+/-0.353) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-05}
0.659 (+/-0.312) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.0001}
0.633 (+/-0.322) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-09}
0.617 (+/-0.238) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-08}
0.696 (+/-0.308) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-07}
0.694 (+/-0.299) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-06}
0.700 (+/-0.347) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-05}
0.659 (+/-0.312) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.0001}
0.632 (+/-0.320) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-09}
0.658 (+/-0.217) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-08}
0.692 (+/-0.300) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-07}
0.695 (+/-0.302) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-06}
0.682 (+/-0.370) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-05}
0.659 (+/-0.313) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 0.0001}
0.632 (+/-0.321) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-09}
0.683 (+/-0.296) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-08}
0.677 (+/-0.265) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-07}
0.694 (+/-0.302) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-06}
0.681 (+/-0.370) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-05}
0.659 (+/-0.313) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 0.0001}
0.633 (+/-0.319) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-10}
0.600 (+/-0.245) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-09}
0.700 (+/-0.309) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-08}
0.639 (+/-0.208) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-07}
0.694 (+/-0.302) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-06}
0.656 (+/-0.315) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-05}
0.659 (+/-0.314) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 0.0001}
0.632 (+/-0.321) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-10}
0.617 (+/-0.238) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-09}
0.700 (+/-0.308) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-08}
0.621 (+/-0.143) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-07}
0.695 (+/-0.304) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-06}
0.655 (+/-0.314) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-05}
0.659 (+/-0.313) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 0.0001}
0.634 (+/-0.322) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-10}
0.617 (+/-0.238) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-09}
0.699 (+/-0.307) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-08}
0.589 (+/-0.141) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-07}
0.679 (+/-0.292) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-06}
0.655 (+/-0.315) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-05}
0.659 (+/-0.315) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.0001}
0.633 (+/-0.322) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-10}
0.658 (+/-0.217) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-09}
0.699 (+/-0.307) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-08}
0.567 (+/-0.124) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-07}
0.662 (+/-0.313) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-06}
0.656 (+/-0.317) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-05}
0.659 (+/-0.315) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 0.0001}
0.633 (+/-0.324) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-10}
0.683 (+/-0.296) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-09}
0.699 (+/-0.307) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-08}
0.555 (+/-0.126) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-07}
0.662 (+/-0.313) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-06}
0.656 (+/-0.317) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-05}
0.658 (+/-0.314) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 0.0001}
0.633 (+/-0.324) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-11}
0.600 (+/-0.245) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-10}
0.700 (+/-0.309) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-09}
0.699 (+/-0.307) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-08}
0.575 (+/-0.233) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-07}
0.662 (+/-0.313) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-06}
0.656 (+/-0.317) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-05}
0.658 (+/-0.313) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 0.0001}
0.633 (+/-0.323) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.700 (+/-0.309) for {'C': 0.1, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 1.0, 'kernel': 'linear'}
0.695 (+/-0.303) for {'C': 10.0, 'kernel': 'linear'}
0.658 (+/-0.314) for {'C': 100.0, 'kernel': 'linear'}
0.659 (+/-0.316) for {'C': 1000.0, 'kernel': 'linear'}
0.634 (+/-0.324) for {'C': 10000.0, 'kernel': 'linear'}
0.633 (+/-0.324) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.05      0.17      0.08         6
          1       0.99      0.97      0.98       623

avg / total       0.98      0.96      0.97       629

本轮grid search结果，得到最好的参数选择是： {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-05}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.7171453747217412
循环迭代之前，delta is: [8.41510681e+06 9.99000000e-06]
执行tunemodel()函数前，使用的grid search parameter是：
 [{'C': array([1000000.        , 1096478.19614319, 1202264.43461741,
       1318256.73855641, 1445439.77074593, 1584893.19246111,
       1737800.82874938, 1905460.71796325, 2089296.13085404,
       2290867.65276777, 2511886.43150958]), 'kernel': ['rbf'], 'gamma': array([1.00000000e-06, 1.58489319e-06, 2.51188643e-06, 3.98107171e-06,
       6.30957344e-06, 1.00000000e-05, 1.58489319e-05, 2.51188643e-05,
       3.98107171e-05, 6.30957344e-05, 1.00000000e-04])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.660 (+/-0.219) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.664 (+/-0.291) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.659 (+/-0.287) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.649 (+/-0.288) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.643 (+/-0.285) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.655 (+/-0.286) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.646 (+/-0.284) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.641 (+/-0.286) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.637 (+/-0.307) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.620 (+/-0.299) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.618 (+/-0.300) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.660 (+/-0.219) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.659 (+/-0.287) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.630 (+/-0.189) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.666 (+/-0.357) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.643 (+/-0.285) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.643 (+/-0.279) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.649 (+/-0.286) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.628 (+/-0.299) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.637 (+/-0.307) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.620 (+/-0.299) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.617 (+/-0.301) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.680 (+/-0.294) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.659 (+/-0.287) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.655 (+/-0.286) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.634 (+/-0.289) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.635 (+/-0.277) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.651 (+/-0.286) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.649 (+/-0.286) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.628 (+/-0.299) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.637 (+/-0.307) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.619 (+/-0.300) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.617 (+/-0.301) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.671 (+/-0.290) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.659 (+/-0.287) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.655 (+/-0.286) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.634 (+/-0.289) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.633 (+/-0.277) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.651 (+/-0.286) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.651 (+/-0.286) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.625 (+/-0.298) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.632 (+/-0.301) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.619 (+/-0.300) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.617 (+/-0.301) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.667 (+/-0.292) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.656 (+/-0.288) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.653 (+/-0.287) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.641 (+/-0.288) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.633 (+/-0.277) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.646 (+/-0.284) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.644 (+/-0.285) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.623 (+/-0.298) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.624 (+/-0.299) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.619 (+/-0.300) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.617 (+/-0.301) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.692 (+/-0.352) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.653 (+/-0.287) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.648 (+/-0.287) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.641 (+/-0.288) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.641 (+/-0.286) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.651 (+/-0.286) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.639 (+/-0.284) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.623 (+/-0.298) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.627 (+/-0.300) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.619 (+/-0.300) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.616 (+/-0.302) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.684 (+/-0.350) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.653 (+/-0.287) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.648 (+/-0.287) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.643 (+/-0.287) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.643 (+/-0.285) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.655 (+/-0.287) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.639 (+/-0.284) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.623 (+/-0.298) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.623 (+/-0.299) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.619 (+/-0.300) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.616 (+/-0.302) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.684 (+/-0.350) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.653 (+/-0.287) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.636 (+/-0.287) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.632 (+/-0.278) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.639 (+/-0.285) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.651 (+/-0.286) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.637 (+/-0.284) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.623 (+/-0.298) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.624 (+/-0.299) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.618 (+/-0.301) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.616 (+/-0.302) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.684 (+/-0.350) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.653 (+/-0.287) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.632 (+/-0.289) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.632 (+/-0.278) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.639 (+/-0.285) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.655 (+/-0.287) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.641 (+/-0.284) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.623 (+/-0.298) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.624 (+/-0.299) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.617 (+/-0.302) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.617 (+/-0.302) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.680 (+/-0.350) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.651 (+/-0.287) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.632 (+/-0.289) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.632 (+/-0.278) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.642 (+/-0.287) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.660 (+/-0.292) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.629 (+/-0.298) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.621 (+/-0.299) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.623 (+/-0.300) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.617 (+/-0.302) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.617 (+/-0.301) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.680 (+/-0.350) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.641 (+/-0.287) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.635 (+/-0.288) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.632 (+/-0.278) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.645 (+/-0.286) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.648 (+/-0.292) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.625 (+/-0.298) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.620 (+/-0.299) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.623 (+/-0.300) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.617 (+/-0.302) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.617 (+/-0.301) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.690 (+/-0.300) for {'C': 1.0, 'kernel': 'linear'}
0.666 (+/-0.357) for {'C': 10.0, 'kernel': 'linear'}
0.629 (+/-0.298) for {'C': 100.0, 'kernel': 'linear'}
0.617 (+/-0.301) for {'C': 1000.0, 'kernel': 'linear'}
0.608 (+/-0.309) for {'C': 10000.0, 'kernel': 'linear'}
0.607 (+/-0.309) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.698 (+/-0.306) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.698 (+/-0.306) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.698 (+/-0.306) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.697 (+/-0.305) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.697 (+/-0.304) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.697 (+/-0.306) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.697 (+/-0.305) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.680 (+/-0.290) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.664 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.662 (+/-0.313) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.662 (+/-0.312) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.698 (+/-0.306) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.698 (+/-0.306) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.697 (+/-0.305) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.696 (+/-0.304) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.697 (+/-0.304) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.697 (+/-0.306) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.697 (+/-0.305) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.663 (+/-0.311) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.663 (+/-0.316) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.662 (+/-0.313) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.661 (+/-0.313) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.698 (+/-0.306) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.698 (+/-0.306) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.698 (+/-0.305) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.696 (+/-0.304) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.697 (+/-0.304) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.697 (+/-0.306) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.697 (+/-0.305) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.663 (+/-0.311) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.663 (+/-0.316) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.661 (+/-0.313) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.661 (+/-0.313) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.698 (+/-0.306) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.698 (+/-0.306) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.698 (+/-0.305) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.696 (+/-0.304) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.696 (+/-0.304) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.697 (+/-0.306) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.697 (+/-0.306) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.663 (+/-0.312) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.663 (+/-0.316) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.661 (+/-0.314) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.661 (+/-0.313) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.698 (+/-0.306) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.698 (+/-0.306) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.697 (+/-0.305) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.697 (+/-0.305) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.696 (+/-0.304) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.697 (+/-0.304) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.680 (+/-0.293) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.663 (+/-0.312) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.663 (+/-0.314) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.661 (+/-0.315) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.661 (+/-0.313) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.698 (+/-0.306) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.697 (+/-0.305) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.697 (+/-0.305) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.697 (+/-0.305) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.696 (+/-0.304) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.697 (+/-0.306) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.680 (+/-0.292) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.663 (+/-0.312) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.663 (+/-0.315) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.661 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.661 (+/-0.313) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.698 (+/-0.306) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.697 (+/-0.305) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.697 (+/-0.305) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.697 (+/-0.305) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.697 (+/-0.305) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.697 (+/-0.306) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.680 (+/-0.292) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.663 (+/-0.313) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.662 (+/-0.315) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.661 (+/-0.316) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.661 (+/-0.313) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.698 (+/-0.306) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.697 (+/-0.305) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.696 (+/-0.303) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.696 (+/-0.305) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.696 (+/-0.305) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.697 (+/-0.305) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.680 (+/-0.293) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.663 (+/-0.313) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.662 (+/-0.315) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.660 (+/-0.315) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.661 (+/-0.313) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.698 (+/-0.306) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.697 (+/-0.305) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.696 (+/-0.303) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.696 (+/-0.305) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.696 (+/-0.305) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.697 (+/-0.306) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.680 (+/-0.293) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.663 (+/-0.313) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.662 (+/-0.315) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.660 (+/-0.315) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.661 (+/-0.313) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.698 (+/-0.305) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.697 (+/-0.304) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.696 (+/-0.303) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.696 (+/-0.305) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.697 (+/-0.305) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.697 (+/-0.307) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.663 (+/-0.313) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.662 (+/-0.312) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.662 (+/-0.316) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.660 (+/-0.315) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.661 (+/-0.313) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.698 (+/-0.305) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.697 (+/-0.304) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.696 (+/-0.304) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.696 (+/-0.305) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.696 (+/-0.306) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.681 (+/-0.294) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.663 (+/-0.312) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.662 (+/-0.313) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.662 (+/-0.316) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.660 (+/-0.315) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.661 (+/-0.313) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.699 (+/-0.307) for {'C': 1.0, 'kernel': 'linear'}
0.697 (+/-0.305) for {'C': 10.0, 'kernel': 'linear'}
0.663 (+/-0.314) for {'C': 100.0, 'kernel': 'linear'}
0.660 (+/-0.315) for {'C': 1000.0, 'kernel': 'linear'}
0.634 (+/-0.323) for {'C': 10000.0, 'kernel': 'linear'}
0.633 (+/-0.324) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6985561464259213
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.641 (+/-0.286) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.616 (+/-0.290) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.611 (+/-0.279) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.609 (+/-0.276) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.624 (+/-0.278) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.625 (+/-0.281) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.630 (+/-0.289) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.628 (+/-0.290) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.615 (+/-0.302) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.612 (+/-0.304) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.612 (+/-0.304) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.627 (+/-0.287) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.618 (+/-0.290) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.586 (+/-0.153) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.609 (+/-0.276) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.622 (+/-0.278) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.621 (+/-0.280) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.632 (+/-0.292) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.616 (+/-0.301) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.615 (+/-0.302) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.612 (+/-0.304) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.612 (+/-0.304) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.645 (+/-0.362) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.616 (+/-0.291) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.613 (+/-0.279) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.611 (+/-0.276) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.621 (+/-0.279) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.628 (+/-0.291) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.633 (+/-0.292) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.616 (+/-0.301) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.615 (+/-0.302) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.612 (+/-0.304) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.612 (+/-0.304) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.644 (+/-0.363) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.617 (+/-0.290) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.610 (+/-0.276) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.615 (+/-0.278) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.620 (+/-0.280) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.632 (+/-0.288) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.632 (+/-0.292) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.616 (+/-0.301) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.614 (+/-0.302) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.611 (+/-0.304) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.613 (+/-0.304) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.642 (+/-0.364) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.613 (+/-0.292) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.610 (+/-0.276) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.622 (+/-0.278) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.620 (+/-0.280) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.631 (+/-0.289) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.624 (+/-0.290) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.615 (+/-0.301) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.615 (+/-0.302) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.611 (+/-0.304) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.613 (+/-0.303) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.641 (+/-0.365) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.614 (+/-0.292) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.611 (+/-0.278) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.621 (+/-0.279) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.626 (+/-0.291) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.631 (+/-0.289) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.624 (+/-0.290) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.615 (+/-0.301) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.615 (+/-0.302) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.611 (+/-0.305) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.613 (+/-0.303) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.641 (+/-0.365) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.606 (+/-0.280) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.609 (+/-0.279) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.623 (+/-0.278) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.630 (+/-0.288) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.634 (+/-0.291) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.623 (+/-0.291) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.612 (+/-0.304) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.611 (+/-0.300) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.611 (+/-0.305) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.613 (+/-0.303) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.640 (+/-0.366) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.606 (+/-0.280) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.609 (+/-0.279) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.620 (+/-0.278) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.630 (+/-0.288) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.634 (+/-0.291) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.623 (+/-0.290) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.612 (+/-0.304) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.615 (+/-0.301) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.612 (+/-0.304) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.613 (+/-0.303) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.638 (+/-0.367) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.604 (+/-0.281) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.614 (+/-0.278) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.620 (+/-0.278) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.632 (+/-0.288) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.633 (+/-0.291) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.626 (+/-0.292) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.612 (+/-0.304) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.612 (+/-0.304) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.612 (+/-0.304) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.613 (+/-0.303) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.638 (+/-0.367) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.604 (+/-0.281) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.614 (+/-0.278) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.617 (+/-0.280) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.634 (+/-0.291) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.633 (+/-0.291) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.611 (+/-0.305) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.612 (+/-0.304) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.612 (+/-0.304) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.612 (+/-0.304) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.614 (+/-0.303) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.639 (+/-0.366) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.606 (+/-0.280) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.618 (+/-0.278) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.617 (+/-0.280) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.633 (+/-0.292) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.625 (+/-0.288) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.611 (+/-0.305) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.612 (+/-0.304) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.615 (+/-0.301) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.612 (+/-0.304) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.614 (+/-0.303) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.798 (+/-0.437) for {'C': 0.1, 'kernel': 'linear'}
0.647 (+/-0.212) for {'C': 1.0, 'kernel': 'linear'}
0.618 (+/-0.276) for {'C': 10.0, 'kernel': 'linear'}
0.612 (+/-0.304) for {'C': 100.0, 'kernel': 'linear'}
0.616 (+/-0.302) for {'C': 1000.0, 'kernel': 'linear'}
0.608 (+/-0.309) for {'C': 10000.0, 'kernel': 'linear'}
0.607 (+/-0.309) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.697 (+/-0.302) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.694 (+/-0.298) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.694 (+/-0.300) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.694 (+/-0.302) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.695 (+/-0.304) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.694 (+/-0.304) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.717 (+/-0.352) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.700 (+/-0.346) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.682 (+/-0.367) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.657 (+/-0.314) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.658 (+/-0.312) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.696 (+/-0.301) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.695 (+/-0.299) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.694 (+/-0.300) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.694 (+/-0.302) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.695 (+/-0.305) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.693 (+/-0.305) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.717 (+/-0.351) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.683 (+/-0.367) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.682 (+/-0.367) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.657 (+/-0.314) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.658 (+/-0.312) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.695 (+/-0.300) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.694 (+/-0.298) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.694 (+/-0.300) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.695 (+/-0.302) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.695 (+/-0.304) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.693 (+/-0.305) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.717 (+/-0.353) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.683 (+/-0.367) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.682 (+/-0.367) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.657 (+/-0.314) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.658 (+/-0.311) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.695 (+/-0.300) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.694 (+/-0.298) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.694 (+/-0.301) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.695 (+/-0.302) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.694 (+/-0.304) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.718 (+/-0.353) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.717 (+/-0.352) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.683 (+/-0.368) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.682 (+/-0.366) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.657 (+/-0.313) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.658 (+/-0.312) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.695 (+/-0.300) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.693 (+/-0.296) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.694 (+/-0.301) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.695 (+/-0.304) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.694 (+/-0.304) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.717 (+/-0.353) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.699 (+/-0.345) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.682 (+/-0.368) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.682 (+/-0.367) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.657 (+/-0.313) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.659 (+/-0.313) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.695 (+/-0.300) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.693 (+/-0.296) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.694 (+/-0.301) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.695 (+/-0.304) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.694 (+/-0.304) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.717 (+/-0.353) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.699 (+/-0.346) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.682 (+/-0.368) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.682 (+/-0.367) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.657 (+/-0.313) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.659 (+/-0.312) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.695 (+/-0.300) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.693 (+/-0.296) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.694 (+/-0.300) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.695 (+/-0.304) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.718 (+/-0.352) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.717 (+/-0.355) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.698 (+/-0.345) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.657 (+/-0.314) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.682 (+/-0.368) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.657 (+/-0.312) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.659 (+/-0.312) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.694 (+/-0.299) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.693 (+/-0.296) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.694 (+/-0.302) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.695 (+/-0.304) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.718 (+/-0.352) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.717 (+/-0.355) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.698 (+/-0.347) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.657 (+/-0.314) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.682 (+/-0.368) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.657 (+/-0.314) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.659 (+/-0.312) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.694 (+/-0.299) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.693 (+/-0.296) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.694 (+/-0.304) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.694 (+/-0.304) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.718 (+/-0.352) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.717 (+/-0.354) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.698 (+/-0.346) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.657 (+/-0.314) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.657 (+/-0.314) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.657 (+/-0.314) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.659 (+/-0.312) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.694 (+/-0.300) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.693 (+/-0.296) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.694 (+/-0.304) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.694 (+/-0.305) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.718 (+/-0.352) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.717 (+/-0.355) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.656 (+/-0.313) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.657 (+/-0.314) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.657 (+/-0.314) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.657 (+/-0.313) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.659 (+/-0.312) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.694 (+/-0.299) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.693 (+/-0.296) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.695 (+/-0.305) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.694 (+/-0.305) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.717 (+/-0.352) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.700 (+/-0.347) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.656 (+/-0.313) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.657 (+/-0.314) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.683 (+/-0.367) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.657 (+/-0.313) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.659 (+/-0.312) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.700 (+/-0.309) for {'C': 0.1, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 1.0, 'kernel': 'linear'}
0.695 (+/-0.303) for {'C': 10.0, 'kernel': 'linear'}
0.658 (+/-0.314) for {'C': 100.0, 'kernel': 'linear'}
0.659 (+/-0.316) for {'C': 1000.0, 'kernel': 'linear'}
0.634 (+/-0.324) for {'C': 10000.0, 'kernel': 'linear'}
0.633 (+/-0.324) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.06      0.17      0.09         6
          1       0.99      0.98      0.98       623

avg / total       0.98      0.97      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.7182671695935362
第0轮loop中，tunemodel函数得到的最优参数是： ([{'C': array([1584893.19246111, 1614358.55682649, 1644371.72321493,
       1674942.87602644, 1706082.38900313, 1737800.82874938,
       1770108.95831742, 1803017.74085957, 1836538.34334835,
       1870682.1403658 , 1905460.71796325]), 'kernel': ['rbf'], 'gamma': array([3.98107171e-06, 4.36515832e-06, 4.78630092e-06, 5.24807460e-06,
       5.75439937e-06, 6.30957344e-06, 6.91830971e-06, 7.58577575e-06,
       8.31763771e-06, 9.12010839e-06, 1.00000000e-05])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}], {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}, 0.7182671695935362)
这是第0次迭代微调C和gamma。
第0次迭代，得到delta： [1.52907636e+05 3.69042656e-06]
执行tunemodel()函数前，使用的grid search parameter是：
 [{'C': array([1584893.19246111, 1614358.55682649, 1644371.72321493,
       1674942.87602644, 1706082.38900313, 1737800.82874938,
       1770108.95831742, 1803017.74085957, 1836538.34334835,
       1870682.1403658 , 1905460.71796325]), 'kernel': ['rbf'], 'gamma': array([3.98107171e-06, 4.36515832e-06, 4.78630092e-06, 5.24807460e-06,
       5.75439937e-06, 6.30957344e-06, 6.91830971e-06, 7.58577575e-06,
       8.31763771e-06, 9.12010839e-06, 1.00000000e-05])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.641 (+/-0.288) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.628 (+/-0.279) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.643 (+/-0.285) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.639 (+/-0.287) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.658 (+/-0.290) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.641 (+/-0.286) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.633 (+/-0.276) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.651 (+/-0.292) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.639 (+/-0.287) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.630 (+/-0.279) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.651 (+/-0.286) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.641 (+/-0.288) for {'C': 1614358.5568264863, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.628 (+/-0.279) for {'C': 1614358.5568264863, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.641 (+/-0.286) for {'C': 1614358.5568264863, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.641 (+/-0.286) for {'C': 1614358.5568264863, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.683 (+/-0.354) for {'C': 1614358.5568264863, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.641 (+/-0.286) for {'C': 1614358.5568264863, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.633 (+/-0.276) for {'C': 1614358.5568264863, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.652 (+/-0.292) for {'C': 1614358.5568264863, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.638 (+/-0.287) for {'C': 1614358.5568264863, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.630 (+/-0.279) for {'C': 1614358.5568264863, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.651 (+/-0.286) for {'C': 1614358.5568264863, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.641 (+/-0.288) for {'C': 1644371.7232149309, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.628 (+/-0.279) for {'C': 1644371.7232149309, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.643 (+/-0.285) for {'C': 1644371.7232149309, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.641 (+/-0.286) for {'C': 1644371.7232149309, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.683 (+/-0.354) for {'C': 1644371.7232149309, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.643 (+/-0.285) for {'C': 1644371.7232149309, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.633 (+/-0.276) for {'C': 1644371.7232149309, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.643 (+/-0.285) for {'C': 1644371.7232149309, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.638 (+/-0.287) for {'C': 1644371.7232149309, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.638 (+/-0.288) for {'C': 1644371.7232149309, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.651 (+/-0.286) for {'C': 1644371.7232149309, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.641 (+/-0.288) for {'C': 1674942.8760264402, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.628 (+/-0.279) for {'C': 1674942.8760264402, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.643 (+/-0.285) for {'C': 1674942.8760264402, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.641 (+/-0.286) for {'C': 1674942.8760264402, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.666 (+/-0.305) for {'C': 1674942.8760264402, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.643 (+/-0.285) for {'C': 1674942.8760264402, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.637 (+/-0.277) for {'C': 1674942.8760264402, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.642 (+/-0.286) for {'C': 1674942.8760264402, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.638 (+/-0.287) for {'C': 1674942.8760264402, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.636 (+/-0.289) for {'C': 1674942.8760264402, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.651 (+/-0.286) for {'C': 1674942.8760264402, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.643 (+/-0.287) for {'C': 1706082.3890031257, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.628 (+/-0.279) for {'C': 1706082.3890031257, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.643 (+/-0.285) for {'C': 1706082.3890031257, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.641 (+/-0.286) for {'C': 1706082.3890031257, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.666 (+/-0.305) for {'C': 1706082.3890031257, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.643 (+/-0.285) for {'C': 1706082.3890031257, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.635 (+/-0.277) for {'C': 1706082.3890031257, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.642 (+/-0.286) for {'C': 1706082.3890031257, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.638 (+/-0.287) for {'C': 1706082.3890031257, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.636 (+/-0.288) for {'C': 1706082.3890031257, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.651 (+/-0.286) for {'C': 1706082.3890031257, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.643 (+/-0.287) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.628 (+/-0.279) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.645 (+/-0.286) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.641 (+/-0.286) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.669 (+/-0.305) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.643 (+/-0.285) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.635 (+/-0.277) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.644 (+/-0.285) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.634 (+/-0.287) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.636 (+/-0.288) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.655 (+/-0.287) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.643 (+/-0.287) for {'C': 1770108.958317422, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.628 (+/-0.279) for {'C': 1770108.958317422, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.643 (+/-0.285) for {'C': 1770108.958317422, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.641 (+/-0.286) for {'C': 1770108.958317422, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.694 (+/-0.355) for {'C': 1770108.958317422, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.643 (+/-0.285) for {'C': 1770108.958317422, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.634 (+/-0.277) for {'C': 1770108.958317422, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.640 (+/-0.286) for {'C': 1770108.958317422, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.634 (+/-0.287) for {'C': 1770108.958317422, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.636 (+/-0.289) for {'C': 1770108.958317422, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.651 (+/-0.286) for {'C': 1770108.958317422, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.635 (+/-0.279) for {'C': 1803017.7408595693, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.628 (+/-0.279) for {'C': 1803017.7408595693, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.643 (+/-0.285) for {'C': 1803017.7408595693, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.641 (+/-0.286) for {'C': 1803017.7408595693, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.694 (+/-0.355) for {'C': 1803017.7408595693, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.643 (+/-0.285) for {'C': 1803017.7408595693, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.634 (+/-0.277) for {'C': 1803017.7408595693, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.642 (+/-0.286) for {'C': 1803017.7408595693, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.635 (+/-0.286) for {'C': 1803017.7408595693, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.636 (+/-0.289) for {'C': 1803017.7408595693, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.651 (+/-0.286) for {'C': 1803017.7408595693, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.635 (+/-0.279) for {'C': 1836538.3433483506, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.624 (+/-0.278) for {'C': 1836538.3433483506, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.646 (+/-0.284) for {'C': 1836538.3433483506, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.645 (+/-0.286) for {'C': 1836538.3433483506, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.694 (+/-0.355) for {'C': 1836538.3433483506, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.643 (+/-0.285) for {'C': 1836538.3433483506, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.634 (+/-0.277) for {'C': 1836538.3433483506, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.642 (+/-0.286) for {'C': 1836538.3433483506, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.635 (+/-0.286) for {'C': 1836538.3433483506, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.636 (+/-0.289) for {'C': 1836538.3433483506, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.651 (+/-0.286) for {'C': 1836538.3433483506, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.635 (+/-0.279) for {'C': 1870682.140365804, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.624 (+/-0.278) for {'C': 1870682.140365804, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.644 (+/-0.285) for {'C': 1870682.140365804, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.637 (+/-0.278) for {'C': 1870682.140365804, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.694 (+/-0.355) for {'C': 1870682.140365804, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.639 (+/-0.285) for {'C': 1870682.140365804, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.645 (+/-0.287) for {'C': 1870682.140365804, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.647 (+/-0.284) for {'C': 1870682.140365804, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.634 (+/-0.287) for {'C': 1870682.140365804, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.636 (+/-0.289) for {'C': 1870682.140365804, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.651 (+/-0.286) for {'C': 1870682.140365804, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.632 (+/-0.278) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.624 (+/-0.278) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.644 (+/-0.285) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.637 (+/-0.278) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.694 (+/-0.355) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.639 (+/-0.285) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.645 (+/-0.287) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.647 (+/-0.284) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.634 (+/-0.287) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.636 (+/-0.289) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.651 (+/-0.286) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.690 (+/-0.300) for {'C': 1.0, 'kernel': 'linear'}
0.666 (+/-0.357) for {'C': 10.0, 'kernel': 'linear'}
0.629 (+/-0.298) for {'C': 100.0, 'kernel': 'linear'}
0.617 (+/-0.301) for {'C': 1000.0, 'kernel': 'linear'}
0.608 (+/-0.309) for {'C': 10000.0, 'kernel': 'linear'}
0.607 (+/-0.309) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.697 (+/-0.305) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.696 (+/-0.301) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.697 (+/-0.304) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.696 (+/-0.302) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.697 (+/-0.306) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.696 (+/-0.304) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.697 (+/-0.304) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.697 (+/-0.303) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.696 (+/-0.303) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.695 (+/-0.301) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.697 (+/-0.306) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.697 (+/-0.305) for {'C': 1614358.5568264863, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.696 (+/-0.301) for {'C': 1614358.5568264863, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.697 (+/-0.304) for {'C': 1614358.5568264863, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.697 (+/-0.302) for {'C': 1614358.5568264863, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.698 (+/-0.307) for {'C': 1614358.5568264863, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.696 (+/-0.304) for {'C': 1614358.5568264863, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.697 (+/-0.304) for {'C': 1614358.5568264863, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.697 (+/-0.303) for {'C': 1614358.5568264863, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.696 (+/-0.302) for {'C': 1614358.5568264863, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.695 (+/-0.301) for {'C': 1614358.5568264863, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.697 (+/-0.306) for {'C': 1614358.5568264863, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.696 (+/-0.305) for {'C': 1644371.7232149309, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.696 (+/-0.301) for {'C': 1644371.7232149309, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.697 (+/-0.304) for {'C': 1644371.7232149309, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.697 (+/-0.302) for {'C': 1644371.7232149309, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.698 (+/-0.307) for {'C': 1644371.7232149309, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.697 (+/-0.305) for {'C': 1644371.7232149309, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.697 (+/-0.304) for {'C': 1644371.7232149309, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.696 (+/-0.303) for {'C': 1644371.7232149309, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.696 (+/-0.302) for {'C': 1644371.7232149309, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.696 (+/-0.301) for {'C': 1644371.7232149309, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.697 (+/-0.306) for {'C': 1644371.7232149309, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.696 (+/-0.305) for {'C': 1674942.8760264402, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.696 (+/-0.301) for {'C': 1674942.8760264402, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.697 (+/-0.304) for {'C': 1674942.8760264402, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.697 (+/-0.302) for {'C': 1674942.8760264402, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.698 (+/-0.307) for {'C': 1674942.8760264402, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.697 (+/-0.305) for {'C': 1674942.8760264402, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.697 (+/-0.304) for {'C': 1674942.8760264402, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.696 (+/-0.302) for {'C': 1674942.8760264402, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.696 (+/-0.302) for {'C': 1674942.8760264402, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.695 (+/-0.301) for {'C': 1674942.8760264402, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.697 (+/-0.306) for {'C': 1674942.8760264402, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.697 (+/-0.305) for {'C': 1706082.3890031257, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.696 (+/-0.301) for {'C': 1706082.3890031257, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.697 (+/-0.304) for {'C': 1706082.3890031257, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.697 (+/-0.302) for {'C': 1706082.3890031257, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.698 (+/-0.307) for {'C': 1706082.3890031257, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.697 (+/-0.305) for {'C': 1706082.3890031257, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.697 (+/-0.304) for {'C': 1706082.3890031257, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.696 (+/-0.302) for {'C': 1706082.3890031257, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.696 (+/-0.302) for {'C': 1706082.3890031257, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.696 (+/-0.301) for {'C': 1706082.3890031257, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.697 (+/-0.306) for {'C': 1706082.3890031257, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.697 (+/-0.305) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.696 (+/-0.301) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.697 (+/-0.305) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.697 (+/-0.302) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.698 (+/-0.307) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.697 (+/-0.305) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.697 (+/-0.304) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.696 (+/-0.304) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.696 (+/-0.302) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.696 (+/-0.301) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.697 (+/-0.306) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.697 (+/-0.305) for {'C': 1770108.958317422, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.696 (+/-0.301) for {'C': 1770108.958317422, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.697 (+/-0.305) for {'C': 1770108.958317422, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.697 (+/-0.302) for {'C': 1770108.958317422, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.698 (+/-0.308) for {'C': 1770108.958317422, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.697 (+/-0.305) for {'C': 1770108.958317422, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.696 (+/-0.303) for {'C': 1770108.958317422, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.696 (+/-0.302) for {'C': 1770108.958317422, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.696 (+/-0.302) for {'C': 1770108.958317422, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.695 (+/-0.301) for {'C': 1770108.958317422, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.697 (+/-0.305) for {'C': 1770108.958317422, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.696 (+/-0.305) for {'C': 1803017.7408595693, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.696 (+/-0.301) for {'C': 1803017.7408595693, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.697 (+/-0.305) for {'C': 1803017.7408595693, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.697 (+/-0.302) for {'C': 1803017.7408595693, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.698 (+/-0.308) for {'C': 1803017.7408595693, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.697 (+/-0.305) for {'C': 1803017.7408595693, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.696 (+/-0.303) for {'C': 1803017.7408595693, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.696 (+/-0.303) for {'C': 1803017.7408595693, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.696 (+/-0.303) for {'C': 1803017.7408595693, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.695 (+/-0.301) for {'C': 1803017.7408595693, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.697 (+/-0.305) for {'C': 1803017.7408595693, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.696 (+/-0.305) for {'C': 1836538.3433483506, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.695 (+/-0.301) for {'C': 1836538.3433483506, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.697 (+/-0.305) for {'C': 1836538.3433483506, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.697 (+/-0.302) for {'C': 1836538.3433483506, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.698 (+/-0.308) for {'C': 1836538.3433483506, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.697 (+/-0.305) for {'C': 1836538.3433483506, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.696 (+/-0.303) for {'C': 1836538.3433483506, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.696 (+/-0.303) for {'C': 1836538.3433483506, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.696 (+/-0.303) for {'C': 1836538.3433483506, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.695 (+/-0.301) for {'C': 1836538.3433483506, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.697 (+/-0.305) for {'C': 1836538.3433483506, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.696 (+/-0.305) for {'C': 1870682.140365804, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.695 (+/-0.301) for {'C': 1870682.140365804, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.697 (+/-0.304) for {'C': 1870682.140365804, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.697 (+/-0.302) for {'C': 1870682.140365804, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.698 (+/-0.308) for {'C': 1870682.140365804, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.696 (+/-0.305) for {'C': 1870682.140365804, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.697 (+/-0.303) for {'C': 1870682.140365804, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.697 (+/-0.305) for {'C': 1870682.140365804, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.696 (+/-0.302) for {'C': 1870682.140365804, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.695 (+/-0.301) for {'C': 1870682.140365804, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.697 (+/-0.305) for {'C': 1870682.140365804, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.696 (+/-0.305) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.695 (+/-0.301) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.697 (+/-0.304) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.697 (+/-0.302) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.698 (+/-0.308) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.696 (+/-0.305) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.697 (+/-0.303) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.697 (+/-0.305) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.696 (+/-0.302) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.695 (+/-0.301) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.697 (+/-0.305) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.699 (+/-0.307) for {'C': 1.0, 'kernel': 'linear'}
0.697 (+/-0.305) for {'C': 10.0, 'kernel': 'linear'}
0.663 (+/-0.314) for {'C': 100.0, 'kernel': 'linear'}
0.660 (+/-0.315) for {'C': 1000.0, 'kernel': 'linear'}
0.634 (+/-0.323) for {'C': 10000.0, 'kernel': 'linear'}
0.633 (+/-0.324) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6985561464259213
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.621 (+/-0.279) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.621 (+/-0.279) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.625 (+/-0.278) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.621 (+/-0.279) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.627 (+/-0.279) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.626 (+/-0.291) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.620 (+/-0.281) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.627 (+/-0.291) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.623 (+/-0.294) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.623 (+/-0.283) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.631 (+/-0.289) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.621 (+/-0.279) for {'C': 1614358.5568264863, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.621 (+/-0.279) for {'C': 1614358.5568264863, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.625 (+/-0.278) for {'C': 1614358.5568264863, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.624 (+/-0.278) for {'C': 1614358.5568264863, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.625 (+/-0.280) for {'C': 1614358.5568264863, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.627 (+/-0.291) for {'C': 1614358.5568264863, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.620 (+/-0.281) for {'C': 1614358.5568264863, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.629 (+/-0.291) for {'C': 1614358.5568264863, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.623 (+/-0.294) for {'C': 1614358.5568264863, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.623 (+/-0.283) for {'C': 1614358.5568264863, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.631 (+/-0.289) for {'C': 1614358.5568264863, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.621 (+/-0.279) for {'C': 1644371.7232149309, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.621 (+/-0.279) for {'C': 1644371.7232149309, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.625 (+/-0.278) for {'C': 1644371.7232149309, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.622 (+/-0.279) for {'C': 1644371.7232149309, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.627 (+/-0.279) for {'C': 1644371.7232149309, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.627 (+/-0.291) for {'C': 1644371.7232149309, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.620 (+/-0.281) for {'C': 1644371.7232149309, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.628 (+/-0.293) for {'C': 1644371.7232149309, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.623 (+/-0.294) for {'C': 1644371.7232149309, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.631 (+/-0.293) for {'C': 1644371.7232149309, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.631 (+/-0.289) for {'C': 1644371.7232149309, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.621 (+/-0.279) for {'C': 1674942.8760264402, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.621 (+/-0.279) for {'C': 1674942.8760264402, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.625 (+/-0.278) for {'C': 1674942.8760264402, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.622 (+/-0.279) for {'C': 1674942.8760264402, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.627 (+/-0.279) for {'C': 1674942.8760264402, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.627 (+/-0.291) for {'C': 1674942.8760264402, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.624 (+/-0.282) for {'C': 1674942.8760264402, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.627 (+/-0.294) for {'C': 1674942.8760264402, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.623 (+/-0.294) for {'C': 1674942.8760264402, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.630 (+/-0.293) for {'C': 1674942.8760264402, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.631 (+/-0.289) for {'C': 1674942.8760264402, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.623 (+/-0.278) for {'C': 1706082.3890031257, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.621 (+/-0.279) for {'C': 1706082.3890031257, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.625 (+/-0.278) for {'C': 1706082.3890031257, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.622 (+/-0.279) for {'C': 1706082.3890031257, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.626 (+/-0.280) for {'C': 1706082.3890031257, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.630 (+/-0.288) for {'C': 1706082.3890031257, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.624 (+/-0.282) for {'C': 1706082.3890031257, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.628 (+/-0.293) for {'C': 1706082.3890031257, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.623 (+/-0.294) for {'C': 1706082.3890031257, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.630 (+/-0.294) for {'C': 1706082.3890031257, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.631 (+/-0.289) for {'C': 1706082.3890031257, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.623 (+/-0.278) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.621 (+/-0.279) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.625 (+/-0.278) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.622 (+/-0.279) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.628 (+/-0.281) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.630 (+/-0.288) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.624 (+/-0.283) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.628 (+/-0.293) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.623 (+/-0.294) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.630 (+/-0.294) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.634 (+/-0.291) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.623 (+/-0.278) for {'C': 1770108.958317422, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.621 (+/-0.279) for {'C': 1770108.958317422, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.625 (+/-0.278) for {'C': 1770108.958317422, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.622 (+/-0.279) for {'C': 1770108.958317422, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.635 (+/-0.290) for {'C': 1770108.958317422, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.630 (+/-0.288) for {'C': 1770108.958317422, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.624 (+/-0.283) for {'C': 1770108.958317422, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.625 (+/-0.294) for {'C': 1770108.958317422, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.623 (+/-0.294) for {'C': 1770108.958317422, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.630 (+/-0.294) for {'C': 1770108.958317422, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.634 (+/-0.291) for {'C': 1770108.958317422, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.623 (+/-0.278) for {'C': 1803017.7408595693, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.621 (+/-0.279) for {'C': 1803017.7408595693, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.625 (+/-0.278) for {'C': 1803017.7408595693, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.622 (+/-0.279) for {'C': 1803017.7408595693, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.634 (+/-0.290) for {'C': 1803017.7408595693, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.630 (+/-0.288) for {'C': 1803017.7408595693, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.624 (+/-0.283) for {'C': 1803017.7408595693, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.627 (+/-0.294) for {'C': 1803017.7408595693, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.623 (+/-0.294) for {'C': 1803017.7408595693, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.630 (+/-0.294) for {'C': 1803017.7408595693, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.634 (+/-0.291) for {'C': 1803017.7408595693, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.623 (+/-0.278) for {'C': 1836538.3433483506, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.621 (+/-0.279) for {'C': 1836538.3433483506, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.628 (+/-0.277) for {'C': 1836538.3433483506, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.627 (+/-0.280) for {'C': 1836538.3433483506, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.633 (+/-0.291) for {'C': 1836538.3433483506, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.626 (+/-0.292) for {'C': 1836538.3433483506, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.624 (+/-0.283) for {'C': 1836538.3433483506, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.627 (+/-0.294) for {'C': 1836538.3433483506, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.623 (+/-0.294) for {'C': 1836538.3433483506, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.629 (+/-0.294) for {'C': 1836538.3433483506, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.634 (+/-0.291) for {'C': 1836538.3433483506, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.620 (+/-0.278) for {'C': 1870682.140365804, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.621 (+/-0.279) for {'C': 1870682.140365804, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.625 (+/-0.278) for {'C': 1870682.140365804, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.627 (+/-0.280) for {'C': 1870682.140365804, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.633 (+/-0.291) for {'C': 1870682.140365804, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.630 (+/-0.288) for {'C': 1870682.140365804, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.636 (+/-0.294) for {'C': 1870682.140365804, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.627 (+/-0.294) for {'C': 1870682.140365804, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.623 (+/-0.294) for {'C': 1870682.140365804, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.630 (+/-0.294) for {'C': 1870682.140365804, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.634 (+/-0.291) for {'C': 1870682.140365804, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.620 (+/-0.278) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.620 (+/-0.280) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.625 (+/-0.278) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.627 (+/-0.280) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.633 (+/-0.291) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.630 (+/-0.288) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.636 (+/-0.294) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.627 (+/-0.294) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.623 (+/-0.294) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.629 (+/-0.294) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.634 (+/-0.291) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.798 (+/-0.437) for {'C': 0.1, 'kernel': 'linear'}
0.647 (+/-0.212) for {'C': 1.0, 'kernel': 'linear'}
0.618 (+/-0.276) for {'C': 10.0, 'kernel': 'linear'}
0.612 (+/-0.304) for {'C': 100.0, 'kernel': 'linear'}
0.616 (+/-0.302) for {'C': 1000.0, 'kernel': 'linear'}
0.608 (+/-0.309) for {'C': 10000.0, 'kernel': 'linear'}
0.607 (+/-0.309) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.695 (+/-0.304) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.694 (+/-0.303) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.695 (+/-0.304) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.695 (+/-0.304) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.694 (+/-0.307) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.694 (+/-0.304) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.693 (+/-0.306) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.693 (+/-0.303) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.692 (+/-0.305) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.693 (+/-0.304) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.717 (+/-0.353) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.695 (+/-0.304) for {'C': 1614358.5568264863, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.694 (+/-0.303) for {'C': 1614358.5568264863, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.695 (+/-0.304) for {'C': 1614358.5568264863, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.695 (+/-0.304) for {'C': 1614358.5568264863, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.694 (+/-0.306) for {'C': 1614358.5568264863, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.694 (+/-0.305) for {'C': 1614358.5568264863, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.693 (+/-0.306) for {'C': 1614358.5568264863, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.693 (+/-0.303) for {'C': 1614358.5568264863, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.692 (+/-0.305) for {'C': 1614358.5568264863, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.693 (+/-0.304) for {'C': 1614358.5568264863, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.717 (+/-0.353) for {'C': 1614358.5568264863, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.695 (+/-0.304) for {'C': 1644371.7232149309, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.694 (+/-0.303) for {'C': 1644371.7232149309, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.695 (+/-0.304) for {'C': 1644371.7232149309, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.695 (+/-0.304) for {'C': 1644371.7232149309, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.694 (+/-0.307) for {'C': 1644371.7232149309, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.694 (+/-0.305) for {'C': 1644371.7232149309, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.693 (+/-0.306) for {'C': 1644371.7232149309, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.693 (+/-0.303) for {'C': 1644371.7232149309, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.692 (+/-0.305) for {'C': 1644371.7232149309, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.693 (+/-0.304) for {'C': 1644371.7232149309, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.717 (+/-0.354) for {'C': 1644371.7232149309, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.695 (+/-0.304) for {'C': 1674942.8760264402, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.694 (+/-0.303) for {'C': 1674942.8760264402, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.695 (+/-0.304) for {'C': 1674942.8760264402, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.695 (+/-0.304) for {'C': 1674942.8760264402, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.694 (+/-0.307) for {'C': 1674942.8760264402, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.694 (+/-0.305) for {'C': 1674942.8760264402, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.693 (+/-0.306) for {'C': 1674942.8760264402, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.693 (+/-0.303) for {'C': 1674942.8760264402, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.692 (+/-0.305) for {'C': 1674942.8760264402, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.693 (+/-0.304) for {'C': 1674942.8760264402, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.717 (+/-0.354) for {'C': 1674942.8760264402, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.695 (+/-0.304) for {'C': 1706082.3890031257, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.694 (+/-0.303) for {'C': 1706082.3890031257, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.695 (+/-0.304) for {'C': 1706082.3890031257, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.695 (+/-0.304) for {'C': 1706082.3890031257, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.694 (+/-0.307) for {'C': 1706082.3890031257, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.718 (+/-0.352) for {'C': 1706082.3890031257, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.693 (+/-0.306) for {'C': 1706082.3890031257, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.693 (+/-0.303) for {'C': 1706082.3890031257, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.692 (+/-0.305) for {'C': 1706082.3890031257, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.693 (+/-0.304) for {'C': 1706082.3890031257, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.717 (+/-0.354) for {'C': 1706082.3890031257, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.695 (+/-0.304) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.694 (+/-0.304) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.695 (+/-0.304) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.695 (+/-0.304) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.694 (+/-0.307) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.718 (+/-0.352) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.693 (+/-0.306) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.693 (+/-0.303) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.692 (+/-0.305) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.693 (+/-0.304) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.717 (+/-0.355) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.695 (+/-0.304) for {'C': 1770108.958317422, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.694 (+/-0.303) for {'C': 1770108.958317422, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.695 (+/-0.304) for {'C': 1770108.958317422, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.695 (+/-0.304) for {'C': 1770108.958317422, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.694 (+/-0.306) for {'C': 1770108.958317422, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.718 (+/-0.352) for {'C': 1770108.958317422, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.693 (+/-0.306) for {'C': 1770108.958317422, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.692 (+/-0.303) for {'C': 1770108.958317422, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.692 (+/-0.305) for {'C': 1770108.958317422, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.692 (+/-0.303) for {'C': 1770108.958317422, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.717 (+/-0.355) for {'C': 1770108.958317422, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.695 (+/-0.304) for {'C': 1803017.7408595693, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.694 (+/-0.303) for {'C': 1803017.7408595693, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.695 (+/-0.304) for {'C': 1803017.7408595693, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.695 (+/-0.304) for {'C': 1803017.7408595693, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.694 (+/-0.306) for {'C': 1803017.7408595693, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.718 (+/-0.351) for {'C': 1803017.7408595693, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.693 (+/-0.306) for {'C': 1803017.7408595693, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.692 (+/-0.304) for {'C': 1803017.7408595693, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.692 (+/-0.305) for {'C': 1803017.7408595693, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.692 (+/-0.303) for {'C': 1803017.7408595693, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.717 (+/-0.355) for {'C': 1803017.7408595693, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.695 (+/-0.304) for {'C': 1836538.3433483506, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.694 (+/-0.304) for {'C': 1836538.3433483506, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.695 (+/-0.304) for {'C': 1836538.3433483506, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.695 (+/-0.304) for {'C': 1836538.3433483506, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.694 (+/-0.306) for {'C': 1836538.3433483506, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.693 (+/-0.305) for {'C': 1836538.3433483506, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.693 (+/-0.306) for {'C': 1836538.3433483506, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.692 (+/-0.304) for {'C': 1836538.3433483506, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.692 (+/-0.305) for {'C': 1836538.3433483506, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.692 (+/-0.303) for {'C': 1836538.3433483506, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.717 (+/-0.355) for {'C': 1836538.3433483506, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.695 (+/-0.304) for {'C': 1870682.140365804, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.694 (+/-0.304) for {'C': 1870682.140365804, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.695 (+/-0.304) for {'C': 1870682.140365804, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.695 (+/-0.304) for {'C': 1870682.140365804, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.694 (+/-0.306) for {'C': 1870682.140365804, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.718 (+/-0.352) for {'C': 1870682.140365804, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.693 (+/-0.307) for {'C': 1870682.140365804, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.692 (+/-0.304) for {'C': 1870682.140365804, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.692 (+/-0.305) for {'C': 1870682.140365804, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.692 (+/-0.303) for {'C': 1870682.140365804, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.717 (+/-0.355) for {'C': 1870682.140365804, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.695 (+/-0.304) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.694 (+/-0.304) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.695 (+/-0.305) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.695 (+/-0.304) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.694 (+/-0.306) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.718 (+/-0.352) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.693 (+/-0.307) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.692 (+/-0.304) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.691 (+/-0.305) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.692 (+/-0.303) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.717 (+/-0.355) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.700 (+/-0.309) for {'C': 0.1, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 1.0, 'kernel': 'linear'}
0.695 (+/-0.303) for {'C': 10.0, 'kernel': 'linear'}
0.658 (+/-0.314) for {'C': 100.0, 'kernel': 'linear'}
0.659 (+/-0.316) for {'C': 1000.0, 'kernel': 'linear'}
0.634 (+/-0.324) for {'C': 10000.0, 'kernel': 'linear'}
0.633 (+/-0.324) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.06      0.17      0.09         6
          1       0.99      0.98      0.98       623

avg / total       0.98      0.97      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 1803017.7408595693, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.7184274260037926
第1轮loop中，tunemodel函数得到的最优参数是： ([{'C': array([1770108.95831742, 1776642.30820362, 1783199.77223292,
       1789781.43940896, 1796387.39906389, 1803017.74085957,
       1809672.55478879, 1816351.93117652, 1823055.96068107,
       1829784.73429542, 1836538.34334835]), 'kernel': ['rbf'], 'gamma': array([5.75439937e-06, 5.86138165e-06, 5.97035287e-06, 6.08135001e-06,
       6.19441075e-06, 6.30957344e-06, 6.42687717e-06, 6.54636174e-06,
       6.66806769e-06, 6.79203633e-06, 6.91830971e-06])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}], {'C': 1803017.7408595693, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}, 0.7184274260037926)
这是第1次迭代微调C和gamma。
第1次迭代，得到delta： [65216.91211019     0.        ]
SVC模型clf_op_iter的参数是： {'kernel': 'rbf', 'decision_function_shape': 'ovr', 'class_weight': {-1: 15}, 'tol': 0.001, 'gamma': 6.309573444801928e-06, 'random_state': 0, 'cache_size': 200, 'verbose': False, 'degree': 3, 'C': 1803017.7408595693, 'probability': False, 'shrinking': True, 'coef0': 0.0, 'max_iter': -1}

训练模型SVC对训练样本的预测准确率： 0.9532246633593197

测试集中，预测为舞弊样本的有： (array([   0,    1,    2, ..., 1254, 1255, 1256], dtype=int64),) 
测试集中，实际为舞弊样本的有： (array([1246, 1247, 1248, 1249, 1250, 1251, 1252, 1253, 1254, 1255, 1256],
      dtype=int64),) 
预测的舞弊样本数目： 1126

训练模型SVC对测试样本的预测准确率： 0.19844082211197733
以上是第38次特征筛选。
第38次特征筛选，AUC值是： 0.5067123887348606
X_train_iter_svc.shape is: (1257, 14)
X_test_iter_svc.shape is: (1257, 14)
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.722 (+/-0.473) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.638 (+/-0.382) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.646 (+/-0.204) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.613 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.698 (+/-0.306) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.17      0.17      0.17         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6979146054909721
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.635 (+/-0.198) for {'C': 1.0, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.589 (+/-0.194) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.14      0.17      0.15         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.698 (+/-0.305) for {'C': 1.0, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.659 (+/-0.314) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.14      0.17      0.15         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.697594607964383
RBF的粗grid search最佳超参： {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001} RBF的粗grid search最高分值： 0.6979146054909721
linear的粗grid search最佳超参： {'C': 1.0, 'kernel': 'linear'} linear的粗grid search最高分值： 0.697594607964383
粗grid search得到的parameter是：
 [{'C': array([1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03, 1.e+04, 1.e+05,
       1.e+06, 1.e+07, 1.e+08]), 'kernel': ['rbf'], 'gamma': array([1.e-08, 1.e-07, 1.e-06, 1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01,
       1.e+00, 1.e+01, 1.e+02])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.848 (+/-0.459) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.848 (+/-0.459) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.665 (+/-0.225) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.618 (+/-0.309) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.848 (+/-0.459) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.646 (+/-0.204) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.660 (+/-0.290) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.636 (+/-0.384) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.848 (+/-0.459) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.646 (+/-0.204) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.167) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.601 (+/-0.315) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.638 (+/-0.382) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.848 (+/-0.459) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.646 (+/-0.204) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.167) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.595 (+/-0.192) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.608 (+/-0.309) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.638 (+/-0.382) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.848 (+/-0.459) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.646 (+/-0.204) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.612 (+/-0.165) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.596 (+/-0.193) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.576 (+/-0.207) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.610 (+/-0.308) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.638 (+/-0.382) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.731 (+/-0.365) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.660 (+/-0.219) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.606 (+/-0.165) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.597 (+/-0.195) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.591 (+/-0.194) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.581 (+/-0.201) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.610 (+/-0.308) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.638 (+/-0.382) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.848 (+/-0.460) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.707 (+/-0.415) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.620 (+/-0.289) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.586 (+/-0.170) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.588 (+/-0.194) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.582 (+/-0.201) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.585 (+/-0.200) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.610 (+/-0.308) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.638 (+/-0.382) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.793 (+/-0.440) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.671 (+/-0.375) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.623 (+/-0.289) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.587 (+/-0.170) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.588 (+/-0.194) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.582 (+/-0.201) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.585 (+/-0.200) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.610 (+/-0.308) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.638 (+/-0.382) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.690 (+/-0.300) for {'C': 1.0, 'kernel': 'linear'}
0.613 (+/-0.190) for {'C': 10.0, 'kernel': 'linear'}
0.608 (+/-0.197) for {'C': 100.0, 'kernel': 'linear'}
0.594 (+/-0.195) for {'C': 1000.0, 'kernel': 'linear'}
0.586 (+/-0.195) for {'C': 10000.0, 'kernel': 'linear'}
0.582 (+/-0.201) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      1.00       623

avg / total       0.98      0.99      0.99       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.658 (+/-0.217) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.658 (+/-0.217) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.698 (+/-0.307) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.236) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.008) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.658 (+/-0.217) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.306) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.698 (+/-0.306) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.611 (+/-0.241) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.007) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.658 (+/-0.217) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.698 (+/-0.306) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.305) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.587 (+/-0.232) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.007) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.658 (+/-0.217) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.698 (+/-0.306) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.305) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.662 (+/-0.314) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.007) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.658 (+/-0.217) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.698 (+/-0.306) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.305) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.662 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.585 (+/-0.235) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.007) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.699 (+/-0.308) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.698 (+/-0.306) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.696 (+/-0.301) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.662 (+/-0.315) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.660 (+/-0.313) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.611 (+/-0.239) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.240) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.239) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.007) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.683 (+/-0.296) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.660 (+/-0.221) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.695 (+/-0.301) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.661 (+/-0.310) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.658 (+/-0.312) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.633 (+/-0.320) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.611 (+/-0.241) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.240) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.239) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.007) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.699 (+/-0.307) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.654 (+/-0.242) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.695 (+/-0.302) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.661 (+/-0.312) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.658 (+/-0.313) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.633 (+/-0.321) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.611 (+/-0.241) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.240) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.239) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.007) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.699 (+/-0.307) for {'C': 1.0, 'kernel': 'linear'}
0.696 (+/-0.304) for {'C': 10.0, 'kernel': 'linear'}
0.663 (+/-0.314) for {'C': 100.0, 'kernel': 'linear'}
0.660 (+/-0.315) for {'C': 1000.0, 'kernel': 'linear'}
0.658 (+/-0.307) for {'C': 10000.0, 'kernel': 'linear'}
0.633 (+/-0.322) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6991976873608706
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.652 (+/-0.313) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.642 (+/-0.204) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.622 (+/-0.310) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.642 (+/-0.204) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.622 (+/-0.185) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.598 (+/-0.298) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.642 (+/-0.204) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.618 (+/-0.182) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.625 (+/-0.282) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.636 (+/-0.384) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.642 (+/-0.204) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.618 (+/-0.182) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.597 (+/-0.161) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.605 (+/-0.311) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.638 (+/-0.382) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.642 (+/-0.204) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.618 (+/-0.182) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.597 (+/-0.161) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.587 (+/-0.196) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.608 (+/-0.309) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.638 (+/-0.382) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.747 (+/-0.502) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.642 (+/-0.204) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.618 (+/-0.182) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.594 (+/-0.157) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.587 (+/-0.195) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.581 (+/-0.202) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.610 (+/-0.308) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.638 (+/-0.382) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.697 (+/-0.492) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.651 (+/-0.225) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.604 (+/-0.176) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.594 (+/-0.162) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.587 (+/-0.195) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.589 (+/-0.194) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.581 (+/-0.201) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.610 (+/-0.308) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.638 (+/-0.382) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.831 (+/-0.449) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.535 (+/-0.144) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.607 (+/-0.277) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.582 (+/-0.166) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.588 (+/-0.194) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.582 (+/-0.201) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.585 (+/-0.200) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.610 (+/-0.308) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.638 (+/-0.382) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.793 (+/-0.440) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.551 (+/-0.297) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.618 (+/-0.278) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.578 (+/-0.172) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.588 (+/-0.194) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.582 (+/-0.201) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.585 (+/-0.200) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.610 (+/-0.308) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.638 (+/-0.382) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.798 (+/-0.437) for {'C': 0.1, 'kernel': 'linear'}
0.635 (+/-0.198) for {'C': 1.0, 'kernel': 'linear'}
0.586 (+/-0.152) for {'C': 10.0, 'kernel': 'linear'}
0.587 (+/-0.195) for {'C': 100.0, 'kernel': 'linear'}
0.589 (+/-0.194) for {'C': 1000.0, 'kernel': 'linear'}
0.586 (+/-0.195) for {'C': 10000.0, 'kernel': 'linear'}
0.582 (+/-0.201) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.665 (+/-0.315) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.698 (+/-0.306) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.495 (+/-0.011) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.306) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.304) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.611 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.008) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.698 (+/-0.306) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.303) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.694 (+/-0.305) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.611 (+/-0.241) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.007) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.698 (+/-0.306) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.303) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.694 (+/-0.305) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.608 (+/-0.243) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.007) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.698 (+/-0.306) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.303) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.694 (+/-0.305) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.657 (+/-0.312) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.007) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.617 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.698 (+/-0.306) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.697 (+/-0.303) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.694 (+/-0.305) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.658 (+/-0.312) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.609 (+/-0.242) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.007) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.600 (+/-0.245) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.698 (+/-0.307) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.696 (+/-0.300) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.693 (+/-0.304) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.658 (+/-0.313) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.659 (+/-0.313) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.611 (+/-0.239) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.240) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.239) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.007) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.700 (+/-0.309) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.639 (+/-0.207) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.694 (+/-0.302) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.705 (+/-0.338) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.658 (+/-0.313) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.633 (+/-0.320) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.611 (+/-0.241) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.240) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.239) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.007) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.699 (+/-0.307) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.575 (+/-0.233) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.695 (+/-0.304) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.679 (+/-0.362) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.658 (+/-0.313) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.633 (+/-0.321) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.611 (+/-0.241) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.240) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.239) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.007) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.700 (+/-0.309) for {'C': 0.1, 'kernel': 'linear'}
0.698 (+/-0.305) for {'C': 1.0, 'kernel': 'linear'}
0.694 (+/-0.301) for {'C': 10.0, 'kernel': 'linear'}
0.657 (+/-0.313) for {'C': 100.0, 'kernel': 'linear'}
0.659 (+/-0.314) for {'C': 1000.0, 'kernel': 'linear'}
0.658 (+/-0.308) for {'C': 10000.0, 'kernel': 'linear'}
0.633 (+/-0.322) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.04      0.17      0.07         6
          1       0.99      0.96      0.98       623

avg / total       0.98      0.96      0.97       629

本轮grid search结果，得到最好的参数选择是： {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.7051261439525104
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.660 (+/-0.219) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.634 (+/-0.193) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.630 (+/-0.189) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.611 (+/-0.184) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.622 (+/-0.185) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.606 (+/-0.165) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.616 (+/-0.179) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.616 (+/-0.182) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.602 (+/-0.196) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.602 (+/-0.196) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.597 (+/-0.195) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.664 (+/-0.291) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.655 (+/-0.286) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.625 (+/-0.184) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.597 (+/-0.161) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.619 (+/-0.170) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.610 (+/-0.168) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.614 (+/-0.179) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.598 (+/-0.191) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.598 (+/-0.192) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.592 (+/-0.192) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.592 (+/-0.194) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.653 (+/-0.286) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.636 (+/-0.288) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.595 (+/-0.160) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.601 (+/-0.157) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.615 (+/-0.172) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.622 (+/-0.189) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.597 (+/-0.189) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.599 (+/-0.192) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.595 (+/-0.192) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.591 (+/-0.193) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.592 (+/-0.195) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.645 (+/-0.286) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.625 (+/-0.289) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.595 (+/-0.160) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.597 (+/-0.155) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.618 (+/-0.172) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.610 (+/-0.180) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.592 (+/-0.188) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.596 (+/-0.193) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.595 (+/-0.194) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.590 (+/-0.194) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.591 (+/-0.194) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.623 (+/-0.288) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.621 (+/-0.290) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.608 (+/-0.186) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.601 (+/-0.159) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.591 (+/-0.173) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.588 (+/-0.170) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.589 (+/-0.188) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.595 (+/-0.192) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.592 (+/-0.193) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.590 (+/-0.194) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.590 (+/-0.194) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.620 (+/-0.289) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.623 (+/-0.289) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.614 (+/-0.182) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.586 (+/-0.184) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.598 (+/-0.194) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.586 (+/-0.170) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.591 (+/-0.193) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.594 (+/-0.194) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.589 (+/-0.194) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.590 (+/-0.194) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.588 (+/-0.194) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.618 (+/-0.290) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.597 (+/-0.175) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.616 (+/-0.183) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.586 (+/-0.184) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.592 (+/-0.177) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.588 (+/-0.170) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.591 (+/-0.193) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.593 (+/-0.193) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.587 (+/-0.195) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.591 (+/-0.196) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.588 (+/-0.194) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.617 (+/-0.290) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.594 (+/-0.175) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.622 (+/-0.212) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.585 (+/-0.184) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.591 (+/-0.177) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.588 (+/-0.170) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.583 (+/-0.171) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.594 (+/-0.195) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.587 (+/-0.195) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.591 (+/-0.195) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.588 (+/-0.194) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.617 (+/-0.290) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.593 (+/-0.175) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.630 (+/-0.224) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.585 (+/-0.184) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.588 (+/-0.174) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.587 (+/-0.170) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.582 (+/-0.172) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.593 (+/-0.195) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.587 (+/-0.195) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.591 (+/-0.195) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.588 (+/-0.194) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.618 (+/-0.289) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.593 (+/-0.175) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.625 (+/-0.215) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.587 (+/-0.185) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.588 (+/-0.175) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.587 (+/-0.170) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.582 (+/-0.172) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.591 (+/-0.194) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.588 (+/-0.194) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.591 (+/-0.195) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.588 (+/-0.194) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.623 (+/-0.289) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.599 (+/-0.176) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.625 (+/-0.215) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.586 (+/-0.185) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.588 (+/-0.175) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.587 (+/-0.170) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.582 (+/-0.172) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.591 (+/-0.194) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.588 (+/-0.194) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.589 (+/-0.195) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.588 (+/-0.194) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.690 (+/-0.300) for {'C': 1.0, 'kernel': 'linear'}
0.613 (+/-0.190) for {'C': 10.0, 'kernel': 'linear'}
0.608 (+/-0.197) for {'C': 100.0, 'kernel': 'linear'}
0.594 (+/-0.195) for {'C': 1000.0, 'kernel': 'linear'}
0.586 (+/-0.195) for {'C': 10000.0, 'kernel': 'linear'}
0.582 (+/-0.201) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.698 (+/-0.306) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.698 (+/-0.306) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.697 (+/-0.305) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.696 (+/-0.304) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.697 (+/-0.304) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.696 (+/-0.301) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.697 (+/-0.304) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.680 (+/-0.291) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.663 (+/-0.314) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.663 (+/-0.314) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.662 (+/-0.315) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.698 (+/-0.305) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.698 (+/-0.305) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.697 (+/-0.304) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.695 (+/-0.303) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.697 (+/-0.305) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.696 (+/-0.301) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.680 (+/-0.293) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.662 (+/-0.313) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.662 (+/-0.314) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.661 (+/-0.313) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.660 (+/-0.314) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.697 (+/-0.304) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.696 (+/-0.304) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.695 (+/-0.303) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.696 (+/-0.304) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.696 (+/-0.306) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.696 (+/-0.302) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.663 (+/-0.313) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.662 (+/-0.314) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.661 (+/-0.314) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.660 (+/-0.313) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.660 (+/-0.314) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.697 (+/-0.303) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.696 (+/-0.302) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.695 (+/-0.303) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.695 (+/-0.303) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.696 (+/-0.307) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.679 (+/-0.289) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.662 (+/-0.313) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.661 (+/-0.315) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.660 (+/-0.316) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.659 (+/-0.315) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.660 (+/-0.314) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.695 (+/-0.302) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.695 (+/-0.302) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.696 (+/-0.305) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.696 (+/-0.304) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.662 (+/-0.314) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.662 (+/-0.312) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.661 (+/-0.313) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.661 (+/-0.315) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.660 (+/-0.315) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.659 (+/-0.315) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.659 (+/-0.314) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.695 (+/-0.301) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.695 (+/-0.301) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.696 (+/-0.305) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.662 (+/-0.310) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.662 (+/-0.314) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.661 (+/-0.310) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.660 (+/-0.313) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.660 (+/-0.316) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.659 (+/-0.314) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.659 (+/-0.315) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.658 (+/-0.312) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.695 (+/-0.301) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.695 (+/-0.301) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.696 (+/-0.307) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.661 (+/-0.311) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.662 (+/-0.314) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.661 (+/-0.312) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.660 (+/-0.314) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.660 (+/-0.316) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.658 (+/-0.312) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.658 (+/-0.316) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.658 (+/-0.311) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.695 (+/-0.301) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.695 (+/-0.301) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.696 (+/-0.307) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.661 (+/-0.311) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.662 (+/-0.315) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.661 (+/-0.313) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.660 (+/-0.314) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.660 (+/-0.316) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.658 (+/-0.312) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.658 (+/-0.316) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.658 (+/-0.311) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.695 (+/-0.300) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.695 (+/-0.301) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.697 (+/-0.307) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.661 (+/-0.311) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.662 (+/-0.314) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.661 (+/-0.312) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.659 (+/-0.314) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.660 (+/-0.316) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.658 (+/-0.311) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.659 (+/-0.316) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.658 (+/-0.312) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.695 (+/-0.301) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.695 (+/-0.301) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.697 (+/-0.308) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.662 (+/-0.312) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.661 (+/-0.314) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.661 (+/-0.312) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.659 (+/-0.314) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.660 (+/-0.314) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.658 (+/-0.313) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.659 (+/-0.316) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.658 (+/-0.312) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.695 (+/-0.302) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.695 (+/-0.302) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.697 (+/-0.308) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.662 (+/-0.312) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.661 (+/-0.314) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.661 (+/-0.312) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.659 (+/-0.314) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.659 (+/-0.314) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.658 (+/-0.313) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.658 (+/-0.316) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.658 (+/-0.313) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.699 (+/-0.307) for {'C': 1.0, 'kernel': 'linear'}
0.696 (+/-0.304) for {'C': 10.0, 'kernel': 'linear'}
0.663 (+/-0.314) for {'C': 100.0, 'kernel': 'linear'}
0.660 (+/-0.315) for {'C': 1000.0, 'kernel': 'linear'}
0.658 (+/-0.307) for {'C': 10000.0, 'kernel': 'linear'}
0.633 (+/-0.322) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.20      0.17      0.18         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.99      0.99       629

本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6985561464259213
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.604 (+/-0.176) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.614 (+/-0.292) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.583 (+/-0.151) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.580 (+/-0.144) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.603 (+/-0.163) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.594 (+/-0.162) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.603 (+/-0.183) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.603 (+/-0.182) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.588 (+/-0.194) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.586 (+/-0.196) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.587 (+/-0.195) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.616 (+/-0.291) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.586 (+/-0.177) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.580 (+/-0.144) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.588 (+/-0.153) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.601 (+/-0.165) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.601 (+/-0.165) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.598 (+/-0.179) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.589 (+/-0.193) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.585 (+/-0.196) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.586 (+/-0.196) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.587 (+/-0.195) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.599 (+/-0.279) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.586 (+/-0.176) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.583 (+/-0.145) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.590 (+/-0.154) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.602 (+/-0.164) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.613 (+/-0.184) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.581 (+/-0.191) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.586 (+/-0.196) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.588 (+/-0.194) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.586 (+/-0.196) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.587 (+/-0.195) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.607 (+/-0.280) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.579 (+/-0.151) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.590 (+/-0.155) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.591 (+/-0.157) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.601 (+/-0.173) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.605 (+/-0.177) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.582 (+/-0.191) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.587 (+/-0.195) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.585 (+/-0.196) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.586 (+/-0.195) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.588 (+/-0.194) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.603 (+/-0.278) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.577 (+/-0.143) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.584 (+/-0.145) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.600 (+/-0.148) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.580 (+/-0.171) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.582 (+/-0.166) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.582 (+/-0.191) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.586 (+/-0.195) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.585 (+/-0.196) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.585 (+/-0.196) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.588 (+/-0.194) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.607 (+/-0.277) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.582 (+/-0.146) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.593 (+/-0.152) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.587 (+/-0.177) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.595 (+/-0.185) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.582 (+/-0.166) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.586 (+/-0.196) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.586 (+/-0.195) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.586 (+/-0.195) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.586 (+/-0.196) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.588 (+/-0.194) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.604 (+/-0.277) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.581 (+/-0.144) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.595 (+/-0.154) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.585 (+/-0.178) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.586 (+/-0.163) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.582 (+/-0.167) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.587 (+/-0.196) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.586 (+/-0.195) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.585 (+/-0.196) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.584 (+/-0.197) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.588 (+/-0.194) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.606 (+/-0.277) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.578 (+/-0.143) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.596 (+/-0.158) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.585 (+/-0.178) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.586 (+/-0.163) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.578 (+/-0.172) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.578 (+/-0.173) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.586 (+/-0.195) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.586 (+/-0.196) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.584 (+/-0.197) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.588 (+/-0.194) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.610 (+/-0.278) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.576 (+/-0.143) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.603 (+/-0.180) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.585 (+/-0.179) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.586 (+/-0.163) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.578 (+/-0.172) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.578 (+/-0.173) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.587 (+/-0.195) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.586 (+/-0.196) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.584 (+/-0.197) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.588 (+/-0.194) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.611 (+/-0.277) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.576 (+/-0.143) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.597 (+/-0.166) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.585 (+/-0.179) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.585 (+/-0.164) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.578 (+/-0.172) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.578 (+/-0.173) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.587 (+/-0.195) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.586 (+/-0.195) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.584 (+/-0.197) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.588 (+/-0.194) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.618 (+/-0.278) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.583 (+/-0.146) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.597 (+/-0.166) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.585 (+/-0.179) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.585 (+/-0.164) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.578 (+/-0.172) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.578 (+/-0.173) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.587 (+/-0.195) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.586 (+/-0.195) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.584 (+/-0.197) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.588 (+/-0.194) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.798 (+/-0.437) for {'C': 0.1, 'kernel': 'linear'}
0.635 (+/-0.198) for {'C': 1.0, 'kernel': 'linear'}
0.586 (+/-0.152) for {'C': 10.0, 'kernel': 'linear'}
0.587 (+/-0.195) for {'C': 100.0, 'kernel': 'linear'}
0.589 (+/-0.194) for {'C': 1000.0, 'kernel': 'linear'}
0.586 (+/-0.195) for {'C': 10000.0, 'kernel': 'linear'}
0.582 (+/-0.201) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.696 (+/-0.300) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.694 (+/-0.299) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.694 (+/-0.299) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.694 (+/-0.300) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.695 (+/-0.304) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.693 (+/-0.304) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.716 (+/-0.351) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.699 (+/-0.346) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.681 (+/-0.365) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.656 (+/-0.312) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.658 (+/-0.313) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.694 (+/-0.298) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.693 (+/-0.296) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.693 (+/-0.300) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.694 (+/-0.303) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.694 (+/-0.306) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.717 (+/-0.352) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.698 (+/-0.345) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.682 (+/-0.366) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.656 (+/-0.311) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.656 (+/-0.312) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.658 (+/-0.311) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.693 (+/-0.298) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.693 (+/-0.296) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.694 (+/-0.304) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.694 (+/-0.304) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.717 (+/-0.353) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.741 (+/-0.313) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.655 (+/-0.313) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.656 (+/-0.312) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.681 (+/-0.365) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.656 (+/-0.311) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.658 (+/-0.311) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.694 (+/-0.301) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.693 (+/-0.297) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.694 (+/-0.305) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.693 (+/-0.304) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.715 (+/-0.352) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.723 (+/-0.311) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.655 (+/-0.314) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.657 (+/-0.313) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.656 (+/-0.310) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.656 (+/-0.311) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.658 (+/-0.313) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.694 (+/-0.301) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.693 (+/-0.299) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.694 (+/-0.304) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.742 (+/-0.316) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.681 (+/-0.365) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.706 (+/-0.336) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.656 (+/-0.314) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.657 (+/-0.312) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.656 (+/-0.309) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.656 (+/-0.308) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.658 (+/-0.313) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.694 (+/-0.302) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.694 (+/-0.300) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.694 (+/-0.305) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.708 (+/-0.341) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.705 (+/-0.345) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.705 (+/-0.338) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.656 (+/-0.314) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.656 (+/-0.313) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.657 (+/-0.310) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.656 (+/-0.309) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.658 (+/-0.313) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.694 (+/-0.301) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.694 (+/-0.299) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.694 (+/-0.306) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.708 (+/-0.340) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.705 (+/-0.346) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.705 (+/-0.337) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.656 (+/-0.315) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.657 (+/-0.313) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.656 (+/-0.309) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.655 (+/-0.309) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.658 (+/-0.312) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.694 (+/-0.301) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.693 (+/-0.299) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.694 (+/-0.306) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.708 (+/-0.339) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.704 (+/-0.347) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.680 (+/-0.362) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.656 (+/-0.315) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.657 (+/-0.313) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.657 (+/-0.310) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.656 (+/-0.308) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.658 (+/-0.312) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.695 (+/-0.303) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.693 (+/-0.299) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.694 (+/-0.307) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.707 (+/-0.339) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.703 (+/-0.347) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.679 (+/-0.362) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.656 (+/-0.314) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.657 (+/-0.314) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.657 (+/-0.310) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.656 (+/-0.308) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.658 (+/-0.312) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.695 (+/-0.303) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.693 (+/-0.299) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.694 (+/-0.307) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.707 (+/-0.339) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.703 (+/-0.346) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.679 (+/-0.362) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.656 (+/-0.315) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.657 (+/-0.314) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.657 (+/-0.310) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.656 (+/-0.308) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.658 (+/-0.312) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.695 (+/-0.304) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.694 (+/-0.300) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.694 (+/-0.307) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.707 (+/-0.339) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.704 (+/-0.344) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.679 (+/-0.362) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.656 (+/-0.314) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.657 (+/-0.313) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.657 (+/-0.310) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.655 (+/-0.309) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.658 (+/-0.313) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.700 (+/-0.309) for {'C': 0.1, 'kernel': 'linear'}
0.698 (+/-0.305) for {'C': 1.0, 'kernel': 'linear'}
0.694 (+/-0.301) for {'C': 10.0, 'kernel': 'linear'}
0.657 (+/-0.313) for {'C': 100.0, 'kernel': 'linear'}
0.659 (+/-0.314) for {'C': 1000.0, 'kernel': 'linear'}
0.658 (+/-0.308) for {'C': 10000.0, 'kernel': 'linear'}
0.633 (+/-0.322) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.07      0.17      0.10         6
          1       0.99      0.98      0.98       623

avg / total       0.98      0.97      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.7423051158380741
循环迭代之前，delta is: [3.69042656e+06 6.01892829e-06]
执行tunemodel()函数前，使用的grid search parameter是：
 [{'C': array([3981071.70553497, 4365158.32240166, 4786300.92322638,
       5248074.60249772, 5754399.37337156, 6309573.44480193,
       6918309.70918936, 7585775.75029184, 8317637.7110267 ,
       9120108.39355909, 9999999.99999999]), 'kernel': ['rbf'], 'gamma': array([2.51188643e-06, 2.75422870e-06, 3.01995172e-06, 3.31131121e-06,
       3.63078055e-06, 3.98107171e-06, 4.36515832e-06, 4.78630092e-06,
       5.24807460e-06, 5.75439937e-06, 6.30957344e-06])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.595 (+/-0.160) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.627 (+/-0.200) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.597 (+/-0.161) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.635 (+/-0.287) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.600 (+/-0.164) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.597 (+/-0.155) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.599 (+/-0.159) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.623 (+/-0.184) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.607 (+/-0.180) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.622 (+/-0.174) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.618 (+/-0.172) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.591 (+/-0.154) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.652 (+/-0.295) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.597 (+/-0.161) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.641 (+/-0.289) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.608 (+/-0.184) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.597 (+/-0.155) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.596 (+/-0.159) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.623 (+/-0.184) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.596 (+/-0.192) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.630 (+/-0.189) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.608 (+/-0.161) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.591 (+/-0.154) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.654 (+/-0.294) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.597 (+/-0.161) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.639 (+/-0.290) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.604 (+/-0.180) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.597 (+/-0.155) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.596 (+/-0.159) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.623 (+/-0.184) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.596 (+/-0.192) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.639 (+/-0.202) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.608 (+/-0.161) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.598 (+/-0.177) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.654 (+/-0.294) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.598 (+/-0.160) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.635 (+/-0.287) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.609 (+/-0.181) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.599 (+/-0.158) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.602 (+/-0.166) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.616 (+/-0.182) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.597 (+/-0.192) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.639 (+/-0.202) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.605 (+/-0.161) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.603 (+/-0.181) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.654 (+/-0.294) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.598 (+/-0.160) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.636 (+/-0.286) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.609 (+/-0.181) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.600 (+/-0.158) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.605 (+/-0.179) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.614 (+/-0.182) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.597 (+/-0.192) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.639 (+/-0.202) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.595 (+/-0.176) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.608 (+/-0.186) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.654 (+/-0.294) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.592 (+/-0.155) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.642 (+/-0.289) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.609 (+/-0.180) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.601 (+/-0.159) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.603 (+/-0.180) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.614 (+/-0.182) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.594 (+/-0.191) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.639 (+/-0.202) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.591 (+/-0.173) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.604 (+/-0.180) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.654 (+/-0.294) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.596 (+/-0.155) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.652 (+/-0.289) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.609 (+/-0.180) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.591 (+/-0.152) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.605 (+/-0.180) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.605 (+/-0.163) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.594 (+/-0.191) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.627 (+/-0.197) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.593 (+/-0.176) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.602 (+/-0.178) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.644 (+/-0.288) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.596 (+/-0.155) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.649 (+/-0.288) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.609 (+/-0.180) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.588 (+/-0.150) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.593 (+/-0.191) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.608 (+/-0.164) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.596 (+/-0.191) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.626 (+/-0.188) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.592 (+/-0.177) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.609 (+/-0.184) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.650 (+/-0.287) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.594 (+/-0.155) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.657 (+/-0.303) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.613 (+/-0.186) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.588 (+/-0.150) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.593 (+/-0.191) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.595 (+/-0.176) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.597 (+/-0.191) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.624 (+/-0.189) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.592 (+/-0.177) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.609 (+/-0.184) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.650 (+/-0.287) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.594 (+/-0.155) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.657 (+/-0.303) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.613 (+/-0.186) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.596 (+/-0.174) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.584 (+/-0.170) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.598 (+/-0.183) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.593 (+/-0.190) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.611 (+/-0.204) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.598 (+/-0.194) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.614 (+/-0.182) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.650 (+/-0.287) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.597 (+/-0.158) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.674 (+/-0.354) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.612 (+/-0.186) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.586 (+/-0.184) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.584 (+/-0.170) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.595 (+/-0.176) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.593 (+/-0.190) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.607 (+/-0.201) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.598 (+/-0.194) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.690 (+/-0.300) for {'C': 1.0, 'kernel': 'linear'}
0.613 (+/-0.190) for {'C': 10.0, 'kernel': 'linear'}
0.608 (+/-0.197) for {'C': 100.0, 'kernel': 'linear'}
0.594 (+/-0.195) for {'C': 1000.0, 'kernel': 'linear'}
0.586 (+/-0.195) for {'C': 10000.0, 'kernel': 'linear'}
0.582 (+/-0.201) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.695 (+/-0.303) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.697 (+/-0.307) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.695 (+/-0.300) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.696 (+/-0.303) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.695 (+/-0.300) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.695 (+/-0.303) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.695 (+/-0.301) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.697 (+/-0.305) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.679 (+/-0.287) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.697 (+/-0.307) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.696 (+/-0.307) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.695 (+/-0.303) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.697 (+/-0.308) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.695 (+/-0.300) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.696 (+/-0.305) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.695 (+/-0.300) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.695 (+/-0.303) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.695 (+/-0.301) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.697 (+/-0.305) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.662 (+/-0.309) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.697 (+/-0.307) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.679 (+/-0.294) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.695 (+/-0.303) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.697 (+/-0.308) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.695 (+/-0.300) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.696 (+/-0.305) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.695 (+/-0.300) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.695 (+/-0.303) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.695 (+/-0.301) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.697 (+/-0.305) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.662 (+/-0.309) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.697 (+/-0.308) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.679 (+/-0.294) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.695 (+/-0.303) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.697 (+/-0.308) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.695 (+/-0.301) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.696 (+/-0.304) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.695 (+/-0.300) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.695 (+/-0.303) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.695 (+/-0.302) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.680 (+/-0.293) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.662 (+/-0.309) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.697 (+/-0.308) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.679 (+/-0.293) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.695 (+/-0.304) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.697 (+/-0.308) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.695 (+/-0.301) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.696 (+/-0.304) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.695 (+/-0.300) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.696 (+/-0.304) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.679 (+/-0.289) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.680 (+/-0.293) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.662 (+/-0.309) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.697 (+/-0.308) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.663 (+/-0.314) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.696 (+/-0.305) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.697 (+/-0.308) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.695 (+/-0.300) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.696 (+/-0.304) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.695 (+/-0.301) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.696 (+/-0.304) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.679 (+/-0.288) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.680 (+/-0.294) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.662 (+/-0.310) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.697 (+/-0.308) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.662 (+/-0.314) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.695 (+/-0.304) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.697 (+/-0.308) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.695 (+/-0.302) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.697 (+/-0.306) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.695 (+/-0.301) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.679 (+/-0.290) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.679 (+/-0.289) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.679 (+/-0.294) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.662 (+/-0.309) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.680 (+/-0.296) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.663 (+/-0.314) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.695 (+/-0.303) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.697 (+/-0.308) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.695 (+/-0.302) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.697 (+/-0.307) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.695 (+/-0.301) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.678 (+/-0.289) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.662 (+/-0.309) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.679 (+/-0.294) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.662 (+/-0.311) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.680 (+/-0.296) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.662 (+/-0.314) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.696 (+/-0.304) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.697 (+/-0.308) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.695 (+/-0.300) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.697 (+/-0.308) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.695 (+/-0.301) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.678 (+/-0.289) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.662 (+/-0.309) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.663 (+/-0.314) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.663 (+/-0.311) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.680 (+/-0.295) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.662 (+/-0.314) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.696 (+/-0.305) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.697 (+/-0.308) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.695 (+/-0.300) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.697 (+/-0.308) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.695 (+/-0.301) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.678 (+/-0.289) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.662 (+/-0.309) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.663 (+/-0.315) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.662 (+/-0.310) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.663 (+/-0.316) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.662 (+/-0.314) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.696 (+/-0.305) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.697 (+/-0.308) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.695 (+/-0.301) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.697 (+/-0.309) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.695 (+/-0.301) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.662 (+/-0.310) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.662 (+/-0.309) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.663 (+/-0.314) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.662 (+/-0.310) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.663 (+/-0.316) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.662 (+/-0.314) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.699 (+/-0.307) for {'C': 1.0, 'kernel': 'linear'}
0.696 (+/-0.304) for {'C': 10.0, 'kernel': 'linear'}
0.663 (+/-0.314) for {'C': 100.0, 'kernel': 'linear'}
0.660 (+/-0.315) for {'C': 1000.0, 'kernel': 'linear'}
0.658 (+/-0.307) for {'C': 10000.0, 'kernel': 'linear'}
0.633 (+/-0.322) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.20      0.17      0.18         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.99      0.99       629

本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6985561464259213
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.590 (+/-0.155) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.610 (+/-0.170) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.589 (+/-0.154) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.625 (+/-0.278) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.588 (+/-0.160) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.591 (+/-0.157) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.591 (+/-0.161) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.608 (+/-0.186) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.602 (+/-0.180) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.601 (+/-0.165) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.601 (+/-0.173) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.586 (+/-0.149) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.610 (+/-0.170) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.589 (+/-0.154) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.628 (+/-0.280) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.596 (+/-0.183) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.595 (+/-0.155) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.588 (+/-0.160) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.608 (+/-0.186) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.591 (+/-0.192) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.608 (+/-0.185) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.594 (+/-0.162) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.586 (+/-0.149) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.608 (+/-0.164) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.589 (+/-0.154) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.627 (+/-0.281) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.595 (+/-0.183) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.595 (+/-0.155) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.588 (+/-0.160) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.607 (+/-0.187) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.591 (+/-0.192) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.611 (+/-0.190) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.593 (+/-0.162) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.582 (+/-0.144) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.608 (+/-0.164) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.588 (+/-0.155) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.625 (+/-0.281) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.600 (+/-0.185) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.596 (+/-0.159) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.594 (+/-0.169) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.600 (+/-0.183) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.590 (+/-0.192) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.611 (+/-0.191) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.593 (+/-0.162) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.583 (+/-0.146) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.608 (+/-0.164) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.588 (+/-0.155) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.629 (+/-0.281) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.599 (+/-0.186) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.601 (+/-0.148) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.598 (+/-0.184) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.597 (+/-0.182) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.590 (+/-0.193) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.611 (+/-0.191) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.580 (+/-0.171) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.584 (+/-0.145) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.608 (+/-0.164) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.587 (+/-0.154) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.635 (+/-0.284) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.599 (+/-0.186) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.600 (+/-0.148) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.598 (+/-0.184) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.596 (+/-0.183) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.590 (+/-0.192) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.610 (+/-0.191) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.580 (+/-0.171) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.584 (+/-0.145) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.608 (+/-0.164) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.588 (+/-0.155) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.635 (+/-0.284) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.599 (+/-0.186) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.593 (+/-0.142) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.601 (+/-0.184) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.588 (+/-0.161) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.590 (+/-0.193) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.602 (+/-0.183) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.580 (+/-0.171) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.584 (+/-0.145) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.606 (+/-0.164) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.588 (+/-0.155) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.635 (+/-0.284) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.599 (+/-0.186) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.590 (+/-0.141) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.588 (+/-0.194) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.590 (+/-0.162) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.589 (+/-0.193) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.602 (+/-0.183) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.580 (+/-0.171) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.589 (+/-0.152) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.612 (+/-0.167) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.588 (+/-0.155) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.634 (+/-0.285) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.602 (+/-0.192) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.589 (+/-0.142) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.588 (+/-0.194) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.577 (+/-0.170) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.589 (+/-0.193) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.599 (+/-0.183) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.580 (+/-0.171) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.589 (+/-0.152) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.612 (+/-0.167) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.588 (+/-0.155) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.634 (+/-0.285) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.602 (+/-0.192) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.597 (+/-0.166) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.583 (+/-0.171) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.577 (+/-0.170) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.587 (+/-0.192) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.590 (+/-0.187) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.588 (+/-0.194) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.593 (+/-0.152) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.612 (+/-0.167) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.590 (+/-0.159) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.634 (+/-0.285) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.602 (+/-0.192) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.587 (+/-0.177) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.583 (+/-0.171) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.576 (+/-0.170) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.587 (+/-0.192) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.590 (+/-0.187) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.595 (+/-0.185) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.798 (+/-0.437) for {'C': 0.1, 'kernel': 'linear'}
0.635 (+/-0.198) for {'C': 1.0, 'kernel': 'linear'}
0.586 (+/-0.152) for {'C': 10.0, 'kernel': 'linear'}
0.587 (+/-0.195) for {'C': 100.0, 'kernel': 'linear'}
0.589 (+/-0.194) for {'C': 1000.0, 'kernel': 'linear'}
0.586 (+/-0.195) for {'C': 10000.0, 'kernel': 'linear'}
0.582 (+/-0.201) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.694 (+/-0.305) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.696 (+/-0.307) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.694 (+/-0.300) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.695 (+/-0.306) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.692 (+/-0.299) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.693 (+/-0.304) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.692 (+/-0.304) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.693 (+/-0.306) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.701 (+/-0.346) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.716 (+/-0.353) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.715 (+/-0.352) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.694 (+/-0.305) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.696 (+/-0.307) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.694 (+/-0.300) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.695 (+/-0.307) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.692 (+/-0.299) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.718 (+/-0.352) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.692 (+/-0.305) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.693 (+/-0.306) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.684 (+/-0.366) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.716 (+/-0.353) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.699 (+/-0.345) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.694 (+/-0.305) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.696 (+/-0.307) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.694 (+/-0.300) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.695 (+/-0.306) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.692 (+/-0.298) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.718 (+/-0.352) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.692 (+/-0.305) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.693 (+/-0.306) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.683 (+/-0.366) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.716 (+/-0.353) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.699 (+/-0.344) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.693 (+/-0.304) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.696 (+/-0.307) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.694 (+/-0.300) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.695 (+/-0.307) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.692 (+/-0.298) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.718 (+/-0.351) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.692 (+/-0.305) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.676 (+/-0.293) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.683 (+/-0.366) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.716 (+/-0.353) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.698 (+/-0.344) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.694 (+/-0.304) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.696 (+/-0.307) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.693 (+/-0.300) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.695 (+/-0.307) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.692 (+/-0.298) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.743 (+/-0.316) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.675 (+/-0.292) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.676 (+/-0.292) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.683 (+/-0.365) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.716 (+/-0.354) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.681 (+/-0.365) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.694 (+/-0.304) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.696 (+/-0.307) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.693 (+/-0.301) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.695 (+/-0.309) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.692 (+/-0.298) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.742 (+/-0.316) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.675 (+/-0.292) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.676 (+/-0.292) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.683 (+/-0.366) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.716 (+/-0.353) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.681 (+/-0.365) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.694 (+/-0.304) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.696 (+/-0.307) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.693 (+/-0.302) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.695 (+/-0.309) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.692 (+/-0.298) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.726 (+/-0.314) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.675 (+/-0.292) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.675 (+/-0.292) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.683 (+/-0.365) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.699 (+/-0.345) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.681 (+/-0.365) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.694 (+/-0.304) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.696 (+/-0.307) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.693 (+/-0.302) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.695 (+/-0.309) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.691 (+/-0.299) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.725 (+/-0.313) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.658 (+/-0.311) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.676 (+/-0.292) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.683 (+/-0.365) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.699 (+/-0.345) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.681 (+/-0.366) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.694 (+/-0.304) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.696 (+/-0.307) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.693 (+/-0.302) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.695 (+/-0.309) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.691 (+/-0.299) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.725 (+/-0.313) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.658 (+/-0.312) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.659 (+/-0.311) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.682 (+/-0.365) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.698 (+/-0.345) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.681 (+/-0.366) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.694 (+/-0.304) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.696 (+/-0.307) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.693 (+/-0.302) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.695 (+/-0.310) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.691 (+/-0.299) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.725 (+/-0.313) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.683 (+/-0.367) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.658 (+/-0.312) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.682 (+/-0.366) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.707 (+/-0.339) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.680 (+/-0.366) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.694 (+/-0.305) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.696 (+/-0.307) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.693 (+/-0.302) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.694 (+/-0.311) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.691 (+/-0.299) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.708 (+/-0.341) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.682 (+/-0.367) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.658 (+/-0.311) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.682 (+/-0.366) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.707 (+/-0.339) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.705 (+/-0.345) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.700 (+/-0.309) for {'C': 0.1, 'kernel': 'linear'}
0.698 (+/-0.305) for {'C': 1.0, 'kernel': 'linear'}
0.694 (+/-0.301) for {'C': 10.0, 'kernel': 'linear'}
0.657 (+/-0.313) for {'C': 100.0, 'kernel': 'linear'}
0.659 (+/-0.314) for {'C': 1000.0, 'kernel': 'linear'}
0.658 (+/-0.308) for {'C': 10000.0, 'kernel': 'linear'}
0.633 (+/-0.322) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.07      0.17      0.10         6
          1       0.99      0.98      0.98       623

avg / total       0.98      0.97      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.7426256286585868
第0轮loop中，tunemodel函数得到的最优参数是： ([{'C': array([5248074.60249772, 5345643.5939697 , 5445026.5284242 ,
       5546257.1295791 , 5649369.74812302, 5754399.37337156,
       5861381.64514028, 5970352.86583836, 6081350.01278718,
       6194410.75076781, 6309573.44480193]), 'kernel': ['rbf'], 'gamma': array([3.63078055e-06, 3.69828180e-06, 3.76703799e-06, 3.83707245e-06,
       3.90840896e-06, 3.98107171e-06, 4.05508535e-06, 4.13047502e-06,
       4.20726628e-06, 4.28548520e-06, 4.36515832e-06])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}], {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}, 0.7426256286585868)
这是第0次迭代微调C和gamma。
第0次迭代，得到delta： [555174.07143037      0.        ]
执行tunemodel()函数前，使用的grid search parameter是：
 [{'C': array([5248074.60249772, 5345643.5939697 , 5445026.5284242 ,
       5546257.1295791 , 5649369.74812302, 5754399.37337156,
       5861381.64514028, 5970352.86583836, 6081350.01278718,
       6194410.75076781, 6309573.44480193]), 'kernel': ['rbf'], 'gamma': array([3.63078055e-06, 3.69828180e-06, 3.76703799e-06, 3.83707245e-06,
       3.90840896e-06, 3.98107171e-06, 4.05508535e-06, 4.13047502e-06,
       4.20726628e-06, 4.28548520e-06, 4.36515832e-06])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.609 (+/-0.181) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.597 (+/-0.161) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.606 (+/-0.164) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.599 (+/-0.158) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.604 (+/-0.163) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.599 (+/-0.158) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.603 (+/-0.160) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.621 (+/-0.189) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.610 (+/-0.165) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.629 (+/-0.213) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.602 (+/-0.166) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.609 (+/-0.181) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.597 (+/-0.161) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.606 (+/-0.164) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.601 (+/-0.159) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.604 (+/-0.163) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.600 (+/-0.158) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.603 (+/-0.160) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.621 (+/-0.189) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.610 (+/-0.165) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.629 (+/-0.213) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.602 (+/-0.166) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.609 (+/-0.181) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.608 (+/-0.182) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.606 (+/-0.164) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.600 (+/-0.159) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.604 (+/-0.163) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.600 (+/-0.158) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.595 (+/-0.176) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.614 (+/-0.182) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.615 (+/-0.167) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.637 (+/-0.224) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.611 (+/-0.185) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.609 (+/-0.181) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.608 (+/-0.182) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.606 (+/-0.164) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.600 (+/-0.159) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.604 (+/-0.163) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.600 (+/-0.158) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.595 (+/-0.176) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.614 (+/-0.182) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.612 (+/-0.165) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.637 (+/-0.224) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.608 (+/-0.185) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.609 (+/-0.181) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.605 (+/-0.182) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.606 (+/-0.164) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.597 (+/-0.158) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.603 (+/-0.163) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.600 (+/-0.158) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.595 (+/-0.176) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.614 (+/-0.182) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.615 (+/-0.167) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.637 (+/-0.224) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.605 (+/-0.179) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.609 (+/-0.181) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.608 (+/-0.182) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.606 (+/-0.164) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.597 (+/-0.158) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.605 (+/-0.163) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.600 (+/-0.158) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.595 (+/-0.176) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.614 (+/-0.182) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.615 (+/-0.167) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.629 (+/-0.199) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.605 (+/-0.179) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.609 (+/-0.181) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.605 (+/-0.181) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.606 (+/-0.164) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.595 (+/-0.157) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.607 (+/-0.163) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.601 (+/-0.159) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.591 (+/-0.171) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.614 (+/-0.182) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.615 (+/-0.167) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.637 (+/-0.224) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.603 (+/-0.180) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.609 (+/-0.180) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.608 (+/-0.182) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.606 (+/-0.164) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.595 (+/-0.157) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.607 (+/-0.163) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.601 (+/-0.159) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.591 (+/-0.171) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.614 (+/-0.182) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.615 (+/-0.167) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.637 (+/-0.224) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.605 (+/-0.180) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.609 (+/-0.180) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.608 (+/-0.182) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.606 (+/-0.164) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.595 (+/-0.157) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.607 (+/-0.163) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.601 (+/-0.159) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.591 (+/-0.171) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.622 (+/-0.198) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.615 (+/-0.167) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.637 (+/-0.224) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.605 (+/-0.180) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.609 (+/-0.180) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.608 (+/-0.182) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.606 (+/-0.164) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.595 (+/-0.157) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.607 (+/-0.163) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.601 (+/-0.159) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.601 (+/-0.159) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.622 (+/-0.198) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.615 (+/-0.167) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.630 (+/-0.224) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.605 (+/-0.180) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.609 (+/-0.180) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.608 (+/-0.182) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.606 (+/-0.164) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.595 (+/-0.157) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.607 (+/-0.163) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.601 (+/-0.159) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.603 (+/-0.160) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.622 (+/-0.198) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.615 (+/-0.167) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.630 (+/-0.224) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.603 (+/-0.180) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.690 (+/-0.300) for {'C': 1.0, 'kernel': 'linear'}
0.613 (+/-0.190) for {'C': 10.0, 'kernel': 'linear'}
0.608 (+/-0.197) for {'C': 100.0, 'kernel': 'linear'}
0.594 (+/-0.195) for {'C': 1000.0, 'kernel': 'linear'}
0.586 (+/-0.195) for {'C': 10000.0, 'kernel': 'linear'}
0.582 (+/-0.201) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.695 (+/-0.300) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.694 (+/-0.298) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.696 (+/-0.301) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.696 (+/-0.303) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.696 (+/-0.303) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.695 (+/-0.303) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.679 (+/-0.293) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.697 (+/-0.307) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.696 (+/-0.305) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.697 (+/-0.308) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.695 (+/-0.302) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.695 (+/-0.300) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.694 (+/-0.298) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.696 (+/-0.301) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.696 (+/-0.303) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.696 (+/-0.303) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.696 (+/-0.304) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.679 (+/-0.293) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.697 (+/-0.307) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.696 (+/-0.305) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.697 (+/-0.308) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.695 (+/-0.302) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.695 (+/-0.300) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.695 (+/-0.298) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.696 (+/-0.301) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.695 (+/-0.301) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.696 (+/-0.303) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.696 (+/-0.304) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.663 (+/-0.314) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.680 (+/-0.295) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.696 (+/-0.306) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.697 (+/-0.308) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.695 (+/-0.302) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.695 (+/-0.300) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.695 (+/-0.298) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.696 (+/-0.301) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.695 (+/-0.301) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.696 (+/-0.303) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.696 (+/-0.304) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.663 (+/-0.314) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.680 (+/-0.295) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.696 (+/-0.306) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.697 (+/-0.308) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.695 (+/-0.302) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.695 (+/-0.300) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.695 (+/-0.298) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.696 (+/-0.301) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.695 (+/-0.301) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.696 (+/-0.302) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.696 (+/-0.304) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.663 (+/-0.314) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.680 (+/-0.295) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.696 (+/-0.306) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.697 (+/-0.308) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.679 (+/-0.289) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.695 (+/-0.300) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.695 (+/-0.298) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.696 (+/-0.301) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.695 (+/-0.301) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.696 (+/-0.303) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.696 (+/-0.304) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.663 (+/-0.314) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.680 (+/-0.295) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.696 (+/-0.306) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.697 (+/-0.307) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.679 (+/-0.289) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.695 (+/-0.300) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.695 (+/-0.299) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.696 (+/-0.301) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.695 (+/-0.301) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.696 (+/-0.303) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.696 (+/-0.304) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.663 (+/-0.313) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.680 (+/-0.295) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.696 (+/-0.306) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.697 (+/-0.308) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.679 (+/-0.288) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.695 (+/-0.301) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.695 (+/-0.298) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.696 (+/-0.301) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.695 (+/-0.301) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.696 (+/-0.303) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.696 (+/-0.304) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.663 (+/-0.313) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.680 (+/-0.295) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.696 (+/-0.306) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.697 (+/-0.308) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.679 (+/-0.289) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.695 (+/-0.301) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.695 (+/-0.299) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.696 (+/-0.301) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.695 (+/-0.301) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.696 (+/-0.303) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.696 (+/-0.304) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.663 (+/-0.313) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.680 (+/-0.295) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.696 (+/-0.306) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.697 (+/-0.308) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.679 (+/-0.289) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.695 (+/-0.301) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.695 (+/-0.298) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.696 (+/-0.301) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.695 (+/-0.301) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.696 (+/-0.303) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.696 (+/-0.304) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.679 (+/-0.292) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.680 (+/-0.295) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.696 (+/-0.306) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.680 (+/-0.296) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.679 (+/-0.289) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.695 (+/-0.301) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.695 (+/-0.298) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.696 (+/-0.301) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.695 (+/-0.301) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.696 (+/-0.303) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.696 (+/-0.304) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.679 (+/-0.293) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.680 (+/-0.295) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.696 (+/-0.306) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.680 (+/-0.296) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.679 (+/-0.288) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.699 (+/-0.307) for {'C': 1.0, 'kernel': 'linear'}
0.696 (+/-0.304) for {'C': 10.0, 'kernel': 'linear'}
0.663 (+/-0.314) for {'C': 100.0, 'kernel': 'linear'}
0.660 (+/-0.315) for {'C': 1000.0, 'kernel': 'linear'}
0.658 (+/-0.307) for {'C': 10000.0, 'kernel': 'linear'}
0.633 (+/-0.322) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.20      0.17      0.18         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.99      0.99       629

本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6985561464259213
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.600 (+/-0.185) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.591 (+/-0.161) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.598 (+/-0.167) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.597 (+/-0.159) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.599 (+/-0.164) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.596 (+/-0.159) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.591 (+/-0.161) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.600 (+/-0.172) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.595 (+/-0.161) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.600 (+/-0.165) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.594 (+/-0.169) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.600 (+/-0.185) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.591 (+/-0.161) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.598 (+/-0.167) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.597 (+/-0.159) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.599 (+/-0.164) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.596 (+/-0.159) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.591 (+/-0.161) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.599 (+/-0.172) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.595 (+/-0.161) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.600 (+/-0.165) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.594 (+/-0.169) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.600 (+/-0.185) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.601 (+/-0.184) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.598 (+/-0.167) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.597 (+/-0.159) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.599 (+/-0.164) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.595 (+/-0.159) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.581 (+/-0.171) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.591 (+/-0.160) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.600 (+/-0.165) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.607 (+/-0.185) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.602 (+/-0.190) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.600 (+/-0.185) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.602 (+/-0.183) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.598 (+/-0.167) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.597 (+/-0.159) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.599 (+/-0.164) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.596 (+/-0.159) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.581 (+/-0.171) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.591 (+/-0.160) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.597 (+/-0.163) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.607 (+/-0.185) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.602 (+/-0.190) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.600 (+/-0.185) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.599 (+/-0.183) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.598 (+/-0.167) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.596 (+/-0.159) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.599 (+/-0.164) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.601 (+/-0.148) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.581 (+/-0.171) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.591 (+/-0.160) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.599 (+/-0.166) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.607 (+/-0.185) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.599 (+/-0.183) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.599 (+/-0.186) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.601 (+/-0.184) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.598 (+/-0.167) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.596 (+/-0.159) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.599 (+/-0.164) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.601 (+/-0.148) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.581 (+/-0.171) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.591 (+/-0.160) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.599 (+/-0.166) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.606 (+/-0.185) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.598 (+/-0.184) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.599 (+/-0.186) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.599 (+/-0.183) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.598 (+/-0.167) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.594 (+/-0.158) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.599 (+/-0.164) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.601 (+/-0.148) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.581 (+/-0.171) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.591 (+/-0.160) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.599 (+/-0.166) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.606 (+/-0.185) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.599 (+/-0.183) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.599 (+/-0.186) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.602 (+/-0.183) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.598 (+/-0.167) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.593 (+/-0.158) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.599 (+/-0.164) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.601 (+/-0.148) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.581 (+/-0.171) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.591 (+/-0.160) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.599 (+/-0.166) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.606 (+/-0.185) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.601 (+/-0.184) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.599 (+/-0.186) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.602 (+/-0.183) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.598 (+/-0.167) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.593 (+/-0.158) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.598 (+/-0.164) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.600 (+/-0.148) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.581 (+/-0.171) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.599 (+/-0.182) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.599 (+/-0.166) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.606 (+/-0.185) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.601 (+/-0.184) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.599 (+/-0.186) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.602 (+/-0.183) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.597 (+/-0.168) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.593 (+/-0.158) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.598 (+/-0.164) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.600 (+/-0.148) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.591 (+/-0.161) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.599 (+/-0.182) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.599 (+/-0.166) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.599 (+/-0.181) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.601 (+/-0.184) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.599 (+/-0.186) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.602 (+/-0.183) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.596 (+/-0.168) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.593 (+/-0.158) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.598 (+/-0.164) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.600 (+/-0.148) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.591 (+/-0.161) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.599 (+/-0.183) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.599 (+/-0.166) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.599 (+/-0.181) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.598 (+/-0.184) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.798 (+/-0.437) for {'C': 0.1, 'kernel': 'linear'}
0.635 (+/-0.198) for {'C': 1.0, 'kernel': 'linear'}
0.586 (+/-0.152) for {'C': 10.0, 'kernel': 'linear'}
0.587 (+/-0.195) for {'C': 100.0, 'kernel': 'linear'}
0.589 (+/-0.194) for {'C': 1000.0, 'kernel': 'linear'}
0.586 (+/-0.195) for {'C': 10000.0, 'kernel': 'linear'}
0.582 (+/-0.201) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.692 (+/-0.298) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.692 (+/-0.301) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.693 (+/-0.302) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.718 (+/-0.351) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.718 (+/-0.350) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.718 (+/-0.351) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.676 (+/-0.291) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.693 (+/-0.304) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.694 (+/-0.303) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.694 (+/-0.306) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.692 (+/-0.305) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.692 (+/-0.298) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.692 (+/-0.301) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.693 (+/-0.302) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.718 (+/-0.351) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.718 (+/-0.350) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.718 (+/-0.351) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.676 (+/-0.291) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.693 (+/-0.303) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.694 (+/-0.303) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.694 (+/-0.306) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.692 (+/-0.306) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.692 (+/-0.298) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.693 (+/-0.301) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.693 (+/-0.303) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.718 (+/-0.351) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.718 (+/-0.351) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.718 (+/-0.351) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.659 (+/-0.311) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.676 (+/-0.290) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.694 (+/-0.304) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.694 (+/-0.306) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.692 (+/-0.306) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.692 (+/-0.298) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.693 (+/-0.302) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.693 (+/-0.303) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.718 (+/-0.351) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.718 (+/-0.351) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.718 (+/-0.351) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.659 (+/-0.311) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.676 (+/-0.290) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.694 (+/-0.303) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.694 (+/-0.306) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.692 (+/-0.306) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.692 (+/-0.298) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.693 (+/-0.302) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.693 (+/-0.303) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.718 (+/-0.351) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.718 (+/-0.351) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.743 (+/-0.316) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.659 (+/-0.311) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.676 (+/-0.290) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.694 (+/-0.304) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.694 (+/-0.306) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.675 (+/-0.292) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.692 (+/-0.298) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.693 (+/-0.300) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.693 (+/-0.303) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.718 (+/-0.351) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.718 (+/-0.351) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.743 (+/-0.316) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.659 (+/-0.311) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.676 (+/-0.290) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.694 (+/-0.304) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.694 (+/-0.305) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.675 (+/-0.292) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.692 (+/-0.298) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.692 (+/-0.300) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.693 (+/-0.303) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.718 (+/-0.351) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.718 (+/-0.351) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.743 (+/-0.316) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.659 (+/-0.311) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.676 (+/-0.290) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.694 (+/-0.304) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.694 (+/-0.305) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.675 (+/-0.292) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.692 (+/-0.298) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.693 (+/-0.302) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.693 (+/-0.303) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.718 (+/-0.350) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.718 (+/-0.351) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.743 (+/-0.316) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.659 (+/-0.311) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.676 (+/-0.290) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.694 (+/-0.304) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.694 (+/-0.305) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.675 (+/-0.292) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.692 (+/-0.298) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.693 (+/-0.302) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.693 (+/-0.303) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.718 (+/-0.350) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.718 (+/-0.350) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.742 (+/-0.315) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.659 (+/-0.311) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.676 (+/-0.290) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.694 (+/-0.304) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.694 (+/-0.305) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.675 (+/-0.292) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.692 (+/-0.298) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.693 (+/-0.302) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.693 (+/-0.302) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.718 (+/-0.350) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.718 (+/-0.350) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.742 (+/-0.316) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.676 (+/-0.292) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.676 (+/-0.290) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.694 (+/-0.304) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.677 (+/-0.292) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.675 (+/-0.292) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.692 (+/-0.298) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.693 (+/-0.302) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.693 (+/-0.301) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.718 (+/-0.350) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.718 (+/-0.350) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.742 (+/-0.316) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.675 (+/-0.292) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.676 (+/-0.289) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.694 (+/-0.304) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.677 (+/-0.292) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.675 (+/-0.292) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.700 (+/-0.309) for {'C': 0.1, 'kernel': 'linear'}
0.698 (+/-0.305) for {'C': 1.0, 'kernel': 'linear'}
0.694 (+/-0.301) for {'C': 10.0, 'kernel': 'linear'}
0.657 (+/-0.313) for {'C': 100.0, 'kernel': 'linear'}
0.659 (+/-0.314) for {'C': 1000.0, 'kernel': 'linear'}
0.658 (+/-0.308) for {'C': 10000.0, 'kernel': 'linear'}
0.633 (+/-0.322) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.07      0.17      0.10         6
          1       0.99      0.98      0.98       623

avg / total       0.98      0.97      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.7427858850688432
第1轮loop中，tunemodel函数得到的最优参数是： ([{'C': array([5546257.1295791 , 5566727.98150948, 5587274.38994019,
       5607896.6337451 , 5628594.99282729, 5649369.74812302,
       5670221.18160543, 5691149.5762884 , 5712155.21623043,
       5733238.38653839, 5754399.37337156]), 'kernel': ['rbf'], 'gamma': array([3.90840896e-06, 3.92283463e-06, 3.93731354e-06, 3.95184589e-06,
       3.96643188e-06, 3.98107171e-06, 3.99576557e-06, 4.01051366e-06,
       4.02531619e-06, 4.04017335e-06, 4.05508535e-06])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}], {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}, 0.7427858850688432)
这是第1次迭代微调C和gamma。
第1次迭代，得到delta： [105029.62524854      0.        ]
SVC模型clf_op_iter的参数是： {'kernel': 'rbf', 'decision_function_shape': 'ovr', 'class_weight': {-1: 15}, 'tol': 0.001, 'gamma': 3.981071705534978e-06, 'random_state': 0, 'cache_size': 200, 'verbose': False, 'degree': 3, 'C': 5649369.748123019, 'probability': False, 'shrinking': True, 'coef0': 0.0, 'max_iter': -1}

训练模型SVC对训练样本的预测准确率： 0.9440113394755493

测试集中，预测为舞弊样本的有： (array([   0,    1,    2, ..., 1254, 1255, 1256], dtype=int64),) 
测试集中，实际为舞弊样本的有： (array([1246, 1247, 1248, 1249, 1250, 1251, 1252, 1253, 1254, 1255, 1256],
      dtype=int64),) 
预测的舞弊样本数目： 1055

训练模型SVC对测试样本的预测准确率： 0.24875974486180014
以上是第39次特征筛选。
第39次特征筛选，AUC值是： 0.5352035604844594
X_train_iter_svc.shape is: (1257, 13)
X_test_iter_svc.shape is: (1257, 13)
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.722 (+/-0.473) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.638 (+/-0.382) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.646 (+/-0.204) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.613 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.698 (+/-0.306) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.17      0.17      0.17         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6979146054909721
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.635 (+/-0.198) for {'C': 1.0, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.589 (+/-0.193) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.14      0.17      0.15         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.698 (+/-0.305) for {'C': 1.0, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.659 (+/-0.314) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.14      0.17      0.15         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.697594607964383
RBF的粗grid search最佳超参： {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001} RBF的粗grid search最高分值： 0.6979146054909721
linear的粗grid search最佳超参： {'C': 1.0, 'kernel': 'linear'} linear的粗grid search最高分值： 0.697594607964383
粗grid search得到的parameter是：
 [{'C': array([1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03, 1.e+04, 1.e+05,
       1.e+06, 1.e+07, 1.e+08]), 'kernel': ['rbf'], 'gamma': array([1.e-08, 1.e-07, 1.e-06, 1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01,
       1.e+00, 1.e+01, 1.e+02])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.848 (+/-0.459) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.848 (+/-0.459) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.665 (+/-0.225) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.618 (+/-0.309) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.848 (+/-0.459) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.646 (+/-0.204) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.660 (+/-0.290) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.636 (+/-0.384) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.848 (+/-0.459) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.646 (+/-0.204) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.167) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.601 (+/-0.315) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.638 (+/-0.382) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.848 (+/-0.459) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.646 (+/-0.204) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.167) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.595 (+/-0.192) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.608 (+/-0.309) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.638 (+/-0.382) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.848 (+/-0.459) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.646 (+/-0.204) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.615 (+/-0.167) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.592 (+/-0.188) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.576 (+/-0.207) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.610 (+/-0.308) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.638 (+/-0.382) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.731 (+/-0.365) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.660 (+/-0.219) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.622 (+/-0.184) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.596 (+/-0.193) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.593 (+/-0.195) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.581 (+/-0.201) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.610 (+/-0.308) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.638 (+/-0.382) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.848 (+/-0.460) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.707 (+/-0.416) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.620 (+/-0.290) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.594 (+/-0.191) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.588 (+/-0.194) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.584 (+/-0.200) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.583 (+/-0.200) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.610 (+/-0.308) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.638 (+/-0.382) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.793 (+/-0.440) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.671 (+/-0.375) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.623 (+/-0.289) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.593 (+/-0.191) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.587 (+/-0.195) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.584 (+/-0.200) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.583 (+/-0.200) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.610 (+/-0.308) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.638 (+/-0.382) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.690 (+/-0.300) for {'C': 1.0, 'kernel': 'linear'}
0.613 (+/-0.190) for {'C': 10.0, 'kernel': 'linear'}
0.608 (+/-0.197) for {'C': 100.0, 'kernel': 'linear'}
0.594 (+/-0.194) for {'C': 1000.0, 'kernel': 'linear'}
0.582 (+/-0.201) for {'C': 10000.0, 'kernel': 'linear'}
0.582 (+/-0.201) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      1.00       623

avg / total       0.98      0.99      0.99       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.658 (+/-0.217) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.658 (+/-0.217) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.698 (+/-0.307) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.236) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.008) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.658 (+/-0.217) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.306) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.698 (+/-0.306) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.611 (+/-0.241) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.007) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.658 (+/-0.217) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.698 (+/-0.306) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.305) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.587 (+/-0.232) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.007) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.658 (+/-0.217) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.698 (+/-0.306) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.305) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.662 (+/-0.314) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.007) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.658 (+/-0.217) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.698 (+/-0.306) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.305) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.662 (+/-0.314) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.585 (+/-0.235) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.007) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.699 (+/-0.308) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.698 (+/-0.306) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.697 (+/-0.303) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.662 (+/-0.314) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.660 (+/-0.315) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.611 (+/-0.238) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.239) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.239) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.007) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.683 (+/-0.296) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.676 (+/-0.244) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.695 (+/-0.301) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.661 (+/-0.312) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.659 (+/-0.312) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.634 (+/-0.324) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.611 (+/-0.240) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.239) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.239) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.007) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.699 (+/-0.307) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.654 (+/-0.242) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.695 (+/-0.302) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.660 (+/-0.313) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.658 (+/-0.310) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.633 (+/-0.326) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.611 (+/-0.240) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.239) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.239) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.007) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.699 (+/-0.307) for {'C': 1.0, 'kernel': 'linear'}
0.696 (+/-0.304) for {'C': 10.0, 'kernel': 'linear'}
0.663 (+/-0.314) for {'C': 100.0, 'kernel': 'linear'}
0.660 (+/-0.316) for {'C': 1000.0, 'kernel': 'linear'}
0.634 (+/-0.321) for {'C': 10000.0, 'kernel': 'linear'}
0.632 (+/-0.323) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6991976873608706
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.652 (+/-0.313) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.642 (+/-0.204) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.622 (+/-0.310) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.642 (+/-0.204) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.622 (+/-0.185) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.598 (+/-0.298) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.642 (+/-0.204) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.618 (+/-0.182) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.625 (+/-0.282) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.636 (+/-0.384) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.642 (+/-0.204) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.618 (+/-0.182) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.597 (+/-0.161) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.605 (+/-0.311) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.638 (+/-0.382) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.642 (+/-0.204) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.618 (+/-0.182) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.597 (+/-0.161) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.587 (+/-0.196) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.608 (+/-0.309) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.638 (+/-0.382) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.747 (+/-0.502) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.642 (+/-0.204) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.622 (+/-0.185) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.597 (+/-0.161) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.583 (+/-0.190) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.580 (+/-0.202) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.610 (+/-0.308) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.638 (+/-0.382) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.697 (+/-0.492) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.651 (+/-0.225) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.604 (+/-0.176) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.600 (+/-0.165) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.587 (+/-0.195) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.589 (+/-0.194) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.581 (+/-0.201) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.610 (+/-0.308) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.638 (+/-0.382) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.831 (+/-0.449) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.535 (+/-0.144) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.607 (+/-0.277) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.591 (+/-0.188) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.588 (+/-0.194) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.584 (+/-0.200) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.583 (+/-0.200) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.610 (+/-0.308) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.638 (+/-0.382) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.793 (+/-0.440) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.551 (+/-0.297) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.609 (+/-0.276) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.588 (+/-0.194) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.587 (+/-0.195) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.584 (+/-0.200) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.583 (+/-0.200) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.610 (+/-0.308) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.638 (+/-0.382) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.798 (+/-0.437) for {'C': 0.1, 'kernel': 'linear'}
0.635 (+/-0.198) for {'C': 1.0, 'kernel': 'linear'}
0.586 (+/-0.152) for {'C': 10.0, 'kernel': 'linear'}
0.587 (+/-0.195) for {'C': 100.0, 'kernel': 'linear'}
0.589 (+/-0.193) for {'C': 1000.0, 'kernel': 'linear'}
0.582 (+/-0.201) for {'C': 10000.0, 'kernel': 'linear'}
0.582 (+/-0.201) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.665 (+/-0.315) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.698 (+/-0.306) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.495 (+/-0.011) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.306) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.304) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.611 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.008) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.698 (+/-0.306) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.303) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.694 (+/-0.305) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.611 (+/-0.241) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.007) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.698 (+/-0.306) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.303) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.694 (+/-0.305) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.608 (+/-0.243) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.007) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.698 (+/-0.306) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.303) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.694 (+/-0.305) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.657 (+/-0.312) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.007) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.617 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.698 (+/-0.306) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.697 (+/-0.304) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.694 (+/-0.305) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.658 (+/-0.313) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.609 (+/-0.241) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.007) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.600 (+/-0.245) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.698 (+/-0.307) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.696 (+/-0.300) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.694 (+/-0.304) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.658 (+/-0.313) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.659 (+/-0.313) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.611 (+/-0.238) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.239) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.239) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.007) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.700 (+/-0.309) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.643 (+/-0.209) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.694 (+/-0.302) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.706 (+/-0.339) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.659 (+/-0.312) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.634 (+/-0.324) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.611 (+/-0.240) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.239) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.239) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.007) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.699 (+/-0.307) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.576 (+/-0.232) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.695 (+/-0.301) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.680 (+/-0.365) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.658 (+/-0.311) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.633 (+/-0.326) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.611 (+/-0.240) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.239) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.239) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.007) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.700 (+/-0.309) for {'C': 0.1, 'kernel': 'linear'}
0.698 (+/-0.305) for {'C': 1.0, 'kernel': 'linear'}
0.694 (+/-0.301) for {'C': 10.0, 'kernel': 'linear'}
0.657 (+/-0.313) for {'C': 100.0, 'kernel': 'linear'}
0.659 (+/-0.314) for {'C': 1000.0, 'kernel': 'linear'}
0.633 (+/-0.321) for {'C': 10000.0, 'kernel': 'linear'}
0.632 (+/-0.323) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.07      0.33      0.12         6
          1       0.99      0.96      0.98       623

avg / total       0.98      0.95      0.97       629

本轮grid search结果，得到最好的参数选择是： {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.7056069131832797
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.660 (+/-0.219) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.634 (+/-0.193) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.630 (+/-0.189) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.617 (+/-0.185) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.622 (+/-0.185) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.622 (+/-0.184) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.608 (+/-0.161) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.617 (+/-0.181) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.603 (+/-0.196) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.594 (+/-0.191) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.596 (+/-0.193) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.664 (+/-0.291) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.655 (+/-0.286) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.625 (+/-0.184) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.597 (+/-0.161) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.615 (+/-0.167) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.618 (+/-0.185) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.618 (+/-0.184) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.597 (+/-0.191) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.596 (+/-0.191) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.592 (+/-0.192) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.593 (+/-0.195) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.653 (+/-0.286) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.638 (+/-0.287) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.595 (+/-0.160) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.595 (+/-0.154) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.620 (+/-0.184) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.621 (+/-0.190) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.595 (+/-0.189) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.595 (+/-0.192) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.595 (+/-0.194) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.589 (+/-0.194) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.591 (+/-0.195) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.645 (+/-0.286) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.623 (+/-0.289) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.595 (+/-0.160) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.597 (+/-0.155) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.621 (+/-0.187) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.596 (+/-0.192) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.596 (+/-0.191) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.595 (+/-0.192) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.592 (+/-0.194) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.587 (+/-0.195) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.590 (+/-0.194) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.621 (+/-0.289) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.621 (+/-0.290) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.591 (+/-0.154) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.600 (+/-0.158) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.591 (+/-0.177) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.594 (+/-0.192) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.591 (+/-0.187) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.592 (+/-0.193) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.590 (+/-0.195) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.588 (+/-0.195) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.589 (+/-0.194) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.620 (+/-0.290) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.622 (+/-0.289) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.601 (+/-0.164) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.585 (+/-0.184) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.598 (+/-0.194) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.594 (+/-0.191) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.591 (+/-0.193) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.591 (+/-0.194) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.589 (+/-0.195) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.588 (+/-0.194) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.588 (+/-0.194) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.618 (+/-0.290) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.622 (+/-0.289) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.618 (+/-0.211) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.588 (+/-0.184) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.593 (+/-0.193) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.594 (+/-0.191) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.587 (+/-0.170) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.592 (+/-0.195) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.588 (+/-0.194) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.588 (+/-0.194) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.587 (+/-0.195) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.616 (+/-0.291) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.596 (+/-0.175) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.616 (+/-0.211) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.587 (+/-0.184) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.589 (+/-0.189) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.593 (+/-0.191) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.583 (+/-0.164) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.590 (+/-0.195) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.589 (+/-0.194) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.588 (+/-0.194) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.587 (+/-0.195) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.618 (+/-0.290) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.595 (+/-0.175) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.628 (+/-0.225) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.584 (+/-0.184) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.590 (+/-0.187) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.593 (+/-0.191) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.582 (+/-0.164) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.589 (+/-0.195) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.589 (+/-0.194) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.588 (+/-0.194) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.586 (+/-0.195) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.618 (+/-0.290) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.598 (+/-0.175) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.620 (+/-0.212) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.585 (+/-0.184) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.590 (+/-0.187) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.593 (+/-0.191) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.580 (+/-0.163) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.588 (+/-0.195) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.589 (+/-0.194) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.588 (+/-0.194) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.586 (+/-0.195) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.623 (+/-0.289) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.598 (+/-0.175) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.623 (+/-0.216) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.586 (+/-0.185) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.590 (+/-0.187) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.593 (+/-0.191) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.580 (+/-0.163) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.588 (+/-0.195) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.589 (+/-0.194) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.588 (+/-0.195) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.587 (+/-0.195) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.690 (+/-0.300) for {'C': 1.0, 'kernel': 'linear'}
0.613 (+/-0.190) for {'C': 10.0, 'kernel': 'linear'}
0.608 (+/-0.197) for {'C': 100.0, 'kernel': 'linear'}
0.594 (+/-0.194) for {'C': 1000.0, 'kernel': 'linear'}
0.582 (+/-0.201) for {'C': 10000.0, 'kernel': 'linear'}
0.582 (+/-0.201) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.698 (+/-0.306) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.698 (+/-0.306) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.697 (+/-0.305) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.696 (+/-0.304) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.697 (+/-0.304) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.697 (+/-0.303) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.696 (+/-0.304) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.680 (+/-0.291) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.663 (+/-0.314) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.662 (+/-0.311) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.662 (+/-0.314) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.698 (+/-0.305) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.698 (+/-0.305) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.697 (+/-0.304) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.695 (+/-0.303) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.697 (+/-0.305) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.696 (+/-0.301) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.680 (+/-0.294) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.662 (+/-0.312) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.662 (+/-0.313) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.661 (+/-0.313) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.660 (+/-0.315) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.697 (+/-0.304) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.697 (+/-0.304) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.695 (+/-0.303) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.695 (+/-0.304) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.696 (+/-0.306) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.696 (+/-0.301) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.663 (+/-0.313) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.662 (+/-0.314) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.661 (+/-0.315) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.659 (+/-0.312) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.660 (+/-0.314) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.697 (+/-0.303) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.695 (+/-0.302) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.695 (+/-0.303) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.695 (+/-0.303) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.696 (+/-0.306) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.662 (+/-0.309) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.662 (+/-0.313) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.661 (+/-0.315) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.660 (+/-0.315) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.659 (+/-0.311) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.659 (+/-0.313) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.695 (+/-0.302) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.695 (+/-0.302) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.695 (+/-0.304) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.696 (+/-0.304) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.662 (+/-0.314) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.662 (+/-0.310) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.661 (+/-0.314) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.660 (+/-0.314) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.659 (+/-0.315) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.659 (+/-0.312) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.659 (+/-0.312) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.695 (+/-0.301) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.695 (+/-0.301) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.695 (+/-0.305) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.661 (+/-0.310) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.662 (+/-0.314) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.661 (+/-0.312) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.660 (+/-0.313) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.660 (+/-0.314) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.659 (+/-0.314) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.659 (+/-0.312) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.659 (+/-0.312) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.695 (+/-0.301) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.695 (+/-0.301) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.696 (+/-0.307) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.662 (+/-0.312) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.662 (+/-0.313) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.661 (+/-0.313) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.685 (+/-0.369) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.660 (+/-0.314) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.658 (+/-0.313) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.659 (+/-0.312) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.658 (+/-0.310) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.694 (+/-0.301) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.695 (+/-0.301) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.696 (+/-0.307) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.661 (+/-0.311) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.661 (+/-0.313) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.660 (+/-0.313) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.685 (+/-0.368) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.659 (+/-0.313) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.659 (+/-0.313) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.659 (+/-0.312) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.658 (+/-0.310) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.695 (+/-0.301) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.695 (+/-0.300) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.696 (+/-0.307) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.661 (+/-0.311) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.685 (+/-0.367) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.660 (+/-0.313) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.684 (+/-0.368) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.659 (+/-0.312) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.659 (+/-0.313) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.658 (+/-0.313) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.658 (+/-0.310) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.695 (+/-0.301) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.695 (+/-0.301) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.696 (+/-0.307) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.661 (+/-0.311) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.685 (+/-0.367) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.660 (+/-0.313) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.684 (+/-0.367) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.659 (+/-0.312) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.659 (+/-0.314) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.658 (+/-0.313) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.658 (+/-0.310) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.695 (+/-0.302) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.695 (+/-0.301) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.696 (+/-0.308) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.662 (+/-0.312) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.685 (+/-0.367) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.660 (+/-0.313) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.684 (+/-0.367) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.659 (+/-0.312) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.658 (+/-0.314) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.658 (+/-0.312) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.658 (+/-0.310) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.699 (+/-0.307) for {'C': 1.0, 'kernel': 'linear'}
0.696 (+/-0.304) for {'C': 10.0, 'kernel': 'linear'}
0.663 (+/-0.314) for {'C': 100.0, 'kernel': 'linear'}
0.660 (+/-0.316) for {'C': 1000.0, 'kernel': 'linear'}
0.634 (+/-0.321) for {'C': 10000.0, 'kernel': 'linear'}
0.632 (+/-0.323) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.20      0.17      0.18         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.99      0.99       629

本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6985561464259213
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.604 (+/-0.176) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.614 (+/-0.292) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.586 (+/-0.153) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.582 (+/-0.145) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.603 (+/-0.163) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.600 (+/-0.165) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.594 (+/-0.162) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.603 (+/-0.182) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.585 (+/-0.196) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.586 (+/-0.195) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.587 (+/-0.195) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.616 (+/-0.291) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.584 (+/-0.177) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.580 (+/-0.144) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.588 (+/-0.153) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.597 (+/-0.161) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.610 (+/-0.184) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.597 (+/-0.180) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.589 (+/-0.193) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.585 (+/-0.196) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.586 (+/-0.196) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.588 (+/-0.195) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.599 (+/-0.278) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.585 (+/-0.176) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.582 (+/-0.145) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.585 (+/-0.152) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.611 (+/-0.183) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.616 (+/-0.182) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.584 (+/-0.189) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.589 (+/-0.193) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.585 (+/-0.196) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.587 (+/-0.195) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.587 (+/-0.195) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.602 (+/-0.278) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.580 (+/-0.150) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.589 (+/-0.153) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.595 (+/-0.155) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.609 (+/-0.192) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.592 (+/-0.188) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.586 (+/-0.196) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.588 (+/-0.194) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.586 (+/-0.196) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.586 (+/-0.195) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.588 (+/-0.194) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.604 (+/-0.277) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.575 (+/-0.143) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.586 (+/-0.149) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.599 (+/-0.149) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.580 (+/-0.171) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.591 (+/-0.188) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.581 (+/-0.191) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.586 (+/-0.195) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.585 (+/-0.196) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.585 (+/-0.196) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.588 (+/-0.194) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.607 (+/-0.277) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.581 (+/-0.144) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.587 (+/-0.152) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.587 (+/-0.177) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.588 (+/-0.194) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.591 (+/-0.188) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.585 (+/-0.197) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.589 (+/-0.194) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.585 (+/-0.196) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.586 (+/-0.195) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.588 (+/-0.194) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.604 (+/-0.277) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.581 (+/-0.144) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.590 (+/-0.155) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.586 (+/-0.178) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.588 (+/-0.194) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.591 (+/-0.189) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.577 (+/-0.174) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.589 (+/-0.193) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.586 (+/-0.196) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.587 (+/-0.194) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.587 (+/-0.195) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.604 (+/-0.277) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.580 (+/-0.144) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.590 (+/-0.155) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.585 (+/-0.178) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.588 (+/-0.183) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.588 (+/-0.194) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.573 (+/-0.167) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.585 (+/-0.196) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.586 (+/-0.195) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.586 (+/-0.195) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.587 (+/-0.195) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.606 (+/-0.277) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.580 (+/-0.144) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.603 (+/-0.180) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.585 (+/-0.178) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.587 (+/-0.183) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.588 (+/-0.194) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.573 (+/-0.167) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.585 (+/-0.196) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.586 (+/-0.195) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.586 (+/-0.195) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.586 (+/-0.195) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.606 (+/-0.277) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.582 (+/-0.146) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.593 (+/-0.160) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.585 (+/-0.178) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.587 (+/-0.184) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.588 (+/-0.194) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.573 (+/-0.167) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.585 (+/-0.196) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.586 (+/-0.195) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.586 (+/-0.196) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.586 (+/-0.195) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.609 (+/-0.276) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.582 (+/-0.146) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.596 (+/-0.167) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.585 (+/-0.178) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.587 (+/-0.184) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.588 (+/-0.194) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.573 (+/-0.167) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.586 (+/-0.196) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.586 (+/-0.195) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.586 (+/-0.196) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.587 (+/-0.195) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.798 (+/-0.437) for {'C': 0.1, 'kernel': 'linear'}
0.635 (+/-0.198) for {'C': 1.0, 'kernel': 'linear'}
0.586 (+/-0.152) for {'C': 10.0, 'kernel': 'linear'}
0.587 (+/-0.195) for {'C': 100.0, 'kernel': 'linear'}
0.589 (+/-0.193) for {'C': 1000.0, 'kernel': 'linear'}
0.582 (+/-0.201) for {'C': 10000.0, 'kernel': 'linear'}
0.582 (+/-0.201) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.696 (+/-0.300) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.694 (+/-0.298) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.694 (+/-0.299) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.694 (+/-0.300) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.695 (+/-0.304) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.694 (+/-0.304) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.716 (+/-0.351) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.699 (+/-0.345) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.656 (+/-0.311) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.657 (+/-0.313) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.658 (+/-0.313) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.694 (+/-0.298) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.692 (+/-0.295) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.694 (+/-0.300) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.694 (+/-0.303) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.694 (+/-0.305) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.717 (+/-0.353) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.697 (+/-0.345) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.682 (+/-0.366) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.656 (+/-0.311) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.656 (+/-0.311) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.658 (+/-0.314) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.693 (+/-0.299) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.693 (+/-0.297) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.694 (+/-0.303) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.693 (+/-0.304) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.717 (+/-0.354) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.742 (+/-0.315) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.680 (+/-0.365) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.681 (+/-0.366) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.656 (+/-0.311) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.657 (+/-0.313) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.658 (+/-0.312) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.694 (+/-0.300) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.693 (+/-0.297) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.694 (+/-0.305) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.718 (+/-0.351) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.716 (+/-0.351) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.707 (+/-0.339) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.655 (+/-0.314) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.681 (+/-0.365) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.657 (+/-0.312) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.657 (+/-0.312) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.658 (+/-0.313) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.694 (+/-0.301) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.693 (+/-0.297) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.694 (+/-0.305) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.742 (+/-0.315) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.681 (+/-0.364) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.706 (+/-0.338) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.655 (+/-0.313) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.656 (+/-0.313) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.656 (+/-0.310) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.657 (+/-0.309) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.659 (+/-0.311) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.694 (+/-0.302) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.694 (+/-0.299) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.694 (+/-0.305) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.708 (+/-0.341) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.680 (+/-0.367) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.706 (+/-0.339) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.655 (+/-0.312) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.681 (+/-0.366) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.656 (+/-0.309) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.657 (+/-0.311) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.659 (+/-0.312) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.694 (+/-0.301) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.694 (+/-0.299) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.694 (+/-0.305) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.708 (+/-0.341) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.680 (+/-0.368) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.705 (+/-0.339) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.655 (+/-0.313) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.681 (+/-0.366) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.657 (+/-0.310) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.658 (+/-0.311) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.658 (+/-0.310) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.694 (+/-0.301) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.693 (+/-0.299) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.694 (+/-0.305) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.708 (+/-0.340) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.705 (+/-0.341) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.680 (+/-0.365) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.655 (+/-0.313) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.656 (+/-0.311) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.657 (+/-0.310) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.658 (+/-0.311) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.658 (+/-0.311) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.694 (+/-0.302) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.693 (+/-0.299) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.694 (+/-0.306) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.708 (+/-0.340) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.705 (+/-0.340) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.680 (+/-0.365) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.655 (+/-0.313) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.656 (+/-0.311) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.657 (+/-0.311) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.657 (+/-0.311) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.658 (+/-0.310) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.694 (+/-0.302) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.694 (+/-0.299) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.694 (+/-0.306) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.708 (+/-0.340) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.705 (+/-0.341) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.680 (+/-0.365) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.655 (+/-0.314) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.656 (+/-0.311) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.657 (+/-0.311) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.657 (+/-0.311) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.658 (+/-0.310) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.695 (+/-0.301) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.694 (+/-0.299) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.694 (+/-0.306) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.708 (+/-0.340) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.705 (+/-0.341) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.680 (+/-0.365) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.655 (+/-0.314) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.656 (+/-0.312) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.657 (+/-0.311) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.657 (+/-0.310) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.658 (+/-0.311) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.700 (+/-0.309) for {'C': 0.1, 'kernel': 'linear'}
0.698 (+/-0.305) for {'C': 1.0, 'kernel': 'linear'}
0.694 (+/-0.301) for {'C': 10.0, 'kernel': 'linear'}
0.657 (+/-0.313) for {'C': 100.0, 'kernel': 'linear'}
0.659 (+/-0.314) for {'C': 1000.0, 'kernel': 'linear'}
0.633 (+/-0.321) for {'C': 10000.0, 'kernel': 'linear'}
0.632 (+/-0.323) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.07      0.17      0.10         6
          1       0.99      0.98      0.98       623

avg / total       0.98      0.97      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.7421448594278177
循环迭代之前，delta is: [3.69042656e+06 6.01892829e-06]
执行tunemodel()函数前，使用的grid search parameter是：
 [{'C': array([3981071.70553497, 4365158.32240166, 4786300.92322638,
       5248074.60249772, 5754399.37337156, 6309573.44480193,
       6918309.70918936, 7585775.75029184, 8317637.7110267 ,
       9120108.39355909, 9999999.99999999]), 'kernel': ['rbf'], 'gamma': array([2.51188643e-06, 2.75422870e-06, 3.01995172e-06, 3.31131121e-06,
       3.63078055e-06, 3.98107171e-06, 4.36515832e-06, 4.78630092e-06,
       5.24807460e-06, 5.75439937e-06, 6.30957344e-06])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.595 (+/-0.160) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.651 (+/-0.296) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.594 (+/-0.155) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.636 (+/-0.286) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.602 (+/-0.165) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.597 (+/-0.155) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.600 (+/-0.176) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.648 (+/-0.286) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.597 (+/-0.192) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.626 (+/-0.188) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.621 (+/-0.187) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.597 (+/-0.161) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.651 (+/-0.296) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.596 (+/-0.155) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.632 (+/-0.286) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.602 (+/-0.165) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.596 (+/-0.155) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.602 (+/-0.177) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.656 (+/-0.292) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.597 (+/-0.192) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.626 (+/-0.188) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.604 (+/-0.161) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.591 (+/-0.154) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.649 (+/-0.296) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.594 (+/-0.155) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.633 (+/-0.287) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.598 (+/-0.160) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.597 (+/-0.158) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.607 (+/-0.180) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.645 (+/-0.287) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.597 (+/-0.192) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.626 (+/-0.188) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.604 (+/-0.161) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.595 (+/-0.160) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.649 (+/-0.296) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.592 (+/-0.155) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.635 (+/-0.287) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.609 (+/-0.181) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.596 (+/-0.159) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.610 (+/-0.180) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.645 (+/-0.287) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.597 (+/-0.192) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.627 (+/-0.197) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.603 (+/-0.162) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.595 (+/-0.160) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.652 (+/-0.295) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.593 (+/-0.155) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.643 (+/-0.285) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.609 (+/-0.180) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.600 (+/-0.158) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.607 (+/-0.180) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.639 (+/-0.288) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.596 (+/-0.192) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.622 (+/-0.186) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.603 (+/-0.162) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.591 (+/-0.154) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.652 (+/-0.295) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.593 (+/-0.155) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.649 (+/-0.287) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.609 (+/-0.180) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.600 (+/-0.158) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.592 (+/-0.192) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.639 (+/-0.288) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.596 (+/-0.192) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.609 (+/-0.201) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.591 (+/-0.177) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.591 (+/-0.154) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.652 (+/-0.295) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.592 (+/-0.155) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.646 (+/-0.287) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.609 (+/-0.180) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.600 (+/-0.158) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.592 (+/-0.192) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.628 (+/-0.300) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.598 (+/-0.192) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.609 (+/-0.201) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.589 (+/-0.174) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.595 (+/-0.160) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.644 (+/-0.288) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.597 (+/-0.155) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.638 (+/-0.280) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.609 (+/-0.180) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.591 (+/-0.151) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.592 (+/-0.192) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.603 (+/-0.196) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.598 (+/-0.192) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.609 (+/-0.201) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.598 (+/-0.194) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.597 (+/-0.160) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.650 (+/-0.287) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.599 (+/-0.158) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.636 (+/-0.280) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.612 (+/-0.186) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.595 (+/-0.174) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.590 (+/-0.191) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.612 (+/-0.225) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.597 (+/-0.192) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.609 (+/-0.201) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.598 (+/-0.194) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.600 (+/-0.164) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.650 (+/-0.287) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.599 (+/-0.158) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.638 (+/-0.280) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.612 (+/-0.186) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.595 (+/-0.174) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.590 (+/-0.191) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.606 (+/-0.196) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.594 (+/-0.191) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.609 (+/-0.201) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.598 (+/-0.194) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.601 (+/-0.164) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.650 (+/-0.287) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.600 (+/-0.159) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.636 (+/-0.280) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.612 (+/-0.186) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.585 (+/-0.184) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.586 (+/-0.186) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.610 (+/-0.199) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.594 (+/-0.191) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.609 (+/-0.201) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.598 (+/-0.194) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.690 (+/-0.300) for {'C': 1.0, 'kernel': 'linear'}
0.613 (+/-0.190) for {'C': 10.0, 'kernel': 'linear'}
0.608 (+/-0.197) for {'C': 100.0, 'kernel': 'linear'}
0.594 (+/-0.194) for {'C': 1000.0, 'kernel': 'linear'}
0.582 (+/-0.201) for {'C': 10000.0, 'kernel': 'linear'}
0.582 (+/-0.201) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.695 (+/-0.303) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.697 (+/-0.308) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.695 (+/-0.300) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.696 (+/-0.304) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.695 (+/-0.300) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.695 (+/-0.303) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.678 (+/-0.288) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.697 (+/-0.308) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.662 (+/-0.309) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.680 (+/-0.294) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.696 (+/-0.306) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.695 (+/-0.303) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.697 (+/-0.308) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.695 (+/-0.302) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.696 (+/-0.304) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.695 (+/-0.300) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.695 (+/-0.303) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.679 (+/-0.288) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.697 (+/-0.308) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.662 (+/-0.309) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.680 (+/-0.295) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.679 (+/-0.293) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.695 (+/-0.303) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.697 (+/-0.307) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.695 (+/-0.300) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.696 (+/-0.304) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.695 (+/-0.301) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.695 (+/-0.302) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.679 (+/-0.288) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.697 (+/-0.308) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.662 (+/-0.309) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.680 (+/-0.295) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.679 (+/-0.293) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.695 (+/-0.304) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.697 (+/-0.307) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.695 (+/-0.300) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.696 (+/-0.304) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.695 (+/-0.300) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.695 (+/-0.301) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.679 (+/-0.289) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.697 (+/-0.308) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.662 (+/-0.309) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.680 (+/-0.295) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.679 (+/-0.293) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.695 (+/-0.304) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.697 (+/-0.308) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.695 (+/-0.301) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.697 (+/-0.305) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.695 (+/-0.301) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.696 (+/-0.304) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.679 (+/-0.288) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.680 (+/-0.296) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.662 (+/-0.309) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.680 (+/-0.295) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.679 (+/-0.293) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.695 (+/-0.304) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.697 (+/-0.308) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.695 (+/-0.301) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.697 (+/-0.306) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.695 (+/-0.301) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.696 (+/-0.304) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.662 (+/-0.308) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.680 (+/-0.297) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.662 (+/-0.309) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.663 (+/-0.315) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.662 (+/-0.314) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.695 (+/-0.304) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.697 (+/-0.308) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.695 (+/-0.300) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.697 (+/-0.305) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.695 (+/-0.301) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.696 (+/-0.304) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.662 (+/-0.308) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.663 (+/-0.317) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.663 (+/-0.310) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.663 (+/-0.315) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.662 (+/-0.313) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.695 (+/-0.304) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.697 (+/-0.308) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.696 (+/-0.303) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.696 (+/-0.305) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.695 (+/-0.301) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.679 (+/-0.291) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.662 (+/-0.308) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.663 (+/-0.316) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.663 (+/-0.310) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.663 (+/-0.315) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.662 (+/-0.314) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.695 (+/-0.304) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.697 (+/-0.308) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.696 (+/-0.303) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.696 (+/-0.305) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.695 (+/-0.301) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.678 (+/-0.289) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.662 (+/-0.308) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.663 (+/-0.317) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.662 (+/-0.309) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.663 (+/-0.315) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.662 (+/-0.314) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.695 (+/-0.304) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.697 (+/-0.308) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.696 (+/-0.303) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.696 (+/-0.306) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.695 (+/-0.301) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.678 (+/-0.289) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.662 (+/-0.308) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.663 (+/-0.316) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.662 (+/-0.309) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.663 (+/-0.316) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.662 (+/-0.314) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.695 (+/-0.305) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.697 (+/-0.308) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.696 (+/-0.302) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.680 (+/-0.294) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.695 (+/-0.301) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.661 (+/-0.310) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.662 (+/-0.308) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.663 (+/-0.316) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.662 (+/-0.309) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.663 (+/-0.316) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.662 (+/-0.314) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.699 (+/-0.307) for {'C': 1.0, 'kernel': 'linear'}
0.696 (+/-0.304) for {'C': 10.0, 'kernel': 'linear'}
0.663 (+/-0.314) for {'C': 100.0, 'kernel': 'linear'}
0.660 (+/-0.316) for {'C': 1000.0, 'kernel': 'linear'}
0.634 (+/-0.321) for {'C': 10000.0, 'kernel': 'linear'}
0.632 (+/-0.323) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.20      0.17      0.18         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.99      0.99       629

本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6985561464259213
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.589 (+/-0.153) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.608 (+/-0.171) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.588 (+/-0.155) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.630 (+/-0.280) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.592 (+/-0.161) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.595 (+/-0.155) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.593 (+/-0.177) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.600 (+/-0.166) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.594 (+/-0.191) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.605 (+/-0.181) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.609 (+/-0.192) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.590 (+/-0.155) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.608 (+/-0.171) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.588 (+/-0.155) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.625 (+/-0.279) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.590 (+/-0.162) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.595 (+/-0.155) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.595 (+/-0.178) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.608 (+/-0.186) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.592 (+/-0.192) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.604 (+/-0.182) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.593 (+/-0.162) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.590 (+/-0.155) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.606 (+/-0.169) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.588 (+/-0.155) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.621 (+/-0.280) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.589 (+/-0.163) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.596 (+/-0.159) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.599 (+/-0.183) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.597 (+/-0.166) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.592 (+/-0.192) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.604 (+/-0.182) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.593 (+/-0.162) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.590 (+/-0.155) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.603 (+/-0.162) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.586 (+/-0.155) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.626 (+/-0.282) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.599 (+/-0.186) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.594 (+/-0.159) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.606 (+/-0.181) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.597 (+/-0.166) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.591 (+/-0.192) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.604 (+/-0.182) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.593 (+/-0.163) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.583 (+/-0.145) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.606 (+/-0.164) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.586 (+/-0.155) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.632 (+/-0.282) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.599 (+/-0.186) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.599 (+/-0.149) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.606 (+/-0.181) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.590 (+/-0.160) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.591 (+/-0.192) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.604 (+/-0.182) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.593 (+/-0.163) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.586 (+/-0.149) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.606 (+/-0.164) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.587 (+/-0.155) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.635 (+/-0.284) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.599 (+/-0.186) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.599 (+/-0.149) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.593 (+/-0.192) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.589 (+/-0.161) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.591 (+/-0.192) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.590 (+/-0.193) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.580 (+/-0.171) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.583 (+/-0.145) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.606 (+/-0.164) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.586 (+/-0.155) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.634 (+/-0.285) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.599 (+/-0.186) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.599 (+/-0.149) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.593 (+/-0.192) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.578 (+/-0.170) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.591 (+/-0.192) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.589 (+/-0.193) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.580 (+/-0.171) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.587 (+/-0.152) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.606 (+/-0.164) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.587 (+/-0.155) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.634 (+/-0.285) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.599 (+/-0.186) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.590 (+/-0.141) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.592 (+/-0.193) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.577 (+/-0.170) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.590 (+/-0.193) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.589 (+/-0.193) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.588 (+/-0.194) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.589 (+/-0.152) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.612 (+/-0.167) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.590 (+/-0.159) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.634 (+/-0.285) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.602 (+/-0.192) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.598 (+/-0.166) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.589 (+/-0.192) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.577 (+/-0.170) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.590 (+/-0.193) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.589 (+/-0.193) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.588 (+/-0.194) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.588 (+/-0.151) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.612 (+/-0.167) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.590 (+/-0.159) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.634 (+/-0.285) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.602 (+/-0.192) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.597 (+/-0.166) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.588 (+/-0.192) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.579 (+/-0.172) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.587 (+/-0.192) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.589 (+/-0.193) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.588 (+/-0.194) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.587 (+/-0.152) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.612 (+/-0.167) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.593 (+/-0.160) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.627 (+/-0.282) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.602 (+/-0.192) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.587 (+/-0.177) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.584 (+/-0.187) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.579 (+/-0.172) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.588 (+/-0.191) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.592 (+/-0.188) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.588 (+/-0.194) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.798 (+/-0.437) for {'C': 0.1, 'kernel': 'linear'}
0.635 (+/-0.198) for {'C': 1.0, 'kernel': 'linear'}
0.586 (+/-0.152) for {'C': 10.0, 'kernel': 'linear'}
0.587 (+/-0.195) for {'C': 100.0, 'kernel': 'linear'}
0.589 (+/-0.193) for {'C': 1000.0, 'kernel': 'linear'}
0.582 (+/-0.201) for {'C': 10000.0, 'kernel': 'linear'}
0.582 (+/-0.201) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.694 (+/-0.305) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.696 (+/-0.307) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.694 (+/-0.300) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.695 (+/-0.308) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.692 (+/-0.299) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.718 (+/-0.351) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.676 (+/-0.292) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.693 (+/-0.306) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.684 (+/-0.368) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.700 (+/-0.347) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.716 (+/-0.351) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.694 (+/-0.305) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.696 (+/-0.307) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.693 (+/-0.300) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.695 (+/-0.308) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.692 (+/-0.299) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.718 (+/-0.352) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.676 (+/-0.293) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.693 (+/-0.306) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.684 (+/-0.367) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.700 (+/-0.346) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.699 (+/-0.343) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.694 (+/-0.305) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.695 (+/-0.307) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.693 (+/-0.300) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.694 (+/-0.308) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.692 (+/-0.298) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.718 (+/-0.351) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.675 (+/-0.291) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.693 (+/-0.306) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.684 (+/-0.366) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.700 (+/-0.346) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.699 (+/-0.343) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.694 (+/-0.305) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.695 (+/-0.306) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.693 (+/-0.299) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.695 (+/-0.308) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.692 (+/-0.298) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.717 (+/-0.350) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.700 (+/-0.347) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.693 (+/-0.306) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.684 (+/-0.366) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.699 (+/-0.347) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.698 (+/-0.343) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.694 (+/-0.304) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.696 (+/-0.307) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.693 (+/-0.299) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.695 (+/-0.309) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.692 (+/-0.298) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.742 (+/-0.314) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.700 (+/-0.347) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.676 (+/-0.293) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.684 (+/-0.366) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.699 (+/-0.347) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.698 (+/-0.343) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.694 (+/-0.305) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.696 (+/-0.307) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.693 (+/-0.300) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.695 (+/-0.310) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.692 (+/-0.298) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.742 (+/-0.315) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.683 (+/-0.368) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.676 (+/-0.293) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.684 (+/-0.365) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.682 (+/-0.367) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.681 (+/-0.364) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.694 (+/-0.304) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.696 (+/-0.307) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.693 (+/-0.300) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.695 (+/-0.310) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.692 (+/-0.298) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.742 (+/-0.315) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.683 (+/-0.368) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.659 (+/-0.312) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.684 (+/-0.365) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.682 (+/-0.367) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.681 (+/-0.365) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.694 (+/-0.304) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.696 (+/-0.307) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.693 (+/-0.301) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.695 (+/-0.310) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.691 (+/-0.299) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.725 (+/-0.313) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.683 (+/-0.367) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.658 (+/-0.312) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.683 (+/-0.364) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.682 (+/-0.367) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.681 (+/-0.365) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.694 (+/-0.304) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.696 (+/-0.307) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.693 (+/-0.302) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.694 (+/-0.311) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.692 (+/-0.299) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.725 (+/-0.313) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.682 (+/-0.367) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.658 (+/-0.311) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.683 (+/-0.364) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.682 (+/-0.366) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.681 (+/-0.365) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.694 (+/-0.304) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.696 (+/-0.307) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.693 (+/-0.302) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.694 (+/-0.311) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.691 (+/-0.299) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.725 (+/-0.313) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.682 (+/-0.367) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.658 (+/-0.311) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.683 (+/-0.365) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.681 (+/-0.367) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.680 (+/-0.367) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.694 (+/-0.305) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.696 (+/-0.307) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.693 (+/-0.302) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.678 (+/-0.298) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.691 (+/-0.299) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.708 (+/-0.341) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.682 (+/-0.367) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.658 (+/-0.312) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.683 (+/-0.366) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.706 (+/-0.340) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.680 (+/-0.367) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.700 (+/-0.309) for {'C': 0.1, 'kernel': 'linear'}
0.698 (+/-0.305) for {'C': 1.0, 'kernel': 'linear'}
0.694 (+/-0.301) for {'C': 10.0, 'kernel': 'linear'}
0.657 (+/-0.313) for {'C': 100.0, 'kernel': 'linear'}
0.659 (+/-0.314) for {'C': 1000.0, 'kernel': 'linear'}
0.633 (+/-0.321) for {'C': 10000.0, 'kernel': 'linear'}
0.632 (+/-0.323) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.07      0.17      0.10         6
          1       0.99      0.98      0.98       623

avg / total       0.98      0.97      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.7421448594278177
第0轮loop中，tunemodel函数得到的最优参数是： ([{'C': array([5248074.60249772, 5345643.5939697 , 5445026.5284242 ,
       5546257.1295791 , 5649369.74812302, 5754399.37337156,
       5861381.64514028, 5970352.86583836, 6081350.01278718,
       6194410.75076781, 6309573.44480193]), 'kernel': ['rbf'], 'gamma': array([3.63078055e-06, 3.69828180e-06, 3.76703799e-06, 3.83707245e-06,
       3.90840896e-06, 3.98107171e-06, 4.05508535e-06, 4.13047502e-06,
       4.20726628e-06, 4.28548520e-06, 4.36515832e-06])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}], {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}, 0.7421448594278177)
这是第0次迭代微调C和gamma。
第0次迭代，得到delta： [555174.07143037      0.        ]
执行tunemodel()函数前，使用的grid search parameter是：
 [{'C': array([5248074.60249772, 5345643.5939697 , 5445026.5284242 ,
       5546257.1295791 , 5649369.74812302, 5754399.37337156,
       5861381.64514028, 5970352.86583836, 6081350.01278718,
       6194410.75076781, 6309573.44480193]), 'kernel': ['rbf'], 'gamma': array([3.63078055e-06, 3.69828180e-06, 3.76703799e-06, 3.83707245e-06,
       3.90840896e-06, 3.98107171e-06, 4.05508535e-06, 4.13047502e-06,
       4.20726628e-06, 4.28548520e-06, 4.36515832e-06])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.609 (+/-0.181) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.597 (+/-0.161) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.601 (+/-0.160) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.601 (+/-0.159) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.607 (+/-0.163) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.596 (+/-0.159) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.601 (+/-0.183) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.645 (+/-0.294) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.604 (+/-0.160) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.622 (+/-0.198) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.610 (+/-0.180) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.601 (+/-0.161) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.597 (+/-0.161) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.601 (+/-0.160) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.600 (+/-0.159) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.605 (+/-0.163) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.600 (+/-0.158) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.601 (+/-0.183) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.645 (+/-0.294) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.604 (+/-0.160) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.612 (+/-0.212) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.610 (+/-0.180) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.609 (+/-0.181) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.597 (+/-0.161) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.602 (+/-0.160) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.598 (+/-0.158) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.605 (+/-0.163) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.600 (+/-0.158) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.601 (+/-0.183) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.648 (+/-0.293) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.604 (+/-0.160) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.612 (+/-0.212) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.609 (+/-0.180) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.600 (+/-0.161) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.599 (+/-0.162) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.602 (+/-0.160) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.600 (+/-0.159) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.605 (+/-0.163) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.600 (+/-0.158) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.601 (+/-0.183) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.648 (+/-0.293) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.604 (+/-0.160) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.612 (+/-0.212) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.607 (+/-0.180) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.609 (+/-0.180) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.599 (+/-0.162) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.607 (+/-0.164) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.600 (+/-0.159) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.605 (+/-0.163) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.600 (+/-0.158) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.601 (+/-0.183) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.648 (+/-0.293) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.610 (+/-0.164) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.612 (+/-0.212) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.607 (+/-0.180) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.609 (+/-0.180) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.608 (+/-0.182) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.607 (+/-0.164) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.600 (+/-0.159) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.605 (+/-0.163) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.600 (+/-0.158) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.601 (+/-0.183) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.648 (+/-0.293) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.610 (+/-0.164) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.612 (+/-0.212) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.607 (+/-0.180) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.609 (+/-0.180) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.608 (+/-0.182) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.607 (+/-0.164) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.595 (+/-0.157) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.605 (+/-0.163) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.601 (+/-0.159) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.610 (+/-0.164) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.635 (+/-0.307) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.610 (+/-0.164) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.612 (+/-0.212) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.605 (+/-0.181) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.609 (+/-0.180) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.608 (+/-0.182) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.607 (+/-0.164) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.595 (+/-0.157) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.605 (+/-0.163) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.600 (+/-0.158) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.610 (+/-0.164) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.635 (+/-0.307) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.610 (+/-0.164) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.620 (+/-0.238) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.592 (+/-0.192) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.609 (+/-0.180) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.608 (+/-0.182) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.607 (+/-0.164) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.595 (+/-0.157) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.605 (+/-0.163) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.600 (+/-0.158) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.598 (+/-0.177) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.635 (+/-0.307) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.627 (+/-0.210) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.618 (+/-0.238) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.592 (+/-0.192) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.609 (+/-0.181) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.608 (+/-0.182) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.607 (+/-0.164) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.596 (+/-0.157) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.605 (+/-0.163) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.600 (+/-0.158) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.598 (+/-0.177) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.635 (+/-0.307) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.627 (+/-0.210) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.609 (+/-0.212) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.592 (+/-0.192) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.609 (+/-0.180) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.612 (+/-0.185) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.607 (+/-0.164) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.596 (+/-0.157) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.605 (+/-0.163) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.600 (+/-0.158) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.601 (+/-0.183) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.635 (+/-0.307) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.610 (+/-0.164) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.612 (+/-0.212) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.592 (+/-0.192) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.690 (+/-0.300) for {'C': 1.0, 'kernel': 'linear'}
0.613 (+/-0.190) for {'C': 10.0, 'kernel': 'linear'}
0.608 (+/-0.197) for {'C': 100.0, 'kernel': 'linear'}
0.594 (+/-0.194) for {'C': 1000.0, 'kernel': 'linear'}
0.582 (+/-0.201) for {'C': 10000.0, 'kernel': 'linear'}
0.582 (+/-0.201) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.695 (+/-0.300) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.694 (+/-0.298) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.695 (+/-0.301) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.696 (+/-0.303) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.696 (+/-0.303) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.695 (+/-0.301) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.663 (+/-0.315) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.680 (+/-0.296) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.680 (+/-0.293) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.680 (+/-0.295) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.679 (+/-0.289) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.695 (+/-0.301) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.694 (+/-0.298) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.695 (+/-0.301) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.696 (+/-0.302) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.696 (+/-0.302) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.696 (+/-0.304) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.663 (+/-0.315) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.680 (+/-0.296) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.680 (+/-0.293) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.663 (+/-0.316) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.679 (+/-0.289) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.695 (+/-0.300) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.694 (+/-0.298) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.696 (+/-0.302) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.695 (+/-0.302) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.696 (+/-0.302) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.696 (+/-0.304) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.663 (+/-0.315) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.681 (+/-0.296) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.680 (+/-0.293) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.663 (+/-0.316) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.679 (+/-0.288) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.695 (+/-0.300) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.695 (+/-0.298) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.696 (+/-0.302) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.696 (+/-0.302) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.696 (+/-0.302) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.696 (+/-0.304) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.663 (+/-0.315) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.681 (+/-0.296) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.680 (+/-0.293) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.663 (+/-0.316) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.679 (+/-0.288) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.695 (+/-0.301) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.695 (+/-0.298) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.696 (+/-0.302) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.696 (+/-0.302) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.696 (+/-0.303) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.696 (+/-0.304) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.663 (+/-0.315) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.681 (+/-0.296) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.696 (+/-0.306) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.663 (+/-0.316) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.679 (+/-0.288) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.695 (+/-0.301) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.695 (+/-0.298) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.696 (+/-0.302) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.696 (+/-0.302) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.696 (+/-0.302) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.696 (+/-0.304) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.663 (+/-0.315) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.681 (+/-0.296) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.696 (+/-0.306) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.663 (+/-0.316) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.679 (+/-0.288) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.695 (+/-0.301) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.695 (+/-0.298) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.696 (+/-0.302) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.695 (+/-0.302) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.696 (+/-0.302) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.696 (+/-0.304) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.680 (+/-0.293) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.664 (+/-0.317) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.696 (+/-0.306) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.663 (+/-0.316) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.679 (+/-0.288) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.695 (+/-0.301) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.695 (+/-0.298) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.696 (+/-0.302) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.695 (+/-0.302) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.696 (+/-0.302) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.696 (+/-0.304) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.680 (+/-0.293) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.664 (+/-0.317) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.696 (+/-0.306) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.664 (+/-0.316) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.662 (+/-0.308) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.695 (+/-0.301) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.695 (+/-0.298) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.696 (+/-0.302) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.695 (+/-0.302) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.696 (+/-0.303) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.696 (+/-0.304) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.663 (+/-0.314) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.664 (+/-0.317) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.697 (+/-0.308) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.663 (+/-0.316) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.662 (+/-0.308) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.695 (+/-0.300) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.695 (+/-0.298) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.696 (+/-0.302) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.695 (+/-0.302) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.696 (+/-0.303) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.696 (+/-0.304) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.663 (+/-0.314) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.664 (+/-0.317) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.697 (+/-0.308) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.663 (+/-0.315) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.662 (+/-0.308) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.695 (+/-0.301) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.695 (+/-0.298) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.696 (+/-0.302) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.695 (+/-0.302) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.696 (+/-0.303) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.696 (+/-0.304) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.663 (+/-0.315) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.664 (+/-0.317) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.696 (+/-0.306) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.663 (+/-0.316) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.662 (+/-0.308) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.699 (+/-0.307) for {'C': 1.0, 'kernel': 'linear'}
0.696 (+/-0.304) for {'C': 10.0, 'kernel': 'linear'}
0.663 (+/-0.314) for {'C': 100.0, 'kernel': 'linear'}
0.660 (+/-0.316) for {'C': 1000.0, 'kernel': 'linear'}
0.634 (+/-0.321) for {'C': 10000.0, 'kernel': 'linear'}
0.632 (+/-0.323) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.20      0.17      0.18         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.99      0.99       629

本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6985561464259213
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.599 (+/-0.186) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.591 (+/-0.161) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.595 (+/-0.161) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.593 (+/-0.160) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.598 (+/-0.165) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.594 (+/-0.159) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.581 (+/-0.171) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.597 (+/-0.178) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.590 (+/-0.154) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.600 (+/-0.180) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.606 (+/-0.181) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.591 (+/-0.164) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.591 (+/-0.161) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.594 (+/-0.162) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.592 (+/-0.160) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.598 (+/-0.165) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.594 (+/-0.159) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.581 (+/-0.171) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.597 (+/-0.178) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.590 (+/-0.154) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.590 (+/-0.191) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.606 (+/-0.181) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.599 (+/-0.186) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.591 (+/-0.161) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.593 (+/-0.162) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.591 (+/-0.161) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.598 (+/-0.165) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.600 (+/-0.148) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.581 (+/-0.171) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.604 (+/-0.176) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.589 (+/-0.154) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.590 (+/-0.191) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.605 (+/-0.181) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.591 (+/-0.165) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.593 (+/-0.163) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.593 (+/-0.162) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.591 (+/-0.161) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.598 (+/-0.165) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.599 (+/-0.149) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.581 (+/-0.171) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.604 (+/-0.176) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.588 (+/-0.154) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.589 (+/-0.191) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.605 (+/-0.181) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.599 (+/-0.186) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.593 (+/-0.163) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.598 (+/-0.167) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.591 (+/-0.161) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.598 (+/-0.165) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.599 (+/-0.149) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.581 (+/-0.171) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.604 (+/-0.176) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.595 (+/-0.161) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.589 (+/-0.191) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.606 (+/-0.181) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.599 (+/-0.186) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.601 (+/-0.184) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.598 (+/-0.167) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.591 (+/-0.161) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.598 (+/-0.165) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.599 (+/-0.149) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.581 (+/-0.171) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.604 (+/-0.176) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.595 (+/-0.161) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.589 (+/-0.191) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.606 (+/-0.181) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.599 (+/-0.186) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.601 (+/-0.184) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.598 (+/-0.167) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.589 (+/-0.160) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.598 (+/-0.165) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.599 (+/-0.149) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.593 (+/-0.162) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.590 (+/-0.187) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.595 (+/-0.161) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.589 (+/-0.191) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.605 (+/-0.181) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.599 (+/-0.186) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.601 (+/-0.184) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.598 (+/-0.167) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.589 (+/-0.160) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.598 (+/-0.165) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.599 (+/-0.149) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.593 (+/-0.162) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.591 (+/-0.187) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.595 (+/-0.161) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.589 (+/-0.191) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.593 (+/-0.192) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.599 (+/-0.186) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.601 (+/-0.184) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.598 (+/-0.167) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.589 (+/-0.160) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.598 (+/-0.165) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.599 (+/-0.149) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.581 (+/-0.171) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.591 (+/-0.187) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.595 (+/-0.161) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.589 (+/-0.191) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.593 (+/-0.192) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.599 (+/-0.186) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.601 (+/-0.184) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.598 (+/-0.167) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.589 (+/-0.160) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.598 (+/-0.165) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.599 (+/-0.149) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.581 (+/-0.171) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.590 (+/-0.187) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.595 (+/-0.161) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.589 (+/-0.191) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.593 (+/-0.192) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.599 (+/-0.186) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.605 (+/-0.188) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.598 (+/-0.167) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.589 (+/-0.160) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.598 (+/-0.165) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.599 (+/-0.149) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.581 (+/-0.171) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.590 (+/-0.187) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.595 (+/-0.161) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.589 (+/-0.191) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.593 (+/-0.192) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.798 (+/-0.437) for {'C': 0.1, 'kernel': 'linear'}
0.635 (+/-0.198) for {'C': 1.0, 'kernel': 'linear'}
0.586 (+/-0.152) for {'C': 10.0, 'kernel': 'linear'}
0.587 (+/-0.195) for {'C': 100.0, 'kernel': 'linear'}
0.589 (+/-0.193) for {'C': 1000.0, 'kernel': 'linear'}
0.582 (+/-0.201) for {'C': 10000.0, 'kernel': 'linear'}
0.582 (+/-0.201) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.692 (+/-0.298) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.693 (+/-0.302) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.693 (+/-0.303) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.694 (+/-0.303) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.718 (+/-0.349) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.717 (+/-0.350) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.660 (+/-0.310) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.677 (+/-0.292) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.677 (+/-0.292) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.677 (+/-0.292) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.700 (+/-0.347) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.692 (+/-0.298) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.692 (+/-0.301) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.693 (+/-0.302) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.694 (+/-0.302) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.718 (+/-0.349) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.717 (+/-0.350) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.660 (+/-0.310) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.677 (+/-0.292) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.677 (+/-0.292) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.661 (+/-0.312) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.700 (+/-0.347) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.692 (+/-0.298) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.692 (+/-0.301) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.693 (+/-0.302) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.693 (+/-0.302) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.718 (+/-0.349) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.742 (+/-0.315) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.660 (+/-0.310) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.702 (+/-0.346) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.677 (+/-0.292) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.661 (+/-0.312) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.700 (+/-0.346) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.692 (+/-0.298) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.693 (+/-0.301) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.693 (+/-0.302) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.693 (+/-0.302) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.718 (+/-0.349) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.742 (+/-0.315) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.660 (+/-0.310) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.702 (+/-0.346) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.677 (+/-0.291) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.661 (+/-0.312) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.700 (+/-0.346) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.692 (+/-0.298) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.693 (+/-0.301) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.693 (+/-0.302) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.693 (+/-0.302) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.717 (+/-0.349) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.742 (+/-0.314) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.660 (+/-0.310) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.702 (+/-0.346) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.693 (+/-0.304) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.661 (+/-0.312) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.700 (+/-0.347) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.692 (+/-0.298) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.692 (+/-0.300) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.693 (+/-0.302) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.693 (+/-0.302) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.717 (+/-0.349) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.742 (+/-0.314) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.660 (+/-0.310) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.702 (+/-0.346) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.693 (+/-0.304) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.660 (+/-0.312) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.700 (+/-0.347) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.692 (+/-0.298) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.692 (+/-0.300) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.693 (+/-0.302) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.693 (+/-0.303) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.717 (+/-0.349) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.742 (+/-0.314) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.676 (+/-0.290) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.685 (+/-0.367) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.693 (+/-0.304) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.660 (+/-0.312) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.700 (+/-0.347) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.692 (+/-0.298) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.692 (+/-0.300) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.693 (+/-0.302) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.693 (+/-0.303) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.717 (+/-0.349) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.742 (+/-0.314) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.676 (+/-0.291) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.685 (+/-0.368) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.693 (+/-0.304) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.660 (+/-0.312) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.683 (+/-0.368) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.692 (+/-0.298) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.692 (+/-0.300) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.693 (+/-0.302) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.693 (+/-0.303) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.717 (+/-0.349) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.742 (+/-0.315) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.659 (+/-0.310) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.685 (+/-0.368) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.693 (+/-0.304) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.660 (+/-0.312) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.683 (+/-0.368) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.692 (+/-0.298) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.692 (+/-0.300) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.693 (+/-0.302) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.693 (+/-0.303) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.717 (+/-0.349) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.742 (+/-0.315) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.659 (+/-0.311) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.685 (+/-0.367) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.693 (+/-0.304) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.660 (+/-0.312) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.683 (+/-0.368) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.692 (+/-0.298) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.693 (+/-0.300) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.693 (+/-0.302) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.693 (+/-0.303) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.717 (+/-0.349) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.742 (+/-0.315) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.659 (+/-0.311) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.685 (+/-0.367) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.693 (+/-0.304) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.660 (+/-0.312) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.683 (+/-0.368) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.700 (+/-0.309) for {'C': 0.1, 'kernel': 'linear'}
0.698 (+/-0.305) for {'C': 1.0, 'kernel': 'linear'}
0.694 (+/-0.301) for {'C': 10.0, 'kernel': 'linear'}
0.657 (+/-0.313) for {'C': 100.0, 'kernel': 'linear'}
0.659 (+/-0.314) for {'C': 1000.0, 'kernel': 'linear'}
0.633 (+/-0.321) for {'C': 10000.0, 'kernel': 'linear'}
0.632 (+/-0.323) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.07      0.17      0.10         6
          1       0.99      0.98      0.98       623

avg / total       0.98      0.97      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.7424653722483305
第1轮loop中，tunemodel函数得到的最优参数是： ([{'C': array([5345643.5939697 , 5365373.99519851, 5385177.21997526,
       5405053.53708668, 5425003.21631149, 5445026.5284242 ,
       5465123.74519871, 5485295.13941203, 5505540.98484794,
       5525861.55630077, 5546257.1295791 ]), 'kernel': ['rbf'], 'gamma': array([3.90840896e-06, 3.92283463e-06, 3.93731354e-06, 3.95184589e-06,
       3.96643188e-06, 3.98107171e-06, 3.99576557e-06, 4.01051366e-06,
       4.02531619e-06, 4.04017335e-06, 4.05508535e-06])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}], {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}, 0.7424653722483305)
这是第1次迭代微调C和gamma。
第1次迭代，得到delta： [309372.84494736      0.        ]
执行tunemodel()函数前，使用的grid search parameter是：
 [{'C': array([5345643.5939697 , 5365373.99519851, 5385177.21997526,
       5405053.53708668, 5425003.21631149, 5445026.5284242 ,
       5465123.74519871, 5485295.13941203, 5505540.98484794,
       5525861.55630077, 5546257.1295791 ]), 'kernel': ['rbf'], 'gamma': array([3.90840896e-06, 3.92283463e-06, 3.93731354e-06, 3.95184589e-06,
       3.96643188e-06, 3.98107171e-06, 3.99576557e-06, 4.01051366e-06,
       4.02531619e-06, 4.04017335e-06, 4.05508535e-06])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.605 (+/-0.163) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.604 (+/-0.163) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.922834625395207e-06}
0.610 (+/-0.163) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.937313537008499e-06}
0.611 (+/-0.179) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.951845889284363e-06}
0.605 (+/-0.179) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.966431879468585e-06}
0.600 (+/-0.158) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.597 (+/-0.160) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.995765566188054e-06}
0.604 (+/-0.159) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 4.01051366086576e-06}
0.615 (+/-0.179) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 4.025316189742124e-06}
0.619 (+/-0.187) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 4.0401733537300044e-06}
0.601 (+/-0.183) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.605 (+/-0.163) for {'C': 5365373.99519851, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.604 (+/-0.163) for {'C': 5365373.99519851, 'kernel': 'rbf', 'gamma': 3.922834625395207e-06}
0.608 (+/-0.161) for {'C': 5365373.99519851, 'kernel': 'rbf', 'gamma': 3.937313537008499e-06}
0.611 (+/-0.179) for {'C': 5365373.99519851, 'kernel': 'rbf', 'gamma': 3.951845889284363e-06}
0.599 (+/-0.175) for {'C': 5365373.99519851, 'kernel': 'rbf', 'gamma': 3.966431879468585e-06}
0.600 (+/-0.158) for {'C': 5365373.99519851, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.597 (+/-0.160) for {'C': 5365373.99519851, 'kernel': 'rbf', 'gamma': 3.995765566188054e-06}
0.597 (+/-0.154) for {'C': 5365373.99519851, 'kernel': 'rbf', 'gamma': 4.01051366086576e-06}
0.615 (+/-0.179) for {'C': 5365373.99519851, 'kernel': 'rbf', 'gamma': 4.025316189742124e-06}
0.614 (+/-0.180) for {'C': 5365373.99519851, 'kernel': 'rbf', 'gamma': 4.0401733537300044e-06}
0.601 (+/-0.183) for {'C': 5365373.99519851, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.605 (+/-0.163) for {'C': 5385177.219975264, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.604 (+/-0.163) for {'C': 5385177.219975264, 'kernel': 'rbf', 'gamma': 3.922834625395207e-06}
0.608 (+/-0.161) for {'C': 5385177.219975264, 'kernel': 'rbf', 'gamma': 3.937313537008499e-06}
0.611 (+/-0.179) for {'C': 5385177.219975264, 'kernel': 'rbf', 'gamma': 3.951845889284363e-06}
0.599 (+/-0.175) for {'C': 5385177.219975264, 'kernel': 'rbf', 'gamma': 3.966431879468585e-06}
0.600 (+/-0.158) for {'C': 5385177.219975264, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.597 (+/-0.160) for {'C': 5385177.219975264, 'kernel': 'rbf', 'gamma': 3.995765566188054e-06}
0.597 (+/-0.154) for {'C': 5385177.219975264, 'kernel': 'rbf', 'gamma': 4.01051366086576e-06}
0.615 (+/-0.179) for {'C': 5385177.219975264, 'kernel': 'rbf', 'gamma': 4.025316189742124e-06}
0.614 (+/-0.180) for {'C': 5385177.219975264, 'kernel': 'rbf', 'gamma': 4.0401733537300044e-06}
0.601 (+/-0.183) for {'C': 5385177.219975264, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.605 (+/-0.163) for {'C': 5405053.537086678, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.604 (+/-0.163) for {'C': 5405053.537086678, 'kernel': 'rbf', 'gamma': 3.922834625395207e-06}
0.608 (+/-0.161) for {'C': 5405053.537086678, 'kernel': 'rbf', 'gamma': 3.937313537008499e-06}
0.611 (+/-0.179) for {'C': 5405053.537086678, 'kernel': 'rbf', 'gamma': 3.951845889284363e-06}
0.599 (+/-0.175) for {'C': 5405053.537086678, 'kernel': 'rbf', 'gamma': 3.966431879468585e-06}
0.600 (+/-0.158) for {'C': 5405053.537086678, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.597 (+/-0.160) for {'C': 5405053.537086678, 'kernel': 'rbf', 'gamma': 3.995765566188054e-06}
0.597 (+/-0.154) for {'C': 5405053.537086678, 'kernel': 'rbf', 'gamma': 4.01051366086576e-06}
0.615 (+/-0.179) for {'C': 5405053.537086678, 'kernel': 'rbf', 'gamma': 4.025316189742124e-06}
0.614 (+/-0.180) for {'C': 5405053.537086678, 'kernel': 'rbf', 'gamma': 4.0401733537300044e-06}
0.601 (+/-0.183) for {'C': 5405053.537086678, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.605 (+/-0.163) for {'C': 5425003.216311495, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.604 (+/-0.163) for {'C': 5425003.216311495, 'kernel': 'rbf', 'gamma': 3.922834625395207e-06}
0.608 (+/-0.161) for {'C': 5425003.216311495, 'kernel': 'rbf', 'gamma': 3.937313537008499e-06}
0.611 (+/-0.179) for {'C': 5425003.216311495, 'kernel': 'rbf', 'gamma': 3.951845889284363e-06}
0.599 (+/-0.175) for {'C': 5425003.216311495, 'kernel': 'rbf', 'gamma': 3.966431879468585e-06}
0.600 (+/-0.158) for {'C': 5425003.216311495, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.597 (+/-0.160) for {'C': 5425003.216311495, 'kernel': 'rbf', 'gamma': 3.995765566188054e-06}
0.597 (+/-0.154) for {'C': 5425003.216311495, 'kernel': 'rbf', 'gamma': 4.01051366086576e-06}
0.615 (+/-0.179) for {'C': 5425003.216311495, 'kernel': 'rbf', 'gamma': 4.025316189742124e-06}
0.614 (+/-0.180) for {'C': 5425003.216311495, 'kernel': 'rbf', 'gamma': 4.0401733537300044e-06}
0.601 (+/-0.183) for {'C': 5425003.216311495, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.605 (+/-0.163) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.604 (+/-0.163) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.922834625395207e-06}
0.610 (+/-0.163) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.937313537008499e-06}
0.611 (+/-0.179) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.951845889284363e-06}
0.599 (+/-0.175) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.966431879468585e-06}
0.600 (+/-0.158) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.597 (+/-0.160) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.995765566188054e-06}
0.597 (+/-0.154) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 4.01051366086576e-06}
0.615 (+/-0.179) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 4.025316189742124e-06}
0.619 (+/-0.187) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 4.0401733537300044e-06}
0.601 (+/-0.183) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.605 (+/-0.163) for {'C': 5465123.745198711, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.604 (+/-0.163) for {'C': 5465123.745198711, 'kernel': 'rbf', 'gamma': 3.922834625395207e-06}
0.610 (+/-0.163) for {'C': 5465123.745198711, 'kernel': 'rbf', 'gamma': 3.937313537008499e-06}
0.611 (+/-0.179) for {'C': 5465123.745198711, 'kernel': 'rbf', 'gamma': 3.951845889284363e-06}
0.605 (+/-0.179) for {'C': 5465123.745198711, 'kernel': 'rbf', 'gamma': 3.966431879468585e-06}
0.600 (+/-0.158) for {'C': 5465123.745198711, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.597 (+/-0.160) for {'C': 5465123.745198711, 'kernel': 'rbf', 'gamma': 3.995765566188054e-06}
0.597 (+/-0.154) for {'C': 5465123.745198711, 'kernel': 'rbf', 'gamma': 4.01051366086576e-06}
0.615 (+/-0.179) for {'C': 5465123.745198711, 'kernel': 'rbf', 'gamma': 4.025316189742124e-06}
0.619 (+/-0.187) for {'C': 5465123.745198711, 'kernel': 'rbf', 'gamma': 4.0401733537300044e-06}
0.601 (+/-0.183) for {'C': 5465123.745198711, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.605 (+/-0.163) for {'C': 5485295.139412026, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.604 (+/-0.163) for {'C': 5485295.139412026, 'kernel': 'rbf', 'gamma': 3.922834625395207e-06}
0.608 (+/-0.161) for {'C': 5485295.139412026, 'kernel': 'rbf', 'gamma': 3.937313537008499e-06}
0.611 (+/-0.179) for {'C': 5485295.139412026, 'kernel': 'rbf', 'gamma': 3.951845889284363e-06}
0.599 (+/-0.175) for {'C': 5485295.139412026, 'kernel': 'rbf', 'gamma': 3.966431879468585e-06}
0.600 (+/-0.158) for {'C': 5485295.139412026, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.598 (+/-0.160) for {'C': 5485295.139412026, 'kernel': 'rbf', 'gamma': 3.995765566188054e-06}
0.597 (+/-0.154) for {'C': 5485295.139412026, 'kernel': 'rbf', 'gamma': 4.01051366086576e-06}
0.615 (+/-0.179) for {'C': 5485295.139412026, 'kernel': 'rbf', 'gamma': 4.025316189742124e-06}
0.614 (+/-0.180) for {'C': 5485295.139412026, 'kernel': 'rbf', 'gamma': 4.0401733537300044e-06}
0.601 (+/-0.183) for {'C': 5485295.139412026, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.605 (+/-0.163) for {'C': 5505540.984847944, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.604 (+/-0.163) for {'C': 5505540.984847944, 'kernel': 'rbf', 'gamma': 3.922834625395207e-06}
0.607 (+/-0.161) for {'C': 5505540.984847944, 'kernel': 'rbf', 'gamma': 3.937313537008499e-06}
0.611 (+/-0.179) for {'C': 5505540.984847944, 'kernel': 'rbf', 'gamma': 3.951845889284363e-06}
0.599 (+/-0.175) for {'C': 5505540.984847944, 'kernel': 'rbf', 'gamma': 3.966431879468585e-06}
0.600 (+/-0.158) for {'C': 5505540.984847944, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.598 (+/-0.160) for {'C': 5505540.984847944, 'kernel': 'rbf', 'gamma': 3.995765566188054e-06}
0.597 (+/-0.154) for {'C': 5505540.984847944, 'kernel': 'rbf', 'gamma': 4.01051366086576e-06}
0.615 (+/-0.179) for {'C': 5505540.984847944, 'kernel': 'rbf', 'gamma': 4.025316189742124e-06}
0.619 (+/-0.187) for {'C': 5505540.984847944, 'kernel': 'rbf', 'gamma': 4.0401733537300044e-06}
0.601 (+/-0.183) for {'C': 5505540.984847944, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.605 (+/-0.163) for {'C': 5525861.556300772, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.604 (+/-0.163) for {'C': 5525861.556300772, 'kernel': 'rbf', 'gamma': 3.922834625395207e-06}
0.607 (+/-0.161) for {'C': 5525861.556300772, 'kernel': 'rbf', 'gamma': 3.937313537008499e-06}
0.611 (+/-0.179) for {'C': 5525861.556300772, 'kernel': 'rbf', 'gamma': 3.951845889284363e-06}
0.599 (+/-0.175) for {'C': 5525861.556300772, 'kernel': 'rbf', 'gamma': 3.966431879468585e-06}
0.600 (+/-0.158) for {'C': 5525861.556300772, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.598 (+/-0.160) for {'C': 5525861.556300772, 'kernel': 'rbf', 'gamma': 3.995765566188054e-06}
0.597 (+/-0.154) for {'C': 5525861.556300772, 'kernel': 'rbf', 'gamma': 4.01051366086576e-06}
0.619 (+/-0.181) for {'C': 5525861.556300772, 'kernel': 'rbf', 'gamma': 4.025316189742124e-06}
0.614 (+/-0.180) for {'C': 5525861.556300772, 'kernel': 'rbf', 'gamma': 4.0401733537300044e-06}
0.601 (+/-0.183) for {'C': 5525861.556300772, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.605 (+/-0.163) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.604 (+/-0.163) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.922834625395207e-06}
0.607 (+/-0.161) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.937313537008499e-06}
0.611 (+/-0.179) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.951845889284363e-06}
0.599 (+/-0.175) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.966431879468585e-06}
0.600 (+/-0.158) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.600 (+/-0.159) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.995765566188054e-06}
0.597 (+/-0.154) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 4.01051366086576e-06}
0.615 (+/-0.179) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 4.025316189742124e-06}
0.614 (+/-0.180) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 4.0401733537300044e-06}
0.601 (+/-0.183) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.690 (+/-0.300) for {'C': 1.0, 'kernel': 'linear'}
0.613 (+/-0.190) for {'C': 10.0, 'kernel': 'linear'}
0.608 (+/-0.197) for {'C': 100.0, 'kernel': 'linear'}
0.594 (+/-0.194) for {'C': 1000.0, 'kernel': 'linear'}
0.582 (+/-0.201) for {'C': 10000.0, 'kernel': 'linear'}
0.582 (+/-0.201) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.696 (+/-0.302) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.696 (+/-0.303) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.922834625395207e-06}
0.696 (+/-0.304) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.937313537008499e-06}
0.696 (+/-0.303) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.951845889284363e-06}
0.695 (+/-0.303) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.966431879468585e-06}
0.696 (+/-0.304) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.695 (+/-0.301) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.995765566188054e-06}
0.696 (+/-0.304) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 4.01051366086576e-06}
0.696 (+/-0.305) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 4.025316189742124e-06}
0.680 (+/-0.294) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 4.0401733537300044e-06}
0.663 (+/-0.315) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.696 (+/-0.302) for {'C': 5365373.99519851, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.696 (+/-0.303) for {'C': 5365373.99519851, 'kernel': 'rbf', 'gamma': 3.922834625395207e-06}
0.696 (+/-0.304) for {'C': 5365373.99519851, 'kernel': 'rbf', 'gamma': 3.937313537008499e-06}
0.696 (+/-0.303) for {'C': 5365373.99519851, 'kernel': 'rbf', 'gamma': 3.951845889284363e-06}
0.679 (+/-0.290) for {'C': 5365373.99519851, 'kernel': 'rbf', 'gamma': 3.966431879468585e-06}
0.696 (+/-0.304) for {'C': 5365373.99519851, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.695 (+/-0.301) for {'C': 5365373.99519851, 'kernel': 'rbf', 'gamma': 3.995765566188054e-06}
0.679 (+/-0.292) for {'C': 5365373.99519851, 'kernel': 'rbf', 'gamma': 4.01051366086576e-06}
0.696 (+/-0.305) for {'C': 5365373.99519851, 'kernel': 'rbf', 'gamma': 4.025316189742124e-06}
0.680 (+/-0.293) for {'C': 5365373.99519851, 'kernel': 'rbf', 'gamma': 4.0401733537300044e-06}
0.663 (+/-0.315) for {'C': 5365373.99519851, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.696 (+/-0.302) for {'C': 5385177.219975264, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.696 (+/-0.303) for {'C': 5385177.219975264, 'kernel': 'rbf', 'gamma': 3.922834625395207e-06}
0.696 (+/-0.304) for {'C': 5385177.219975264, 'kernel': 'rbf', 'gamma': 3.937313537008499e-06}
0.696 (+/-0.303) for {'C': 5385177.219975264, 'kernel': 'rbf', 'gamma': 3.951845889284363e-06}
0.679 (+/-0.290) for {'C': 5385177.219975264, 'kernel': 'rbf', 'gamma': 3.966431879468585e-06}
0.696 (+/-0.304) for {'C': 5385177.219975264, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.695 (+/-0.301) for {'C': 5385177.219975264, 'kernel': 'rbf', 'gamma': 3.995765566188054e-06}
0.679 (+/-0.292) for {'C': 5385177.219975264, 'kernel': 'rbf', 'gamma': 4.01051366086576e-06}
0.696 (+/-0.305) for {'C': 5385177.219975264, 'kernel': 'rbf', 'gamma': 4.025316189742124e-06}
0.680 (+/-0.293) for {'C': 5385177.219975264, 'kernel': 'rbf', 'gamma': 4.0401733537300044e-06}
0.663 (+/-0.315) for {'C': 5385177.219975264, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.696 (+/-0.302) for {'C': 5405053.537086678, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.696 (+/-0.303) for {'C': 5405053.537086678, 'kernel': 'rbf', 'gamma': 3.922834625395207e-06}
0.696 (+/-0.304) for {'C': 5405053.537086678, 'kernel': 'rbf', 'gamma': 3.937313537008499e-06}
0.696 (+/-0.303) for {'C': 5405053.537086678, 'kernel': 'rbf', 'gamma': 3.951845889284363e-06}
0.679 (+/-0.290) for {'C': 5405053.537086678, 'kernel': 'rbf', 'gamma': 3.966431879468585e-06}
0.696 (+/-0.304) for {'C': 5405053.537086678, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.695 (+/-0.301) for {'C': 5405053.537086678, 'kernel': 'rbf', 'gamma': 3.995765566188054e-06}
0.679 (+/-0.292) for {'C': 5405053.537086678, 'kernel': 'rbf', 'gamma': 4.01051366086576e-06}
0.696 (+/-0.305) for {'C': 5405053.537086678, 'kernel': 'rbf', 'gamma': 4.025316189742124e-06}
0.680 (+/-0.293) for {'C': 5405053.537086678, 'kernel': 'rbf', 'gamma': 4.0401733537300044e-06}
0.663 (+/-0.315) for {'C': 5405053.537086678, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.696 (+/-0.302) for {'C': 5425003.216311495, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.696 (+/-0.303) for {'C': 5425003.216311495, 'kernel': 'rbf', 'gamma': 3.922834625395207e-06}
0.696 (+/-0.304) for {'C': 5425003.216311495, 'kernel': 'rbf', 'gamma': 3.937313537008499e-06}
0.696 (+/-0.303) for {'C': 5425003.216311495, 'kernel': 'rbf', 'gamma': 3.951845889284363e-06}
0.679 (+/-0.290) for {'C': 5425003.216311495, 'kernel': 'rbf', 'gamma': 3.966431879468585e-06}
0.696 (+/-0.304) for {'C': 5425003.216311495, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.695 (+/-0.301) for {'C': 5425003.216311495, 'kernel': 'rbf', 'gamma': 3.995765566188054e-06}
0.679 (+/-0.292) for {'C': 5425003.216311495, 'kernel': 'rbf', 'gamma': 4.01051366086576e-06}
0.696 (+/-0.305) for {'C': 5425003.216311495, 'kernel': 'rbf', 'gamma': 4.025316189742124e-06}
0.680 (+/-0.293) for {'C': 5425003.216311495, 'kernel': 'rbf', 'gamma': 4.0401733537300044e-06}
0.663 (+/-0.315) for {'C': 5425003.216311495, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.696 (+/-0.302) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.696 (+/-0.303) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.922834625395207e-06}
0.696 (+/-0.304) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.937313537008499e-06}
0.696 (+/-0.303) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.951845889284363e-06}
0.679 (+/-0.290) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.966431879468585e-06}
0.696 (+/-0.304) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.695 (+/-0.301) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.995765566188054e-06}
0.679 (+/-0.292) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 4.01051366086576e-06}
0.696 (+/-0.305) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 4.025316189742124e-06}
0.680 (+/-0.294) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 4.0401733537300044e-06}
0.663 (+/-0.315) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.696 (+/-0.302) for {'C': 5465123.745198711, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.696 (+/-0.303) for {'C': 5465123.745198711, 'kernel': 'rbf', 'gamma': 3.922834625395207e-06}
0.696 (+/-0.304) for {'C': 5465123.745198711, 'kernel': 'rbf', 'gamma': 3.937313537008499e-06}
0.696 (+/-0.303) for {'C': 5465123.745198711, 'kernel': 'rbf', 'gamma': 3.951845889284363e-06}
0.695 (+/-0.303) for {'C': 5465123.745198711, 'kernel': 'rbf', 'gamma': 3.966431879468585e-06}
0.696 (+/-0.304) for {'C': 5465123.745198711, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.695 (+/-0.301) for {'C': 5465123.745198711, 'kernel': 'rbf', 'gamma': 3.995765566188054e-06}
0.679 (+/-0.292) for {'C': 5465123.745198711, 'kernel': 'rbf', 'gamma': 4.01051366086576e-06}
0.696 (+/-0.305) for {'C': 5465123.745198711, 'kernel': 'rbf', 'gamma': 4.025316189742124e-06}
0.680 (+/-0.294) for {'C': 5465123.745198711, 'kernel': 'rbf', 'gamma': 4.0401733537300044e-06}
0.663 (+/-0.315) for {'C': 5465123.745198711, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.696 (+/-0.303) for {'C': 5485295.139412026, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.696 (+/-0.303) for {'C': 5485295.139412026, 'kernel': 'rbf', 'gamma': 3.922834625395207e-06}
0.696 (+/-0.304) for {'C': 5485295.139412026, 'kernel': 'rbf', 'gamma': 3.937313537008499e-06}
0.696 (+/-0.303) for {'C': 5485295.139412026, 'kernel': 'rbf', 'gamma': 3.951845889284363e-06}
0.679 (+/-0.290) for {'C': 5485295.139412026, 'kernel': 'rbf', 'gamma': 3.966431879468585e-06}
0.696 (+/-0.304) for {'C': 5485295.139412026, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.695 (+/-0.302) for {'C': 5485295.139412026, 'kernel': 'rbf', 'gamma': 3.995765566188054e-06}
0.679 (+/-0.292) for {'C': 5485295.139412026, 'kernel': 'rbf', 'gamma': 4.01051366086576e-06}
0.696 (+/-0.305) for {'C': 5485295.139412026, 'kernel': 'rbf', 'gamma': 4.025316189742124e-06}
0.680 (+/-0.293) for {'C': 5485295.139412026, 'kernel': 'rbf', 'gamma': 4.0401733537300044e-06}
0.663 (+/-0.315) for {'C': 5485295.139412026, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.696 (+/-0.303) for {'C': 5505540.984847944, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.696 (+/-0.303) for {'C': 5505540.984847944, 'kernel': 'rbf', 'gamma': 3.922834625395207e-06}
0.696 (+/-0.303) for {'C': 5505540.984847944, 'kernel': 'rbf', 'gamma': 3.937313537008499e-06}
0.696 (+/-0.303) for {'C': 5505540.984847944, 'kernel': 'rbf', 'gamma': 3.951845889284363e-06}
0.679 (+/-0.290) for {'C': 5505540.984847944, 'kernel': 'rbf', 'gamma': 3.966431879468585e-06}
0.696 (+/-0.304) for {'C': 5505540.984847944, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.695 (+/-0.302) for {'C': 5505540.984847944, 'kernel': 'rbf', 'gamma': 3.995765566188054e-06}
0.679 (+/-0.292) for {'C': 5505540.984847944, 'kernel': 'rbf', 'gamma': 4.01051366086576e-06}
0.696 (+/-0.305) for {'C': 5505540.984847944, 'kernel': 'rbf', 'gamma': 4.025316189742124e-06}
0.680 (+/-0.294) for {'C': 5505540.984847944, 'kernel': 'rbf', 'gamma': 4.0401733537300044e-06}
0.663 (+/-0.315) for {'C': 5505540.984847944, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.696 (+/-0.302) for {'C': 5525861.556300772, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.696 (+/-0.303) for {'C': 5525861.556300772, 'kernel': 'rbf', 'gamma': 3.922834625395207e-06}
0.696 (+/-0.303) for {'C': 5525861.556300772, 'kernel': 'rbf', 'gamma': 3.937313537008499e-06}
0.696 (+/-0.303) for {'C': 5525861.556300772, 'kernel': 'rbf', 'gamma': 3.951845889284363e-06}
0.679 (+/-0.290) for {'C': 5525861.556300772, 'kernel': 'rbf', 'gamma': 3.966431879468585e-06}
0.696 (+/-0.304) for {'C': 5525861.556300772, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.695 (+/-0.302) for {'C': 5525861.556300772, 'kernel': 'rbf', 'gamma': 3.995765566188054e-06}
0.679 (+/-0.292) for {'C': 5525861.556300772, 'kernel': 'rbf', 'gamma': 4.01051366086576e-06}
0.696 (+/-0.305) for {'C': 5525861.556300772, 'kernel': 'rbf', 'gamma': 4.025316189742124e-06}
0.680 (+/-0.293) for {'C': 5525861.556300772, 'kernel': 'rbf', 'gamma': 4.0401733537300044e-06}
0.663 (+/-0.315) for {'C': 5525861.556300772, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.696 (+/-0.302) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.696 (+/-0.303) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.922834625395207e-06}
0.696 (+/-0.304) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.937313537008499e-06}
0.696 (+/-0.303) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.951845889284363e-06}
0.679 (+/-0.290) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.966431879468585e-06}
0.696 (+/-0.304) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.695 (+/-0.303) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.995765566188054e-06}
0.679 (+/-0.292) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 4.01051366086576e-06}
0.696 (+/-0.305) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 4.025316189742124e-06}
0.680 (+/-0.293) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 4.0401733537300044e-06}
0.663 (+/-0.315) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.699 (+/-0.307) for {'C': 1.0, 'kernel': 'linear'}
0.696 (+/-0.304) for {'C': 10.0, 'kernel': 'linear'}
0.663 (+/-0.314) for {'C': 100.0, 'kernel': 'linear'}
0.660 (+/-0.316) for {'C': 1000.0, 'kernel': 'linear'}
0.634 (+/-0.321) for {'C': 10000.0, 'kernel': 'linear'}
0.632 (+/-0.323) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.20      0.17      0.18         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.99      0.99       629

本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6985561464259213
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.598 (+/-0.165) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.595 (+/-0.167) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.922834625395207e-06}
0.610 (+/-0.151) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.937313537008499e-06}
0.609 (+/-0.179) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.951845889284363e-06}
0.605 (+/-0.180) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.966431879468585e-06}
0.594 (+/-0.159) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.597 (+/-0.158) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.995765566188054e-06}
0.606 (+/-0.146) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 4.01051366086576e-06}
0.616 (+/-0.167) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 4.025316189742124e-06}
0.602 (+/-0.183) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 4.0401733537300044e-06}
0.581 (+/-0.171) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.598 (+/-0.165) for {'C': 5365373.99519851, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.595 (+/-0.167) for {'C': 5365373.99519851, 'kernel': 'rbf', 'gamma': 3.922834625395207e-06}
0.608 (+/-0.148) for {'C': 5365373.99519851, 'kernel': 'rbf', 'gamma': 3.937313537008499e-06}
0.609 (+/-0.179) for {'C': 5365373.99519851, 'kernel': 'rbf', 'gamma': 3.951845889284363e-06}
0.598 (+/-0.175) for {'C': 5365373.99519851, 'kernel': 'rbf', 'gamma': 3.966431879468585e-06}
0.600 (+/-0.148) for {'C': 5365373.99519851, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.597 (+/-0.158) for {'C': 5365373.99519851, 'kernel': 'rbf', 'gamma': 3.995765566188054e-06}
0.600 (+/-0.140) for {'C': 5365373.99519851, 'kernel': 'rbf', 'gamma': 4.01051366086576e-06}
0.616 (+/-0.167) for {'C': 5365373.99519851, 'kernel': 'rbf', 'gamma': 4.025316189742124e-06}
0.602 (+/-0.183) for {'C': 5365373.99519851, 'kernel': 'rbf', 'gamma': 4.0401733537300044e-06}
0.581 (+/-0.171) for {'C': 5365373.99519851, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.598 (+/-0.165) for {'C': 5385177.219975264, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.595 (+/-0.167) for {'C': 5385177.219975264, 'kernel': 'rbf', 'gamma': 3.922834625395207e-06}
0.608 (+/-0.148) for {'C': 5385177.219975264, 'kernel': 'rbf', 'gamma': 3.937313537008499e-06}
0.609 (+/-0.179) for {'C': 5385177.219975264, 'kernel': 'rbf', 'gamma': 3.951845889284363e-06}
0.597 (+/-0.175) for {'C': 5385177.219975264, 'kernel': 'rbf', 'gamma': 3.966431879468585e-06}
0.600 (+/-0.148) for {'C': 5385177.219975264, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.597 (+/-0.158) for {'C': 5385177.219975264, 'kernel': 'rbf', 'gamma': 3.995765566188054e-06}
0.600 (+/-0.140) for {'C': 5385177.219975264, 'kernel': 'rbf', 'gamma': 4.01051366086576e-06}
0.616 (+/-0.167) for {'C': 5385177.219975264, 'kernel': 'rbf', 'gamma': 4.025316189742124e-06}
0.602 (+/-0.183) for {'C': 5385177.219975264, 'kernel': 'rbf', 'gamma': 4.0401733537300044e-06}
0.581 (+/-0.171) for {'C': 5385177.219975264, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.598 (+/-0.165) for {'C': 5405053.537086678, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.595 (+/-0.167) for {'C': 5405053.537086678, 'kernel': 'rbf', 'gamma': 3.922834625395207e-06}
0.608 (+/-0.148) for {'C': 5405053.537086678, 'kernel': 'rbf', 'gamma': 3.937313537008499e-06}
0.609 (+/-0.179) for {'C': 5405053.537086678, 'kernel': 'rbf', 'gamma': 3.951845889284363e-06}
0.597 (+/-0.175) for {'C': 5405053.537086678, 'kernel': 'rbf', 'gamma': 3.966431879468585e-06}
0.600 (+/-0.148) for {'C': 5405053.537086678, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.597 (+/-0.158) for {'C': 5405053.537086678, 'kernel': 'rbf', 'gamma': 3.995765566188054e-06}
0.600 (+/-0.140) for {'C': 5405053.537086678, 'kernel': 'rbf', 'gamma': 4.01051366086576e-06}
0.616 (+/-0.167) for {'C': 5405053.537086678, 'kernel': 'rbf', 'gamma': 4.025316189742124e-06}
0.602 (+/-0.183) for {'C': 5405053.537086678, 'kernel': 'rbf', 'gamma': 4.0401733537300044e-06}
0.581 (+/-0.171) for {'C': 5405053.537086678, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.598 (+/-0.165) for {'C': 5425003.216311495, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.595 (+/-0.167) for {'C': 5425003.216311495, 'kernel': 'rbf', 'gamma': 3.922834625395207e-06}
0.608 (+/-0.148) for {'C': 5425003.216311495, 'kernel': 'rbf', 'gamma': 3.937313537008499e-06}
0.609 (+/-0.179) for {'C': 5425003.216311495, 'kernel': 'rbf', 'gamma': 3.951845889284363e-06}
0.598 (+/-0.175) for {'C': 5425003.216311495, 'kernel': 'rbf', 'gamma': 3.966431879468585e-06}
0.600 (+/-0.148) for {'C': 5425003.216311495, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.597 (+/-0.158) for {'C': 5425003.216311495, 'kernel': 'rbf', 'gamma': 3.995765566188054e-06}
0.600 (+/-0.140) for {'C': 5425003.216311495, 'kernel': 'rbf', 'gamma': 4.01051366086576e-06}
0.616 (+/-0.167) for {'C': 5425003.216311495, 'kernel': 'rbf', 'gamma': 4.025316189742124e-06}
0.602 (+/-0.183) for {'C': 5425003.216311495, 'kernel': 'rbf', 'gamma': 4.0401733537300044e-06}
0.581 (+/-0.171) for {'C': 5425003.216311495, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.598 (+/-0.165) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.595 (+/-0.167) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.922834625395207e-06}
0.610 (+/-0.151) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.937313537008499e-06}
0.609 (+/-0.179) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.951845889284363e-06}
0.597 (+/-0.175) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.966431879468585e-06}
0.600 (+/-0.148) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.597 (+/-0.158) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.995765566188054e-06}
0.599 (+/-0.141) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 4.01051366086576e-06}
0.616 (+/-0.167) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 4.025316189742124e-06}
0.602 (+/-0.183) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 4.0401733537300044e-06}
0.581 (+/-0.171) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.598 (+/-0.165) for {'C': 5465123.745198711, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.595 (+/-0.167) for {'C': 5465123.745198711, 'kernel': 'rbf', 'gamma': 3.922834625395207e-06}
0.610 (+/-0.151) for {'C': 5465123.745198711, 'kernel': 'rbf', 'gamma': 3.937313537008499e-06}
0.609 (+/-0.179) for {'C': 5465123.745198711, 'kernel': 'rbf', 'gamma': 3.951845889284363e-06}
0.604 (+/-0.180) for {'C': 5465123.745198711, 'kernel': 'rbf', 'gamma': 3.966431879468585e-06}
0.600 (+/-0.148) for {'C': 5465123.745198711, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.597 (+/-0.158) for {'C': 5465123.745198711, 'kernel': 'rbf', 'gamma': 3.995765566188054e-06}
0.599 (+/-0.141) for {'C': 5465123.745198711, 'kernel': 'rbf', 'gamma': 4.01051366086576e-06}
0.616 (+/-0.167) for {'C': 5465123.745198711, 'kernel': 'rbf', 'gamma': 4.025316189742124e-06}
0.602 (+/-0.183) for {'C': 5465123.745198711, 'kernel': 'rbf', 'gamma': 4.0401733537300044e-06}
0.581 (+/-0.171) for {'C': 5465123.745198711, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.598 (+/-0.165) for {'C': 5485295.139412026, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.595 (+/-0.167) for {'C': 5485295.139412026, 'kernel': 'rbf', 'gamma': 3.922834625395207e-06}
0.608 (+/-0.148) for {'C': 5485295.139412026, 'kernel': 'rbf', 'gamma': 3.937313537008499e-06}
0.609 (+/-0.179) for {'C': 5485295.139412026, 'kernel': 'rbf', 'gamma': 3.951845889284363e-06}
0.597 (+/-0.175) for {'C': 5485295.139412026, 'kernel': 'rbf', 'gamma': 3.966431879468585e-06}
0.600 (+/-0.148) for {'C': 5485295.139412026, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.597 (+/-0.158) for {'C': 5485295.139412026, 'kernel': 'rbf', 'gamma': 3.995765566188054e-06}
0.599 (+/-0.141) for {'C': 5485295.139412026, 'kernel': 'rbf', 'gamma': 4.01051366086576e-06}
0.616 (+/-0.167) for {'C': 5485295.139412026, 'kernel': 'rbf', 'gamma': 4.025316189742124e-06}
0.602 (+/-0.183) for {'C': 5485295.139412026, 'kernel': 'rbf', 'gamma': 4.0401733537300044e-06}
0.581 (+/-0.171) for {'C': 5485295.139412026, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.598 (+/-0.165) for {'C': 5505540.984847944, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.594 (+/-0.168) for {'C': 5505540.984847944, 'kernel': 'rbf', 'gamma': 3.922834625395207e-06}
0.608 (+/-0.148) for {'C': 5505540.984847944, 'kernel': 'rbf', 'gamma': 3.937313537008499e-06}
0.609 (+/-0.179) for {'C': 5505540.984847944, 'kernel': 'rbf', 'gamma': 3.951845889284363e-06}
0.597 (+/-0.175) for {'C': 5505540.984847944, 'kernel': 'rbf', 'gamma': 3.966431879468585e-06}
0.600 (+/-0.148) for {'C': 5505540.984847944, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.597 (+/-0.158) for {'C': 5505540.984847944, 'kernel': 'rbf', 'gamma': 3.995765566188054e-06}
0.599 (+/-0.141) for {'C': 5505540.984847944, 'kernel': 'rbf', 'gamma': 4.01051366086576e-06}
0.616 (+/-0.167) for {'C': 5505540.984847944, 'kernel': 'rbf', 'gamma': 4.025316189742124e-06}
0.602 (+/-0.183) for {'C': 5505540.984847944, 'kernel': 'rbf', 'gamma': 4.0401733537300044e-06}
0.581 (+/-0.171) for {'C': 5505540.984847944, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.598 (+/-0.165) for {'C': 5525861.556300772, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.595 (+/-0.167) for {'C': 5525861.556300772, 'kernel': 'rbf', 'gamma': 3.922834625395207e-06}
0.608 (+/-0.148) for {'C': 5525861.556300772, 'kernel': 'rbf', 'gamma': 3.937313537008499e-06}
0.609 (+/-0.179) for {'C': 5525861.556300772, 'kernel': 'rbf', 'gamma': 3.951845889284363e-06}
0.597 (+/-0.175) for {'C': 5525861.556300772, 'kernel': 'rbf', 'gamma': 3.966431879468585e-06}
0.599 (+/-0.149) for {'C': 5525861.556300772, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.597 (+/-0.158) for {'C': 5525861.556300772, 'kernel': 'rbf', 'gamma': 3.995765566188054e-06}
0.599 (+/-0.141) for {'C': 5525861.556300772, 'kernel': 'rbf', 'gamma': 4.01051366086576e-06}
0.616 (+/-0.167) for {'C': 5525861.556300772, 'kernel': 'rbf', 'gamma': 4.025316189742124e-06}
0.602 (+/-0.183) for {'C': 5525861.556300772, 'kernel': 'rbf', 'gamma': 4.0401733537300044e-06}
0.581 (+/-0.171) for {'C': 5525861.556300772, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.598 (+/-0.165) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.594 (+/-0.168) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.922834625395207e-06}
0.608 (+/-0.148) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.937313537008499e-06}
0.609 (+/-0.179) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.951845889284363e-06}
0.603 (+/-0.165) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.966431879468585e-06}
0.599 (+/-0.149) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.599 (+/-0.158) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.995765566188054e-06}
0.599 (+/-0.141) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 4.01051366086576e-06}
0.616 (+/-0.167) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 4.025316189742124e-06}
0.602 (+/-0.183) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 4.0401733537300044e-06}
0.581 (+/-0.171) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.798 (+/-0.437) for {'C': 0.1, 'kernel': 'linear'}
0.635 (+/-0.198) for {'C': 1.0, 'kernel': 'linear'}
0.586 (+/-0.152) for {'C': 10.0, 'kernel': 'linear'}
0.587 (+/-0.195) for {'C': 100.0, 'kernel': 'linear'}
0.589 (+/-0.193) for {'C': 1000.0, 'kernel': 'linear'}
0.582 (+/-0.201) for {'C': 10000.0, 'kernel': 'linear'}
0.582 (+/-0.201) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.718 (+/-0.349) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.693 (+/-0.303) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.922834625395207e-06}
0.743 (+/-0.318) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.937313537008499e-06}
0.718 (+/-0.353) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.951845889284363e-06}
0.718 (+/-0.352) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.966431879468585e-06}
0.717 (+/-0.350) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.719 (+/-0.353) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.995765566188054e-06}
0.744 (+/-0.319) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 4.01051366086576e-06}
0.744 (+/-0.318) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 4.025316189742124e-06}
0.676 (+/-0.291) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 4.0401733537300044e-06}
0.660 (+/-0.310) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.718 (+/-0.349) for {'C': 5365373.99519851, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.693 (+/-0.303) for {'C': 5365373.99519851, 'kernel': 'rbf', 'gamma': 3.922834625395207e-06}
0.743 (+/-0.317) for {'C': 5365373.99519851, 'kernel': 'rbf', 'gamma': 3.937313537008499e-06}
0.718 (+/-0.353) for {'C': 5365373.99519851, 'kernel': 'rbf', 'gamma': 3.951845889284363e-06}
0.702 (+/-0.345) for {'C': 5365373.99519851, 'kernel': 'rbf', 'gamma': 3.966431879468585e-06}
0.742 (+/-0.315) for {'C': 5365373.99519851, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.719 (+/-0.353) for {'C': 5365373.99519851, 'kernel': 'rbf', 'gamma': 3.995765566188054e-06}
0.727 (+/-0.317) for {'C': 5365373.99519851, 'kernel': 'rbf', 'gamma': 4.01051366086576e-06}
0.744 (+/-0.318) for {'C': 5365373.99519851, 'kernel': 'rbf', 'gamma': 4.025316189742124e-06}
0.676 (+/-0.291) for {'C': 5365373.99519851, 'kernel': 'rbf', 'gamma': 4.0401733537300044e-06}
0.660 (+/-0.310) for {'C': 5365373.99519851, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.718 (+/-0.349) for {'C': 5385177.219975264, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.693 (+/-0.303) for {'C': 5385177.219975264, 'kernel': 'rbf', 'gamma': 3.922834625395207e-06}
0.743 (+/-0.317) for {'C': 5385177.219975264, 'kernel': 'rbf', 'gamma': 3.937313537008499e-06}
0.718 (+/-0.353) for {'C': 5385177.219975264, 'kernel': 'rbf', 'gamma': 3.951845889284363e-06}
0.701 (+/-0.345) for {'C': 5385177.219975264, 'kernel': 'rbf', 'gamma': 3.966431879468585e-06}
0.742 (+/-0.315) for {'C': 5385177.219975264, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.719 (+/-0.353) for {'C': 5385177.219975264, 'kernel': 'rbf', 'gamma': 3.995765566188054e-06}
0.727 (+/-0.317) for {'C': 5385177.219975264, 'kernel': 'rbf', 'gamma': 4.01051366086576e-06}
0.744 (+/-0.318) for {'C': 5385177.219975264, 'kernel': 'rbf', 'gamma': 4.025316189742124e-06}
0.676 (+/-0.291) for {'C': 5385177.219975264, 'kernel': 'rbf', 'gamma': 4.0401733537300044e-06}
0.660 (+/-0.310) for {'C': 5385177.219975264, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.718 (+/-0.349) for {'C': 5405053.537086678, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.693 (+/-0.303) for {'C': 5405053.537086678, 'kernel': 'rbf', 'gamma': 3.922834625395207e-06}
0.743 (+/-0.317) for {'C': 5405053.537086678, 'kernel': 'rbf', 'gamma': 3.937313537008499e-06}
0.718 (+/-0.353) for {'C': 5405053.537086678, 'kernel': 'rbf', 'gamma': 3.951845889284363e-06}
0.701 (+/-0.345) for {'C': 5405053.537086678, 'kernel': 'rbf', 'gamma': 3.966431879468585e-06}
0.742 (+/-0.315) for {'C': 5405053.537086678, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.719 (+/-0.353) for {'C': 5405053.537086678, 'kernel': 'rbf', 'gamma': 3.995765566188054e-06}
0.727 (+/-0.317) for {'C': 5405053.537086678, 'kernel': 'rbf', 'gamma': 4.01051366086576e-06}
0.744 (+/-0.318) for {'C': 5405053.537086678, 'kernel': 'rbf', 'gamma': 4.025316189742124e-06}
0.676 (+/-0.291) for {'C': 5405053.537086678, 'kernel': 'rbf', 'gamma': 4.0401733537300044e-06}
0.660 (+/-0.310) for {'C': 5405053.537086678, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.718 (+/-0.349) for {'C': 5425003.216311495, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.693 (+/-0.303) for {'C': 5425003.216311495, 'kernel': 'rbf', 'gamma': 3.922834625395207e-06}
0.743 (+/-0.317) for {'C': 5425003.216311495, 'kernel': 'rbf', 'gamma': 3.937313537008499e-06}
0.718 (+/-0.353) for {'C': 5425003.216311495, 'kernel': 'rbf', 'gamma': 3.951845889284363e-06}
0.702 (+/-0.345) for {'C': 5425003.216311495, 'kernel': 'rbf', 'gamma': 3.966431879468585e-06}
0.742 (+/-0.315) for {'C': 5425003.216311495, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.719 (+/-0.353) for {'C': 5425003.216311495, 'kernel': 'rbf', 'gamma': 3.995765566188054e-06}
0.727 (+/-0.317) for {'C': 5425003.216311495, 'kernel': 'rbf', 'gamma': 4.01051366086576e-06}
0.744 (+/-0.318) for {'C': 5425003.216311495, 'kernel': 'rbf', 'gamma': 4.025316189742124e-06}
0.676 (+/-0.291) for {'C': 5425003.216311495, 'kernel': 'rbf', 'gamma': 4.0401733537300044e-06}
0.660 (+/-0.310) for {'C': 5425003.216311495, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.718 (+/-0.349) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.693 (+/-0.303) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.922834625395207e-06}
0.743 (+/-0.318) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.937313537008499e-06}
0.718 (+/-0.353) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.951845889284363e-06}
0.701 (+/-0.345) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.966431879468585e-06}
0.742 (+/-0.315) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.718 (+/-0.353) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.995765566188054e-06}
0.727 (+/-0.317) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 4.01051366086576e-06}
0.744 (+/-0.318) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 4.025316189742124e-06}
0.676 (+/-0.291) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 4.0401733537300044e-06}
0.660 (+/-0.310) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.718 (+/-0.349) for {'C': 5465123.745198711, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.693 (+/-0.303) for {'C': 5465123.745198711, 'kernel': 'rbf', 'gamma': 3.922834625395207e-06}
0.743 (+/-0.318) for {'C': 5465123.745198711, 'kernel': 'rbf', 'gamma': 3.937313537008499e-06}
0.718 (+/-0.353) for {'C': 5465123.745198711, 'kernel': 'rbf', 'gamma': 3.951845889284363e-06}
0.718 (+/-0.351) for {'C': 5465123.745198711, 'kernel': 'rbf', 'gamma': 3.966431879468585e-06}
0.742 (+/-0.315) for {'C': 5465123.745198711, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.718 (+/-0.353) for {'C': 5465123.745198711, 'kernel': 'rbf', 'gamma': 3.995765566188054e-06}
0.727 (+/-0.317) for {'C': 5465123.745198711, 'kernel': 'rbf', 'gamma': 4.01051366086576e-06}
0.744 (+/-0.318) for {'C': 5465123.745198711, 'kernel': 'rbf', 'gamma': 4.025316189742124e-06}
0.676 (+/-0.291) for {'C': 5465123.745198711, 'kernel': 'rbf', 'gamma': 4.0401733537300044e-06}
0.660 (+/-0.310) for {'C': 5465123.745198711, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.718 (+/-0.349) for {'C': 5485295.139412026, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.693 (+/-0.303) for {'C': 5485295.139412026, 'kernel': 'rbf', 'gamma': 3.922834625395207e-06}
0.743 (+/-0.317) for {'C': 5485295.139412026, 'kernel': 'rbf', 'gamma': 3.937313537008499e-06}
0.718 (+/-0.353) for {'C': 5485295.139412026, 'kernel': 'rbf', 'gamma': 3.951845889284363e-06}
0.701 (+/-0.344) for {'C': 5485295.139412026, 'kernel': 'rbf', 'gamma': 3.966431879468585e-06}
0.742 (+/-0.315) for {'C': 5485295.139412026, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.718 (+/-0.353) for {'C': 5485295.139412026, 'kernel': 'rbf', 'gamma': 3.995765566188054e-06}
0.727 (+/-0.317) for {'C': 5485295.139412026, 'kernel': 'rbf', 'gamma': 4.01051366086576e-06}
0.744 (+/-0.318) for {'C': 5485295.139412026, 'kernel': 'rbf', 'gamma': 4.025316189742124e-06}
0.676 (+/-0.291) for {'C': 5485295.139412026, 'kernel': 'rbf', 'gamma': 4.0401733537300044e-06}
0.660 (+/-0.310) for {'C': 5485295.139412026, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.718 (+/-0.349) for {'C': 5505540.984847944, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.692 (+/-0.301) for {'C': 5505540.984847944, 'kernel': 'rbf', 'gamma': 3.922834625395207e-06}
0.743 (+/-0.317) for {'C': 5505540.984847944, 'kernel': 'rbf', 'gamma': 3.937313537008499e-06}
0.718 (+/-0.353) for {'C': 5505540.984847944, 'kernel': 'rbf', 'gamma': 3.951845889284363e-06}
0.701 (+/-0.344) for {'C': 5505540.984847944, 'kernel': 'rbf', 'gamma': 3.966431879468585e-06}
0.742 (+/-0.315) for {'C': 5505540.984847944, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.718 (+/-0.353) for {'C': 5505540.984847944, 'kernel': 'rbf', 'gamma': 3.995765566188054e-06}
0.727 (+/-0.317) for {'C': 5505540.984847944, 'kernel': 'rbf', 'gamma': 4.01051366086576e-06}
0.744 (+/-0.318) for {'C': 5505540.984847944, 'kernel': 'rbf', 'gamma': 4.025316189742124e-06}
0.676 (+/-0.291) for {'C': 5505540.984847944, 'kernel': 'rbf', 'gamma': 4.0401733537300044e-06}
0.660 (+/-0.310) for {'C': 5505540.984847944, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.718 (+/-0.349) for {'C': 5525861.556300772, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.693 (+/-0.303) for {'C': 5525861.556300772, 'kernel': 'rbf', 'gamma': 3.922834625395207e-06}
0.743 (+/-0.317) for {'C': 5525861.556300772, 'kernel': 'rbf', 'gamma': 3.937313537008499e-06}
0.718 (+/-0.353) for {'C': 5525861.556300772, 'kernel': 'rbf', 'gamma': 3.951845889284363e-06}
0.701 (+/-0.344) for {'C': 5525861.556300772, 'kernel': 'rbf', 'gamma': 3.966431879468585e-06}
0.742 (+/-0.315) for {'C': 5525861.556300772, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.718 (+/-0.353) for {'C': 5525861.556300772, 'kernel': 'rbf', 'gamma': 3.995765566188054e-06}
0.727 (+/-0.317) for {'C': 5525861.556300772, 'kernel': 'rbf', 'gamma': 4.01051366086576e-06}
0.744 (+/-0.318) for {'C': 5525861.556300772, 'kernel': 'rbf', 'gamma': 4.025316189742124e-06}
0.676 (+/-0.291) for {'C': 5525861.556300772, 'kernel': 'rbf', 'gamma': 4.0401733537300044e-06}
0.660 (+/-0.310) for {'C': 5525861.556300772, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.718 (+/-0.349) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.692 (+/-0.302) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.922834625395207e-06}
0.743 (+/-0.317) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.937313537008499e-06}
0.718 (+/-0.353) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.951845889284363e-06}
0.726 (+/-0.314) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.966431879468585e-06}
0.742 (+/-0.315) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.718 (+/-0.353) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.995765566188054e-06}
0.727 (+/-0.317) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 4.01051366086576e-06}
0.744 (+/-0.318) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 4.025316189742124e-06}
0.676 (+/-0.291) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 4.0401733537300044e-06}
0.660 (+/-0.310) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.700 (+/-0.309) for {'C': 0.1, 'kernel': 'linear'}
0.698 (+/-0.305) for {'C': 1.0, 'kernel': 'linear'}
0.694 (+/-0.301) for {'C': 10.0, 'kernel': 'linear'}
0.657 (+/-0.313) for {'C': 100.0, 'kernel': 'linear'}
0.659 (+/-0.314) for {'C': 1000.0, 'kernel': 'linear'}
0.633 (+/-0.321) for {'C': 10000.0, 'kernel': 'linear'}
0.632 (+/-0.323) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.06      0.17      0.08         6
          1       0.99      0.97      0.98       623

avg / total       0.98      0.97      0.97       629

本轮grid search结果，得到最好的参数选择是： {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 4.01051366086576e-06}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.7437474235303818
发现最优参数C为原先的最大/最小值，直接重新设置超参。
第2轮loop中，tunemodel函数得到的最优参数是： ([{'C': array([5.34564359e+01, 5.34564359e+02, 5.34564359e+03, 5.34564359e+04,
       5.34564359e+05, 5.34564359e+06, 5.34564359e+07, 5.34564359e+08,
       5.34564359e+09, 5.34564359e+10, 5.34564359e+11]), 'kernel': ['rbf'], 'gamma': array([3.99576557e-06, 3.99871084e-06, 4.00165828e-06, 4.00460790e-06,
       4.00755969e-06, 4.01051366e-06, 4.01346981e-06, 4.01642813e-06,
       4.01938863e-06, 4.02235132e-06, 4.02531619e-06])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}], {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 4.01051366086576e-06}, 0.7437474235303818)
这是第2次迭代微调C和gamma。
第2次迭代，得到delta： [9.93829345e+04 2.94419553e-08]
SVC模型clf_op_iter的参数是： {'kernel': 'rbf', 'decision_function_shape': 'ovr', 'class_weight': {-1: 15}, 'tol': 0.001, 'gamma': 4.01051366086576e-06, 'random_state': 0, 'cache_size': 200, 'verbose': False, 'degree': 3, 'C': 5345643.593969704, 'probability': False, 'shrinking': True, 'coef0': 0.0, 'max_iter': -1}

训练模型SVC对训练样本的预测准确率： 0.9518072289156626

测试集中，预测为舞弊样本的有： (array([   0,    1,    2, ..., 1254, 1255, 1256], dtype=int64),) 
测试集中，实际为舞弊样本的有： (array([1246, 1247, 1248, 1249, 1250, 1251, 1252, 1253, 1254, 1255, 1256],
      dtype=int64),) 
预测的舞弊样本数目： 1076

训练模型SVC对测试样本的预测准确率： 0.23387668320340185
以上是第40次特征筛选。
第40次特征筛选，AUC值是： 0.5267765941923246
X_train_iter_svc.shape is: (1257, 12)
X_test_iter_svc.shape is: (1257, 12)
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.722 (+/-0.473) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.609 (+/-0.309) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.660 (+/-0.219) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.612 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.698 (+/-0.306) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.17      0.17      0.17         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6982356336054085
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.660 (+/-0.219) for {'C': 1.0, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.585 (+/-0.188) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.14      0.17      0.15         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.698 (+/-0.306) for {'C': 1.0, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.659 (+/-0.313) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.14      0.17      0.15         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6982356336054085
RBF的粗grid search最佳超参： {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001} RBF的粗grid search最高分值： 0.6982356336054085
linear的粗grid search最佳超参： {'C': 1.0, 'kernel': 'linear'} linear的粗grid search最高分值： 0.6982356336054085
粗grid search得到的parameter是：
 [{'C': array([1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03, 1.e+04, 1.e+05,
       1.e+06, 1.e+07, 1.e+08]), 'kernel': ['rbf'], 'gamma': array([1.e-08, 1.e-07, 1.e-06, 1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01,
       1.e+00, 1.e+01, 1.e+02])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.848 (+/-0.459) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.848 (+/-0.459) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.685 (+/-0.297) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.308) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.848 (+/-0.459) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.660 (+/-0.219) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.644 (+/-0.285) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.608 (+/-0.309) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.848 (+/-0.459) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.660 (+/-0.219) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.611 (+/-0.184) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.597 (+/-0.315) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.609 (+/-0.309) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.848 (+/-0.459) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.660 (+/-0.219) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.608 (+/-0.185) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.593 (+/-0.188) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.604 (+/-0.309) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.609 (+/-0.309) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.848 (+/-0.459) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.660 (+/-0.219) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.611 (+/-0.185) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.599 (+/-0.192) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.572 (+/-0.201) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.606 (+/-0.308) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.609 (+/-0.309) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.748 (+/-0.396) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.651 (+/-0.211) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.610 (+/-0.183) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.591 (+/-0.189) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.587 (+/-0.189) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.576 (+/-0.196) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.606 (+/-0.308) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.609 (+/-0.309) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.848 (+/-0.460) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.703 (+/-0.418) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.622 (+/-0.291) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.595 (+/-0.191) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.581 (+/-0.187) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.578 (+/-0.195) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.578 (+/-0.195) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.606 (+/-0.308) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.609 (+/-0.309) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.793 (+/-0.440) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.690 (+/-0.435) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.614 (+/-0.292) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.592 (+/-0.192) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.580 (+/-0.188) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.578 (+/-0.196) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.578 (+/-0.195) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.606 (+/-0.308) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.609 (+/-0.309) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.685 (+/-0.297) for {'C': 1.0, 'kernel': 'linear'}
0.607 (+/-0.182) for {'C': 10.0, 'kernel': 'linear'}
0.608 (+/-0.197) for {'C': 100.0, 'kernel': 'linear'}
0.589 (+/-0.189) for {'C': 1000.0, 'kernel': 'linear'}
0.585 (+/-0.189) for {'C': 10000.0, 'kernel': 'linear'}
0.580 (+/-0.195) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      1.00       623

avg / total       0.98      0.99      0.99       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.658 (+/-0.217) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.004) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.658 (+/-0.217) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.698 (+/-0.306) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.007) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.658 (+/-0.217) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.306) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.304) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.611 (+/-0.240) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.006) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.658 (+/-0.217) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.698 (+/-0.306) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.696 (+/-0.304) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.587 (+/-0.231) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.612 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.006) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.658 (+/-0.217) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.698 (+/-0.306) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.696 (+/-0.304) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.662 (+/-0.315) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.611 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.006) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.658 (+/-0.217) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.698 (+/-0.306) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.696 (+/-0.304) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.662 (+/-0.316) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.585 (+/-0.235) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.006) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.699 (+/-0.308) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.698 (+/-0.306) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.696 (+/-0.302) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.661 (+/-0.315) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.660 (+/-0.314) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.610 (+/-0.239) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.239) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.237) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.006) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.683 (+/-0.296) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.683 (+/-0.258) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.695 (+/-0.303) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.662 (+/-0.311) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.658 (+/-0.311) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.632 (+/-0.323) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.611 (+/-0.239) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.239) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.237) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.006) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.699 (+/-0.307) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.633 (+/-0.237) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.694 (+/-0.301) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.660 (+/-0.312) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.657 (+/-0.310) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.631 (+/-0.324) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.611 (+/-0.239) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.239) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.237) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.006) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 1.0, 'kernel': 'linear'}
0.696 (+/-0.303) for {'C': 10.0, 'kernel': 'linear'}
0.663 (+/-0.314) for {'C': 100.0, 'kernel': 'linear'}
0.660 (+/-0.314) for {'C': 1000.0, 'kernel': 'linear'}
0.659 (+/-0.312) for {'C': 10000.0, 'kernel': 'linear'}
0.634 (+/-0.325) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6991982026547944
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.669 (+/-0.296) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.667 (+/-0.292) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.656 (+/-0.384) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.642 (+/-0.204) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.633 (+/-0.197) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.593 (+/-0.296) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.642 (+/-0.204) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.624 (+/-0.186) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.618 (+/-0.278) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.608 (+/-0.309) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.642 (+/-0.204) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.624 (+/-0.186) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.585 (+/-0.153) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.601 (+/-0.311) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.609 (+/-0.309) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.642 (+/-0.204) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.624 (+/-0.186) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.582 (+/-0.153) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.580 (+/-0.188) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.604 (+/-0.309) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.609 (+/-0.309) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.747 (+/-0.502) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.642 (+/-0.204) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.631 (+/-0.197) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.585 (+/-0.155) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.581 (+/-0.191) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.576 (+/-0.197) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.606 (+/-0.308) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.609 (+/-0.309) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.647 (+/-0.460) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.656 (+/-0.231) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.606 (+/-0.180) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.590 (+/-0.158) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.578 (+/-0.189) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.585 (+/-0.189) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.576 (+/-0.196) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.606 (+/-0.308) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.609 (+/-0.309) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.848 (+/-0.460) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.542 (+/-0.143) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.599 (+/-0.282) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.591 (+/-0.188) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.580 (+/-0.188) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.578 (+/-0.195) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.578 (+/-0.195) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.606 (+/-0.308) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.609 (+/-0.309) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.793 (+/-0.440) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.553 (+/-0.296) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.608 (+/-0.286) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.591 (+/-0.188) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.580 (+/-0.188) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.578 (+/-0.196) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.578 (+/-0.195) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.606 (+/-0.308) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.609 (+/-0.309) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.798 (+/-0.437) for {'C': 0.1, 'kernel': 'linear'}
0.660 (+/-0.219) for {'C': 1.0, 'kernel': 'linear'}
0.584 (+/-0.147) for {'C': 10.0, 'kernel': 'linear'}
0.587 (+/-0.195) for {'C': 100.0, 'kernel': 'linear'}
0.585 (+/-0.188) for {'C': 1000.0, 'kernel': 'linear'}
0.585 (+/-0.189) for {'C': 10000.0, 'kernel': 'linear'}
0.580 (+/-0.195) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.682 (+/-0.294) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.698 (+/-0.306) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.494 (+/-0.012) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.306) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.305) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.611 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.007) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.698 (+/-0.306) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.304) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.694 (+/-0.305) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.611 (+/-0.240) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.006) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.698 (+/-0.306) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.304) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.693 (+/-0.304) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.608 (+/-0.241) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.612 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.006) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.698 (+/-0.306) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.304) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.692 (+/-0.304) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.657 (+/-0.312) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.611 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.006) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.617 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.698 (+/-0.306) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.697 (+/-0.304) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.693 (+/-0.304) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.657 (+/-0.310) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.608 (+/-0.242) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.006) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.575 (+/-0.229) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.698 (+/-0.308) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.696 (+/-0.300) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.693 (+/-0.304) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.656 (+/-0.310) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.659 (+/-0.313) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.610 (+/-0.239) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.239) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.237) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.006) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.700 (+/-0.309) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.660 (+/-0.256) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.693 (+/-0.301) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.705 (+/-0.341) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.657 (+/-0.310) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.632 (+/-0.323) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.611 (+/-0.239) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.239) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.237) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.006) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.699 (+/-0.307) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.607 (+/-0.162) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.694 (+/-0.303) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.705 (+/-0.340) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.657 (+/-0.310) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.631 (+/-0.324) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.611 (+/-0.239) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.239) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.237) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.006) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.700 (+/-0.309) for {'C': 0.1, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 1.0, 'kernel': 'linear'}
0.694 (+/-0.301) for {'C': 10.0, 'kernel': 'linear'}
0.657 (+/-0.313) for {'C': 100.0, 'kernel': 'linear'}
0.659 (+/-0.313) for {'C': 1000.0, 'kernel': 'linear'}
0.659 (+/-0.313) for {'C': 10000.0, 'kernel': 'linear'}
0.634 (+/-0.325) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.07      0.33      0.12         6
          1       0.99      0.96      0.98       623

avg / total       0.98      0.95      0.97       629

本轮grid search结果，得到最好的参数选择是： {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.705286400362767
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.651 (+/-0.211) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.693 (+/-0.350) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.634 (+/-0.193) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.620 (+/-0.183) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.601 (+/-0.176) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.610 (+/-0.183) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.598 (+/-0.159) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.617 (+/-0.187) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.601 (+/-0.198) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.595 (+/-0.188) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.591 (+/-0.189) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.664 (+/-0.291) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.639 (+/-0.202) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.625 (+/-0.184) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.595 (+/-0.160) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.594 (+/-0.157) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.605 (+/-0.165) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.616 (+/-0.182) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.588 (+/-0.185) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.594 (+/-0.190) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.590 (+/-0.188) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.591 (+/-0.189) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.658 (+/-0.290) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.661 (+/-0.293) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.602 (+/-0.182) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.593 (+/-0.155) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.625 (+/-0.211) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.611 (+/-0.183) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.606 (+/-0.176) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.589 (+/-0.187) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.596 (+/-0.193) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.589 (+/-0.189) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.589 (+/-0.191) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.649 (+/-0.286) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.640 (+/-0.288) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.597 (+/-0.161) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.599 (+/-0.180) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.614 (+/-0.179) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.600 (+/-0.197) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.597 (+/-0.195) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.588 (+/-0.187) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.595 (+/-0.194) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.585 (+/-0.189) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.584 (+/-0.187) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.638 (+/-0.292) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.625 (+/-0.289) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.601 (+/-0.180) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.600 (+/-0.180) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.619 (+/-0.181) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.595 (+/-0.191) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.591 (+/-0.189) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.588 (+/-0.187) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.589 (+/-0.191) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.587 (+/-0.191) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.583 (+/-0.187) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.621 (+/-0.291) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.622 (+/-0.288) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.603 (+/-0.182) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.599 (+/-0.175) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.599 (+/-0.190) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.595 (+/-0.191) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.590 (+/-0.194) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.587 (+/-0.187) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.586 (+/-0.190) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.585 (+/-0.190) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.581 (+/-0.187) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.616 (+/-0.292) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.593 (+/-0.175) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.611 (+/-0.185) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.587 (+/-0.186) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.598 (+/-0.194) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.595 (+/-0.191) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.581 (+/-0.172) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.580 (+/-0.187) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.582 (+/-0.188) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.584 (+/-0.190) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.580 (+/-0.188) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.616 (+/-0.292) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.594 (+/-0.176) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.603 (+/-0.177) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.587 (+/-0.186) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.600 (+/-0.197) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.593 (+/-0.191) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.581 (+/-0.172) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.580 (+/-0.188) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.586 (+/-0.186) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.583 (+/-0.189) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.580 (+/-0.188) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.615 (+/-0.293) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.592 (+/-0.176) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.594 (+/-0.188) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.586 (+/-0.186) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.587 (+/-0.172) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.593 (+/-0.191) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.582 (+/-0.173) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.580 (+/-0.188) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.585 (+/-0.186) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.583 (+/-0.190) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.579 (+/-0.189) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.614 (+/-0.293) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.595 (+/-0.175) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.605 (+/-0.209) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.588 (+/-0.186) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.587 (+/-0.172) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.592 (+/-0.192) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.582 (+/-0.173) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.579 (+/-0.188) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.585 (+/-0.186) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.583 (+/-0.190) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.580 (+/-0.188) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.614 (+/-0.292) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.595 (+/-0.174) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.608 (+/-0.209) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.588 (+/-0.186) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.586 (+/-0.173) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.592 (+/-0.192) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.581 (+/-0.172) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.579 (+/-0.188) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.584 (+/-0.186) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.583 (+/-0.190) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.580 (+/-0.188) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.685 (+/-0.297) for {'C': 1.0, 'kernel': 'linear'}
0.607 (+/-0.182) for {'C': 10.0, 'kernel': 'linear'}
0.608 (+/-0.197) for {'C': 100.0, 'kernel': 'linear'}
0.589 (+/-0.189) for {'C': 1000.0, 'kernel': 'linear'}
0.585 (+/-0.189) for {'C': 10000.0, 'kernel': 'linear'}
0.580 (+/-0.195) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.698 (+/-0.306) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.698 (+/-0.306) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.698 (+/-0.306) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.697 (+/-0.304) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.695 (+/-0.302) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.696 (+/-0.302) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.696 (+/-0.303) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.696 (+/-0.304) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.663 (+/-0.315) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.662 (+/-0.314) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.661 (+/-0.315) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.698 (+/-0.306) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.698 (+/-0.306) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.697 (+/-0.304) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.695 (+/-0.302) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.695 (+/-0.303) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.696 (+/-0.300) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.680 (+/-0.290) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.662 (+/-0.311) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.662 (+/-0.315) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.661 (+/-0.315) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.661 (+/-0.315) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.698 (+/-0.305) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.698 (+/-0.306) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.695 (+/-0.302) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.695 (+/-0.303) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.697 (+/-0.307) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.679 (+/-0.289) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.679 (+/-0.292) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.661 (+/-0.312) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.661 (+/-0.316) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.660 (+/-0.315) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.660 (+/-0.315) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.697 (+/-0.304) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.696 (+/-0.303) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.695 (+/-0.303) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.695 (+/-0.302) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.680 (+/-0.294) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.662 (+/-0.308) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.663 (+/-0.312) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.660 (+/-0.313) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.660 (+/-0.318) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.659 (+/-0.314) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.659 (+/-0.313) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.696 (+/-0.303) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.696 (+/-0.302) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.679 (+/-0.291) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.695 (+/-0.301) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.680 (+/-0.295) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.662 (+/-0.310) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.661 (+/-0.314) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.660 (+/-0.313) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.660 (+/-0.317) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.659 (+/-0.314) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.659 (+/-0.312) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.695 (+/-0.302) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.695 (+/-0.301) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.679 (+/-0.292) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.679 (+/-0.290) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.662 (+/-0.315) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.662 (+/-0.311) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.660 (+/-0.313) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.660 (+/-0.312) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.659 (+/-0.315) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.658 (+/-0.314) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.658 (+/-0.311) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.694 (+/-0.301) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.695 (+/-0.299) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.696 (+/-0.304) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.662 (+/-0.311) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.662 (+/-0.315) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.661 (+/-0.312) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.659 (+/-0.312) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.659 (+/-0.312) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.658 (+/-0.314) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.658 (+/-0.312) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.658 (+/-0.309) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.694 (+/-0.301) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.694 (+/-0.300) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.679 (+/-0.292) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.662 (+/-0.311) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.662 (+/-0.315) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.661 (+/-0.312) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.659 (+/-0.313) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.658 (+/-0.312) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.683 (+/-0.367) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.659 (+/-0.311) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.657 (+/-0.309) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.694 (+/-0.301) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.694 (+/-0.300) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.663 (+/-0.313) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.662 (+/-0.311) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.662 (+/-0.315) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.661 (+/-0.312) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.659 (+/-0.314) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.658 (+/-0.312) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.683 (+/-0.367) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.658 (+/-0.310) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.657 (+/-0.308) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.694 (+/-0.301) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.695 (+/-0.301) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.663 (+/-0.315) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.662 (+/-0.312) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.661 (+/-0.315) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.660 (+/-0.312) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.659 (+/-0.315) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.658 (+/-0.311) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.683 (+/-0.366) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.658 (+/-0.310) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.658 (+/-0.309) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.694 (+/-0.301) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.695 (+/-0.301) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.663 (+/-0.315) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.662 (+/-0.312) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.661 (+/-0.315) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.660 (+/-0.312) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.658 (+/-0.314) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.658 (+/-0.310) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.683 (+/-0.365) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.658 (+/-0.310) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.657 (+/-0.310) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 1.0, 'kernel': 'linear'}
0.696 (+/-0.303) for {'C': 10.0, 'kernel': 'linear'}
0.663 (+/-0.314) for {'C': 100.0, 'kernel': 'linear'}
0.660 (+/-0.314) for {'C': 1000.0, 'kernel': 'linear'}
0.659 (+/-0.312) for {'C': 10000.0, 'kernel': 'linear'}
0.634 (+/-0.325) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.20      0.17      0.18         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.99      0.99       629

本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6983958900156649
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.606 (+/-0.180) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.645 (+/-0.363) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.584 (+/-0.153) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.576 (+/-0.141) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.585 (+/-0.145) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.590 (+/-0.158) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.585 (+/-0.162) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.602 (+/-0.189) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.583 (+/-0.190) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.582 (+/-0.190) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.578 (+/-0.189) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.610 (+/-0.294) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.582 (+/-0.178) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.581 (+/-0.147) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.581 (+/-0.144) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.581 (+/-0.146) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.597 (+/-0.160) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.601 (+/-0.184) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.581 (+/-0.187) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.583 (+/-0.190) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.581 (+/-0.191) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.582 (+/-0.190) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.591 (+/-0.281) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.586 (+/-0.177) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.587 (+/-0.154) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.586 (+/-0.152) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.591 (+/-0.153) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.600 (+/-0.182) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.593 (+/-0.184) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.581 (+/-0.191) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.583 (+/-0.190) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.582 (+/-0.190) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.582 (+/-0.190) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.594 (+/-0.282) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.579 (+/-0.143) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.591 (+/-0.156) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.596 (+/-0.181) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.598 (+/-0.178) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.593 (+/-0.187) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.585 (+/-0.196) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.581 (+/-0.191) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.583 (+/-0.190) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.579 (+/-0.189) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.580 (+/-0.188) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.594 (+/-0.281) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.582 (+/-0.146) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.594 (+/-0.175) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.596 (+/-0.182) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.606 (+/-0.175) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.592 (+/-0.188) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.581 (+/-0.191) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.581 (+/-0.191) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.580 (+/-0.188) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.581 (+/-0.191) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.581 (+/-0.187) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.599 (+/-0.282) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.584 (+/-0.144) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.591 (+/-0.175) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.591 (+/-0.177) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.589 (+/-0.182) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.591 (+/-0.188) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.585 (+/-0.196) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.581 (+/-0.191) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.580 (+/-0.189) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.582 (+/-0.190) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.580 (+/-0.188) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.602 (+/-0.281) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.580 (+/-0.144) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.599 (+/-0.181) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.578 (+/-0.188) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.599 (+/-0.178) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.591 (+/-0.188) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.576 (+/-0.174) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.577 (+/-0.189) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.579 (+/-0.189) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.581 (+/-0.191) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.580 (+/-0.188) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.608 (+/-0.287) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.582 (+/-0.145) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.594 (+/-0.175) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.578 (+/-0.188) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.599 (+/-0.178) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.592 (+/-0.188) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.576 (+/-0.174) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.577 (+/-0.189) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.582 (+/-0.187) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.582 (+/-0.190) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.580 (+/-0.188) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.608 (+/-0.287) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.580 (+/-0.144) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.586 (+/-0.185) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.578 (+/-0.188) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.586 (+/-0.150) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.592 (+/-0.188) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.576 (+/-0.174) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.577 (+/-0.189) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.583 (+/-0.186) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.582 (+/-0.190) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.579 (+/-0.189) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.609 (+/-0.286) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.583 (+/-0.144) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.585 (+/-0.185) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.577 (+/-0.188) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.586 (+/-0.150) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.591 (+/-0.188) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.576 (+/-0.174) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.577 (+/-0.189) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.583 (+/-0.186) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.582 (+/-0.190) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.580 (+/-0.188) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.608 (+/-0.286) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.582 (+/-0.144) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.586 (+/-0.186) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.577 (+/-0.188) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.586 (+/-0.150) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.591 (+/-0.188) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.576 (+/-0.174) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.577 (+/-0.189) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.583 (+/-0.186) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.582 (+/-0.190) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.580 (+/-0.188) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.798 (+/-0.437) for {'C': 0.1, 'kernel': 'linear'}
0.660 (+/-0.219) for {'C': 1.0, 'kernel': 'linear'}
0.584 (+/-0.147) for {'C': 10.0, 'kernel': 'linear'}
0.587 (+/-0.195) for {'C': 100.0, 'kernel': 'linear'}
0.585 (+/-0.188) for {'C': 1000.0, 'kernel': 'linear'}
0.585 (+/-0.189) for {'C': 10000.0, 'kernel': 'linear'}
0.580 (+/-0.195) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.696 (+/-0.300) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.695 (+/-0.299) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.694 (+/-0.299) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.693 (+/-0.300) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.694 (+/-0.304) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.693 (+/-0.304) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.691 (+/-0.304) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.715 (+/-0.351) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.657 (+/-0.314) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.657 (+/-0.311) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.656 (+/-0.310) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.693 (+/-0.296) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.692 (+/-0.296) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.694 (+/-0.300) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.694 (+/-0.301) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.693 (+/-0.305) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.717 (+/-0.353) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.698 (+/-0.344) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.681 (+/-0.363) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.657 (+/-0.314) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.656 (+/-0.311) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.657 (+/-0.310) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.691 (+/-0.297) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.693 (+/-0.298) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.694 (+/-0.304) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.693 (+/-0.303) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.717 (+/-0.353) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.699 (+/-0.344) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.671 (+/-0.292) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.656 (+/-0.310) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.657 (+/-0.313) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.657 (+/-0.311) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.657 (+/-0.310) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.692 (+/-0.297) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.694 (+/-0.299) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.694 (+/-0.303) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.693 (+/-0.304) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.699 (+/-0.345) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.707 (+/-0.340) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.655 (+/-0.314) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.656 (+/-0.311) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.657 (+/-0.314) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.657 (+/-0.310) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.658 (+/-0.310) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.692 (+/-0.300) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.694 (+/-0.301) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.678 (+/-0.291) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.693 (+/-0.303) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.724 (+/-0.313) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.706 (+/-0.341) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.655 (+/-0.313) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.656 (+/-0.310) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.657 (+/-0.313) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.657 (+/-0.311) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.658 (+/-0.311) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.693 (+/-0.301) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.694 (+/-0.301) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.677 (+/-0.292) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.676 (+/-0.289) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.706 (+/-0.342) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.705 (+/-0.341) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.655 (+/-0.313) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.656 (+/-0.310) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.657 (+/-0.312) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.657 (+/-0.312) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.657 (+/-0.310) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.693 (+/-0.302) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.694 (+/-0.300) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.694 (+/-0.305) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.658 (+/-0.310) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.731 (+/-0.311) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.705 (+/-0.340) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.655 (+/-0.311) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.656 (+/-0.310) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.656 (+/-0.312) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.657 (+/-0.311) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.658 (+/-0.309) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.694 (+/-0.303) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.694 (+/-0.301) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.677 (+/-0.293) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.658 (+/-0.310) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.731 (+/-0.311) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.705 (+/-0.341) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.655 (+/-0.311) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.656 (+/-0.309) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.682 (+/-0.365) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.657 (+/-0.310) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.657 (+/-0.309) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.693 (+/-0.304) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.694 (+/-0.301) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.661 (+/-0.313) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.658 (+/-0.310) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.730 (+/-0.310) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.705 (+/-0.340) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.655 (+/-0.311) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.656 (+/-0.309) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.682 (+/-0.366) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.657 (+/-0.311) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.657 (+/-0.308) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.694 (+/-0.303) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.694 (+/-0.301) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.661 (+/-0.312) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.658 (+/-0.310) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.730 (+/-0.310) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.705 (+/-0.340) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.655 (+/-0.311) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.656 (+/-0.309) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.682 (+/-0.366) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.657 (+/-0.311) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.658 (+/-0.309) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.694 (+/-0.303) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.694 (+/-0.302) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.661 (+/-0.313) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.658 (+/-0.310) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.730 (+/-0.310) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.705 (+/-0.340) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.655 (+/-0.311) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.657 (+/-0.309) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.682 (+/-0.366) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.657 (+/-0.311) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.657 (+/-0.310) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.700 (+/-0.309) for {'C': 0.1, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 1.0, 'kernel': 'linear'}
0.694 (+/-0.301) for {'C': 10.0, 'kernel': 'linear'}
0.657 (+/-0.313) for {'C': 100.0, 'kernel': 'linear'}
0.659 (+/-0.313) for {'C': 1000.0, 'kernel': 'linear'}
0.659 (+/-0.313) for {'C': 10000.0, 'kernel': 'linear'}
0.634 (+/-0.325) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.05      0.17      0.07         6
          1       0.99      0.97      0.98       623

avg / total       0.98      0.96      0.97       629

本轮grid search结果，得到最好的参数选择是： {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.7309279412977162
循环迭代之前，delta is: [5.84893192e+06 3.69042656e-06]
执行tunemodel()函数前，使用的grid search parameter是：
 [{'C': array([ 9999999.99999999, 10964781.96143185, 12022644.34617414,
       13182567.38556406, 14454397.70745927, 15848931.92461113,
       17378008.28749376, 19054607.17963249, 20892961.30854038,
       22908676.52767773, 25118864.31509581]), 'kernel': ['rbf'], 'gamma': array([3.98107171e-06, 4.36515832e-06, 4.78630092e-06, 5.24807460e-06,
       5.75439937e-06, 6.30957344e-06, 6.91830971e-06, 7.58577575e-06,
       8.31763771e-06, 9.12010839e-06, 1.00000000e-05])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.599 (+/-0.175) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.589 (+/-0.176) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.598 (+/-0.183) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.585 (+/-0.170) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.603 (+/-0.201) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.599 (+/-0.190) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.585 (+/-0.186) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.587 (+/-0.187) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.577 (+/-0.162) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.582 (+/-0.184) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.595 (+/-0.191) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.601 (+/-0.175) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.583 (+/-0.170) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.598 (+/-0.183) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.581 (+/-0.164) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.603 (+/-0.201) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.604 (+/-0.193) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.584 (+/-0.186) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.587 (+/-0.187) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.577 (+/-0.162) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.585 (+/-0.186) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.595 (+/-0.191) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.601 (+/-0.175) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.583 (+/-0.170) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.598 (+/-0.183) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.581 (+/-0.164) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.603 (+/-0.201) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.601 (+/-0.193) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.584 (+/-0.186) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.587 (+/-0.187) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.575 (+/-0.162) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.585 (+/-0.186) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.595 (+/-0.191) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.588 (+/-0.186) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.583 (+/-0.170) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.598 (+/-0.183) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.581 (+/-0.164) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.603 (+/-0.201) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.599 (+/-0.193) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.588 (+/-0.191) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.586 (+/-0.188) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.576 (+/-0.162) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.585 (+/-0.186) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.595 (+/-0.191) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.588 (+/-0.186) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.579 (+/-0.164) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.598 (+/-0.183) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.580 (+/-0.164) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.603 (+/-0.201) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.599 (+/-0.193) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.588 (+/-0.191) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.586 (+/-0.188) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.575 (+/-0.162) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.585 (+/-0.186) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.595 (+/-0.191) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.587 (+/-0.186) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.583 (+/-0.170) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.603 (+/-0.196) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.577 (+/-0.162) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.603 (+/-0.201) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.598 (+/-0.194) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.588 (+/-0.191) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.585 (+/-0.188) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.575 (+/-0.162) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.582 (+/-0.184) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.595 (+/-0.191) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.587 (+/-0.186) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.583 (+/-0.170) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.603 (+/-0.196) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.577 (+/-0.162) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.605 (+/-0.201) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.598 (+/-0.194) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.584 (+/-0.186) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.585 (+/-0.188) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.573 (+/-0.161) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.582 (+/-0.184) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.595 (+/-0.191) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.587 (+/-0.186) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.583 (+/-0.170) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.603 (+/-0.196) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.581 (+/-0.168) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.605 (+/-0.201) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.598 (+/-0.194) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.584 (+/-0.186) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.585 (+/-0.188) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.573 (+/-0.161) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.583 (+/-0.184) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.595 (+/-0.191) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.587 (+/-0.186) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.583 (+/-0.170) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.603 (+/-0.196) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.581 (+/-0.168) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.605 (+/-0.201) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.601 (+/-0.197) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.584 (+/-0.186) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.577 (+/-0.164) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.573 (+/-0.161) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.583 (+/-0.184) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.593 (+/-0.191) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.587 (+/-0.186) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.579 (+/-0.164) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.601 (+/-0.195) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.577 (+/-0.162) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.605 (+/-0.201) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.601 (+/-0.197) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.584 (+/-0.186) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.576 (+/-0.165) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.572 (+/-0.162) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.583 (+/-0.184) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.593 (+/-0.191) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.587 (+/-0.186) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.579 (+/-0.164) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.601 (+/-0.195) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.577 (+/-0.162) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.605 (+/-0.201) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.600 (+/-0.197) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.584 (+/-0.186) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.576 (+/-0.165) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.572 (+/-0.162) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.583 (+/-0.184) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.593 (+/-0.191) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.685 (+/-0.297) for {'C': 1.0, 'kernel': 'linear'}
0.607 (+/-0.182) for {'C': 10.0, 'kernel': 'linear'}
0.608 (+/-0.197) for {'C': 100.0, 'kernel': 'linear'}
0.589 (+/-0.189) for {'C': 1000.0, 'kernel': 'linear'}
0.585 (+/-0.189) for {'C': 10000.0, 'kernel': 'linear'}
0.580 (+/-0.195) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.679 (+/-0.290) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.662 (+/-0.308) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.663 (+/-0.315) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.662 (+/-0.308) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.662 (+/-0.316) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.662 (+/-0.315) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.661 (+/-0.309) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.661 (+/-0.311) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.661 (+/-0.311) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.661 (+/-0.310) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.662 (+/-0.311) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.679 (+/-0.290) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.662 (+/-0.307) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.663 (+/-0.315) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.662 (+/-0.309) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.662 (+/-0.316) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.662 (+/-0.315) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.660 (+/-0.309) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.660 (+/-0.311) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.660 (+/-0.310) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.661 (+/-0.310) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.662 (+/-0.312) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.679 (+/-0.290) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.662 (+/-0.307) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.663 (+/-0.315) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.662 (+/-0.309) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.663 (+/-0.315) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.662 (+/-0.315) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.660 (+/-0.309) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.661 (+/-0.311) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.660 (+/-0.308) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.661 (+/-0.310) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.661 (+/-0.312) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.662 (+/-0.311) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.662 (+/-0.307) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.663 (+/-0.315) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.662 (+/-0.309) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.663 (+/-0.315) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.662 (+/-0.315) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.661 (+/-0.309) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.660 (+/-0.311) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.660 (+/-0.309) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.661 (+/-0.310) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.661 (+/-0.312) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.662 (+/-0.311) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.662 (+/-0.307) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.663 (+/-0.315) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.661 (+/-0.308) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.663 (+/-0.315) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.662 (+/-0.314) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.661 (+/-0.309) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.660 (+/-0.311) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.660 (+/-0.309) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.660 (+/-0.311) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.661 (+/-0.312) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.662 (+/-0.311) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.662 (+/-0.307) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.663 (+/-0.316) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.661 (+/-0.308) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.662 (+/-0.316) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.662 (+/-0.315) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.661 (+/-0.309) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.660 (+/-0.311) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.660 (+/-0.309) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.660 (+/-0.311) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.661 (+/-0.312) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.662 (+/-0.311) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.662 (+/-0.307) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.663 (+/-0.316) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.661 (+/-0.308) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.662 (+/-0.316) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.662 (+/-0.314) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.660 (+/-0.309) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.660 (+/-0.311) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.659 (+/-0.308) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.660 (+/-0.311) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.661 (+/-0.312) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.662 (+/-0.311) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.662 (+/-0.307) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.663 (+/-0.316) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.661 (+/-0.308) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.662 (+/-0.316) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.662 (+/-0.315) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.660 (+/-0.309) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.660 (+/-0.311) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.659 (+/-0.307) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.660 (+/-0.311) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.661 (+/-0.312) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.662 (+/-0.311) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.662 (+/-0.307) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.663 (+/-0.316) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.661 (+/-0.308) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.662 (+/-0.316) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.662 (+/-0.316) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.660 (+/-0.309) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.659 (+/-0.311) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.659 (+/-0.307) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.660 (+/-0.312) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.661 (+/-0.312) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.662 (+/-0.311) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.661 (+/-0.307) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.663 (+/-0.316) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.661 (+/-0.308) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.662 (+/-0.316) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.662 (+/-0.316) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.660 (+/-0.309) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.659 (+/-0.311) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.659 (+/-0.307) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.660 (+/-0.311) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.661 (+/-0.312) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.662 (+/-0.311) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.661 (+/-0.307) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.663 (+/-0.316) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.661 (+/-0.308) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.662 (+/-0.316) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.662 (+/-0.315) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.660 (+/-0.309) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.659 (+/-0.311) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.659 (+/-0.307) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.660 (+/-0.311) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.661 (+/-0.312) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 1.0, 'kernel': 'linear'}
0.696 (+/-0.303) for {'C': 10.0, 'kernel': 'linear'}
0.663 (+/-0.314) for {'C': 100.0, 'kernel': 'linear'}
0.660 (+/-0.314) for {'C': 1000.0, 'kernel': 'linear'}
0.659 (+/-0.312) for {'C': 10000.0, 'kernel': 'linear'}
0.634 (+/-0.325) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.20      0.17      0.18         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.99      0.99       629

本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6983958900156649
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.591 (+/-0.177) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.580 (+/-0.169) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.573 (+/-0.163) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.575 (+/-0.171) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.585 (+/-0.192) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.589 (+/-0.182) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.585 (+/-0.182) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.581 (+/-0.191) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.569 (+/-0.165) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.578 (+/-0.186) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.591 (+/-0.188) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.593 (+/-0.179) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.575 (+/-0.162) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.572 (+/-0.163) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.571 (+/-0.164) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.585 (+/-0.192) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.593 (+/-0.187) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.585 (+/-0.182) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.580 (+/-0.192) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.569 (+/-0.165) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.581 (+/-0.187) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.591 (+/-0.188) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.592 (+/-0.180) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.575 (+/-0.162) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.572 (+/-0.163) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.571 (+/-0.164) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.585 (+/-0.192) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.599 (+/-0.178) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.585 (+/-0.182) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.580 (+/-0.191) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.569 (+/-0.165) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.581 (+/-0.187) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.591 (+/-0.189) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.579 (+/-0.187) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.575 (+/-0.162) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.572 (+/-0.163) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.571 (+/-0.164) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.585 (+/-0.192) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.593 (+/-0.187) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.589 (+/-0.187) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.580 (+/-0.192) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.569 (+/-0.165) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.581 (+/-0.187) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.591 (+/-0.188) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.579 (+/-0.188) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.575 (+/-0.162) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.572 (+/-0.163) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.569 (+/-0.163) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.585 (+/-0.192) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.599 (+/-0.178) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.589 (+/-0.187) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.580 (+/-0.192) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.570 (+/-0.165) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.581 (+/-0.187) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.591 (+/-0.188) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.578 (+/-0.188) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.575 (+/-0.162) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.572 (+/-0.163) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.569 (+/-0.163) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.585 (+/-0.192) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.599 (+/-0.178) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.589 (+/-0.187) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.583 (+/-0.189) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.570 (+/-0.165) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.578 (+/-0.186) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.591 (+/-0.188) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.578 (+/-0.188) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.575 (+/-0.162) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.572 (+/-0.163) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.569 (+/-0.163) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.585 (+/-0.192) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.599 (+/-0.178) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.585 (+/-0.182) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.583 (+/-0.189) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.568 (+/-0.164) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.579 (+/-0.186) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.591 (+/-0.188) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.578 (+/-0.188) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.575 (+/-0.162) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.572 (+/-0.163) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.573 (+/-0.170) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.585 (+/-0.192) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.599 (+/-0.178) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.585 (+/-0.182) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.583 (+/-0.189) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.568 (+/-0.164) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.579 (+/-0.186) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.591 (+/-0.188) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.578 (+/-0.188) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.574 (+/-0.162) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.572 (+/-0.163) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.574 (+/-0.170) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.585 (+/-0.192) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.599 (+/-0.178) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.585 (+/-0.182) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.575 (+/-0.165) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.568 (+/-0.164) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.579 (+/-0.186) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.591 (+/-0.188) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.578 (+/-0.188) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.574 (+/-0.162) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.569 (+/-0.161) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.569 (+/-0.163) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.585 (+/-0.192) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.599 (+/-0.178) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.585 (+/-0.182) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.575 (+/-0.165) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.568 (+/-0.164) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.579 (+/-0.186) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.591 (+/-0.188) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.578 (+/-0.188) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.574 (+/-0.162) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.569 (+/-0.161) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.569 (+/-0.163) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.585 (+/-0.192) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.599 (+/-0.178) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.585 (+/-0.182) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.575 (+/-0.165) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.568 (+/-0.164) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.579 (+/-0.186) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.592 (+/-0.188) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.798 (+/-0.437) for {'C': 0.1, 'kernel': 'linear'}
0.660 (+/-0.219) for {'C': 1.0, 'kernel': 'linear'}
0.584 (+/-0.147) for {'C': 10.0, 'kernel': 'linear'}
0.587 (+/-0.195) for {'C': 100.0, 'kernel': 'linear'}
0.585 (+/-0.188) for {'C': 1000.0, 'kernel': 'linear'}
0.585 (+/-0.189) for {'C': 10000.0, 'kernel': 'linear'}
0.580 (+/-0.195) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.676 (+/-0.289) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.683 (+/-0.365) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.657 (+/-0.313) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.657 (+/-0.311) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.680 (+/-0.369) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.706 (+/-0.342) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.705 (+/-0.343) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.655 (+/-0.311) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.654 (+/-0.310) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.680 (+/-0.363) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.705 (+/-0.341) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.676 (+/-0.290) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.682 (+/-0.366) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.657 (+/-0.312) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.657 (+/-0.310) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.680 (+/-0.370) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.705 (+/-0.344) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.705 (+/-0.343) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.655 (+/-0.310) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.654 (+/-0.311) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.681 (+/-0.363) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.705 (+/-0.341) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.675 (+/-0.291) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.682 (+/-0.366) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.657 (+/-0.312) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.657 (+/-0.311) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.680 (+/-0.370) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.730 (+/-0.313) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.705 (+/-0.343) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.655 (+/-0.311) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.653 (+/-0.312) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.681 (+/-0.363) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.705 (+/-0.340) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.658 (+/-0.310) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.682 (+/-0.366) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.657 (+/-0.312) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.656 (+/-0.310) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.680 (+/-0.370) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.706 (+/-0.342) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.705 (+/-0.343) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.654 (+/-0.311) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.654 (+/-0.312) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.681 (+/-0.363) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.705 (+/-0.340) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.658 (+/-0.310) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.682 (+/-0.366) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.657 (+/-0.312) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.656 (+/-0.310) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.679 (+/-0.371) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.731 (+/-0.310) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.705 (+/-0.343) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.654 (+/-0.311) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.654 (+/-0.313) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.681 (+/-0.363) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.705 (+/-0.340) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.658 (+/-0.310) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.682 (+/-0.367) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.657 (+/-0.313) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.656 (+/-0.311) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.679 (+/-0.370) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.731 (+/-0.311) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.705 (+/-0.343) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.679 (+/-0.365) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.654 (+/-0.312) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.680 (+/-0.363) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.705 (+/-0.340) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.658 (+/-0.310) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.682 (+/-0.367) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.657 (+/-0.313) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.656 (+/-0.311) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.679 (+/-0.370) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.731 (+/-0.311) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.704 (+/-0.343) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.679 (+/-0.365) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.653 (+/-0.312) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.680 (+/-0.364) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.705 (+/-0.340) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.658 (+/-0.310) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.682 (+/-0.367) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.657 (+/-0.313) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.656 (+/-0.310) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.679 (+/-0.370) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.731 (+/-0.311) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.704 (+/-0.343) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.680 (+/-0.365) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.653 (+/-0.312) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.681 (+/-0.364) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.705 (+/-0.340) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.658 (+/-0.310) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.682 (+/-0.366) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.657 (+/-0.312) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.656 (+/-0.311) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.679 (+/-0.370) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.731 (+/-0.311) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.704 (+/-0.343) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.680 (+/-0.364) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.653 (+/-0.312) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.681 (+/-0.364) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.705 (+/-0.340) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.658 (+/-0.310) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.682 (+/-0.366) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.657 (+/-0.312) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.656 (+/-0.310) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.679 (+/-0.371) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.731 (+/-0.311) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.704 (+/-0.343) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.680 (+/-0.364) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.653 (+/-0.312) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.680 (+/-0.364) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.705 (+/-0.340) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.658 (+/-0.310) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.681 (+/-0.367) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.656 (+/-0.312) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.656 (+/-0.310) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.679 (+/-0.371) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.731 (+/-0.311) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.704 (+/-0.343) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.680 (+/-0.364) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.653 (+/-0.312) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.680 (+/-0.364) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.705 (+/-0.341) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.700 (+/-0.309) for {'C': 0.1, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 1.0, 'kernel': 'linear'}
0.694 (+/-0.301) for {'C': 10.0, 'kernel': 'linear'}
0.657 (+/-0.313) for {'C': 100.0, 'kernel': 'linear'}
0.659 (+/-0.313) for {'C': 1000.0, 'kernel': 'linear'}
0.659 (+/-0.313) for {'C': 10000.0, 'kernel': 'linear'}
0.634 (+/-0.325) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.05      0.17      0.07         6
          1       0.99      0.97      0.98       623

avg / total       0.98      0.96      0.97       629

本轮grid search结果，得到最好的参数选择是： {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.7310881977079726
第0轮loop中，tunemodel函数得到的最优参数是： ([{'C': array([13182567.38556406, 13427649.61137862, 13677288.25595846,
       13931568.02945303, 14190575.21689092, 14454397.70745927,
       14723125.02432717, 14996848.35502371, 15275660.58238074,
       15559656.31605075, 15848931.92461113]), 'kernel': ['rbf'], 'gamma': array([5.75439937e-06, 5.86138165e-06, 5.97035287e-06, 6.08135001e-06,
       6.19441075e-06, 6.30957344e-06, 6.42687717e-06, 6.54636174e-06,
       6.66806769e-06, 6.79203633e-06, 6.91830971e-06])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}], {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}, 0.7310881977079726)
这是第0次迭代微调C和gamma。
第0次迭代，得到delta： [1394534.21715187       0.        ]
执行tunemodel()函数前，使用的grid search parameter是：
 [{'C': array([13182567.38556406, 13427649.61137862, 13677288.25595846,
       13931568.02945303, 14190575.21689092, 14454397.70745927,
       14723125.02432717, 14996848.35502371, 15275660.58238074,
       15559656.31605075, 15848931.92461113]), 'kernel': ['rbf'], 'gamma': array([5.75439937e-06, 5.86138165e-06, 5.97035287e-06, 6.08135001e-06,
       6.19441075e-06, 6.30957344e-06, 6.42687717e-06, 6.54636174e-06,
       6.66806769e-06, 6.79203633e-06, 6.91830971e-06])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.603 (+/-0.201) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.585 (+/-0.170) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 5.861381645140292e-06}
0.581 (+/-0.164) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 5.970352865838371e-06}
0.588 (+/-0.186) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 6.081350012787179e-06}
0.597 (+/-0.191) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 6.194410750767812e-06}
0.599 (+/-0.193) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.592 (+/-0.177) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 6.426877173170205e-06}
0.598 (+/-0.197) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 6.546361740672756e-06}
0.600 (+/-0.197) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 6.668067692136225e-06}
0.587 (+/-0.191) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 6.792036326171847e-06}
0.588 (+/-0.191) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.603 (+/-0.201) for {'C': 13427649.611378616, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.587 (+/-0.170) for {'C': 13427649.611378616, 'kernel': 'rbf', 'gamma': 5.861381645140292e-06}
0.581 (+/-0.164) for {'C': 13427649.611378616, 'kernel': 'rbf', 'gamma': 5.970352865838371e-06}
0.588 (+/-0.186) for {'C': 13427649.611378616, 'kernel': 'rbf', 'gamma': 6.081350012787179e-06}
0.597 (+/-0.191) for {'C': 13427649.611378616, 'kernel': 'rbf', 'gamma': 6.194410750767812e-06}
0.599 (+/-0.193) for {'C': 13427649.611378616, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.592 (+/-0.177) for {'C': 13427649.611378616, 'kernel': 'rbf', 'gamma': 6.426877173170205e-06}
0.598 (+/-0.197) for {'C': 13427649.611378616, 'kernel': 'rbf', 'gamma': 6.546361740672756e-06}
0.600 (+/-0.197) for {'C': 13427649.611378616, 'kernel': 'rbf', 'gamma': 6.668067692136225e-06}
0.587 (+/-0.191) for {'C': 13427649.611378616, 'kernel': 'rbf', 'gamma': 6.792036326171847e-06}
0.588 (+/-0.191) for {'C': 13427649.611378616, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.603 (+/-0.201) for {'C': 13677288.255958464, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.587 (+/-0.170) for {'C': 13677288.255958464, 'kernel': 'rbf', 'gamma': 5.861381645140292e-06}
0.581 (+/-0.164) for {'C': 13677288.255958464, 'kernel': 'rbf', 'gamma': 5.970352865838371e-06}
0.588 (+/-0.186) for {'C': 13677288.255958464, 'kernel': 'rbf', 'gamma': 6.081350012787179e-06}
0.597 (+/-0.191) for {'C': 13677288.255958464, 'kernel': 'rbf', 'gamma': 6.194410750767812e-06}
0.599 (+/-0.193) for {'C': 13677288.255958464, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.592 (+/-0.177) for {'C': 13677288.255958464, 'kernel': 'rbf', 'gamma': 6.426877173170205e-06}
0.598 (+/-0.197) for {'C': 13677288.255958464, 'kernel': 'rbf', 'gamma': 6.546361740672756e-06}
0.600 (+/-0.197) for {'C': 13677288.255958464, 'kernel': 'rbf', 'gamma': 6.668067692136225e-06}
0.587 (+/-0.191) for {'C': 13677288.255958464, 'kernel': 'rbf', 'gamma': 6.792036326171847e-06}
0.588 (+/-0.191) for {'C': 13677288.255958464, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.603 (+/-0.201) for {'C': 13931568.029453032, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.585 (+/-0.170) for {'C': 13931568.029453032, 'kernel': 'rbf', 'gamma': 5.861381645140292e-06}
0.581 (+/-0.164) for {'C': 13931568.029453032, 'kernel': 'rbf', 'gamma': 5.970352865838371e-06}
0.588 (+/-0.186) for {'C': 13931568.029453032, 'kernel': 'rbf', 'gamma': 6.081350012787179e-06}
0.597 (+/-0.191) for {'C': 13931568.029453032, 'kernel': 'rbf', 'gamma': 6.194410750767812e-06}
0.599 (+/-0.193) for {'C': 13931568.029453032, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.592 (+/-0.177) for {'C': 13931568.029453032, 'kernel': 'rbf', 'gamma': 6.426877173170205e-06}
0.598 (+/-0.197) for {'C': 13931568.029453032, 'kernel': 'rbf', 'gamma': 6.546361740672756e-06}
0.596 (+/-0.193) for {'C': 13931568.029453032, 'kernel': 'rbf', 'gamma': 6.668067692136225e-06}
0.587 (+/-0.191) for {'C': 13931568.029453032, 'kernel': 'rbf', 'gamma': 6.792036326171847e-06}
0.588 (+/-0.191) for {'C': 13931568.029453032, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.603 (+/-0.201) for {'C': 14190575.216890918, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.587 (+/-0.170) for {'C': 14190575.216890918, 'kernel': 'rbf', 'gamma': 5.861381645140292e-06}
0.581 (+/-0.164) for {'C': 14190575.216890918, 'kernel': 'rbf', 'gamma': 5.970352865838371e-06}
0.588 (+/-0.186) for {'C': 14190575.216890918, 'kernel': 'rbf', 'gamma': 6.081350012787179e-06}
0.597 (+/-0.191) for {'C': 14190575.216890918, 'kernel': 'rbf', 'gamma': 6.194410750767812e-06}
0.599 (+/-0.193) for {'C': 14190575.216890918, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.591 (+/-0.177) for {'C': 14190575.216890918, 'kernel': 'rbf', 'gamma': 6.426877173170205e-06}
0.598 (+/-0.197) for {'C': 14190575.216890918, 'kernel': 'rbf', 'gamma': 6.546361740672756e-06}
0.596 (+/-0.193) for {'C': 14190575.216890918, 'kernel': 'rbf', 'gamma': 6.668067692136225e-06}
0.587 (+/-0.191) for {'C': 14190575.216890918, 'kernel': 'rbf', 'gamma': 6.792036326171847e-06}
0.588 (+/-0.191) for {'C': 14190575.216890918, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.603 (+/-0.201) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.584 (+/-0.169) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 5.861381645140292e-06}
0.581 (+/-0.164) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 5.970352865838371e-06}
0.586 (+/-0.185) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 6.081350012787179e-06}
0.597 (+/-0.191) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 6.194410750767812e-06}
0.599 (+/-0.193) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.591 (+/-0.177) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 6.426877173170205e-06}
0.598 (+/-0.197) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 6.546361740672756e-06}
0.596 (+/-0.193) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 6.668067692136225e-06}
0.587 (+/-0.191) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 6.792036326171847e-06}
0.588 (+/-0.191) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.603 (+/-0.201) for {'C': 14723125.024327174, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.584 (+/-0.169) for {'C': 14723125.024327174, 'kernel': 'rbf', 'gamma': 5.861381645140292e-06}
0.581 (+/-0.164) for {'C': 14723125.024327174, 'kernel': 'rbf', 'gamma': 5.970352865838371e-06}
0.588 (+/-0.186) for {'C': 14723125.024327174, 'kernel': 'rbf', 'gamma': 6.081350012787179e-06}
0.597 (+/-0.191) for {'C': 14723125.024327174, 'kernel': 'rbf', 'gamma': 6.194410750767812e-06}
0.599 (+/-0.193) for {'C': 14723125.024327174, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.591 (+/-0.177) for {'C': 14723125.024327174, 'kernel': 'rbf', 'gamma': 6.426877173170205e-06}
0.598 (+/-0.197) for {'C': 14723125.024327174, 'kernel': 'rbf', 'gamma': 6.546361740672756e-06}
0.596 (+/-0.193) for {'C': 14723125.024327174, 'kernel': 'rbf', 'gamma': 6.668067692136225e-06}
0.587 (+/-0.191) for {'C': 14723125.024327174, 'kernel': 'rbf', 'gamma': 6.792036326171847e-06}
0.588 (+/-0.191) for {'C': 14723125.024327174, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.603 (+/-0.201) for {'C': 14996848.355023714, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.584 (+/-0.169) for {'C': 14996848.355023714, 'kernel': 'rbf', 'gamma': 5.861381645140292e-06}
0.581 (+/-0.164) for {'C': 14996848.355023714, 'kernel': 'rbf', 'gamma': 5.970352865838371e-06}
0.588 (+/-0.186) for {'C': 14996848.355023714, 'kernel': 'rbf', 'gamma': 6.081350012787179e-06}
0.597 (+/-0.191) for {'C': 14996848.355023714, 'kernel': 'rbf', 'gamma': 6.194410750767812e-06}
0.599 (+/-0.193) for {'C': 14996848.355023714, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.591 (+/-0.177) for {'C': 14996848.355023714, 'kernel': 'rbf', 'gamma': 6.426877173170205e-06}
0.598 (+/-0.197) for {'C': 14996848.355023714, 'kernel': 'rbf', 'gamma': 6.546361740672756e-06}
0.596 (+/-0.193) for {'C': 14996848.355023714, 'kernel': 'rbf', 'gamma': 6.668067692136225e-06}
0.587 (+/-0.191) for {'C': 14996848.355023714, 'kernel': 'rbf', 'gamma': 6.792036326171847e-06}
0.588 (+/-0.191) for {'C': 14996848.355023714, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.603 (+/-0.201) for {'C': 15275660.582380736, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.584 (+/-0.169) for {'C': 15275660.582380736, 'kernel': 'rbf', 'gamma': 5.861381645140292e-06}
0.581 (+/-0.164) for {'C': 15275660.582380736, 'kernel': 'rbf', 'gamma': 5.970352865838371e-06}
0.588 (+/-0.186) for {'C': 15275660.582380736, 'kernel': 'rbf', 'gamma': 6.081350012787179e-06}
0.597 (+/-0.191) for {'C': 15275660.582380736, 'kernel': 'rbf', 'gamma': 6.194410750767812e-06}
0.599 (+/-0.193) for {'C': 15275660.582380736, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.591 (+/-0.177) for {'C': 15275660.582380736, 'kernel': 'rbf', 'gamma': 6.426877173170205e-06}
0.598 (+/-0.197) for {'C': 15275660.582380736, 'kernel': 'rbf', 'gamma': 6.546361740672756e-06}
0.596 (+/-0.193) for {'C': 15275660.582380736, 'kernel': 'rbf', 'gamma': 6.668067692136225e-06}
0.587 (+/-0.191) for {'C': 15275660.582380736, 'kernel': 'rbf', 'gamma': 6.792036326171847e-06}
0.588 (+/-0.191) for {'C': 15275660.582380736, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.603 (+/-0.201) for {'C': 15559656.31605075, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.587 (+/-0.170) for {'C': 15559656.31605075, 'kernel': 'rbf', 'gamma': 5.861381645140292e-06}
0.581 (+/-0.164) for {'C': 15559656.31605075, 'kernel': 'rbf', 'gamma': 5.970352865838371e-06}
0.588 (+/-0.186) for {'C': 15559656.31605075, 'kernel': 'rbf', 'gamma': 6.081350012787179e-06}
0.597 (+/-0.191) for {'C': 15559656.31605075, 'kernel': 'rbf', 'gamma': 6.194410750767812e-06}
0.599 (+/-0.193) for {'C': 15559656.31605075, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.591 (+/-0.177) for {'C': 15559656.31605075, 'kernel': 'rbf', 'gamma': 6.426877173170205e-06}
0.598 (+/-0.197) for {'C': 15559656.31605075, 'kernel': 'rbf', 'gamma': 6.546361740672756e-06}
0.596 (+/-0.193) for {'C': 15559656.31605075, 'kernel': 'rbf', 'gamma': 6.668067692136225e-06}
0.587 (+/-0.191) for {'C': 15559656.31605075, 'kernel': 'rbf', 'gamma': 6.792036326171847e-06}
0.588 (+/-0.191) for {'C': 15559656.31605075, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.603 (+/-0.201) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.587 (+/-0.170) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 5.861381645140292e-06}
0.581 (+/-0.164) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 5.970352865838371e-06}
0.588 (+/-0.186) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.081350012787179e-06}
0.597 (+/-0.191) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.194410750767812e-06}
0.598 (+/-0.194) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.591 (+/-0.177) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.426877173170205e-06}
0.598 (+/-0.197) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.546361740672756e-06}
0.596 (+/-0.193) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.668067692136225e-06}
0.587 (+/-0.191) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.792036326171847e-06}
0.588 (+/-0.191) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.685 (+/-0.297) for {'C': 1.0, 'kernel': 'linear'}
0.607 (+/-0.182) for {'C': 10.0, 'kernel': 'linear'}
0.608 (+/-0.197) for {'C': 100.0, 'kernel': 'linear'}
0.589 (+/-0.189) for {'C': 1000.0, 'kernel': 'linear'}
0.585 (+/-0.189) for {'C': 10000.0, 'kernel': 'linear'}
0.580 (+/-0.195) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.663 (+/-0.315) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.662 (+/-0.314) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 5.861381645140292e-06}
0.661 (+/-0.309) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 5.970352865838371e-06}
0.661 (+/-0.310) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 6.081350012787179e-06}
0.662 (+/-0.314) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 6.194410750767812e-06}
0.662 (+/-0.315) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.662 (+/-0.313) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 6.426877173170205e-06}
0.663 (+/-0.315) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 6.546361740672756e-06}
0.662 (+/-0.314) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 6.668067692136225e-06}
0.661 (+/-0.311) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 6.792036326171847e-06}
0.661 (+/-0.309) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.663 (+/-0.315) for {'C': 13427649.611378616, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.662 (+/-0.315) for {'C': 13427649.611378616, 'kernel': 'rbf', 'gamma': 5.861381645140292e-06}
0.661 (+/-0.309) for {'C': 13427649.611378616, 'kernel': 'rbf', 'gamma': 5.970352865838371e-06}
0.661 (+/-0.310) for {'C': 13427649.611378616, 'kernel': 'rbf', 'gamma': 6.081350012787179e-06}
0.662 (+/-0.314) for {'C': 13427649.611378616, 'kernel': 'rbf', 'gamma': 6.194410750767812e-06}
0.662 (+/-0.314) for {'C': 13427649.611378616, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.662 (+/-0.313) for {'C': 13427649.611378616, 'kernel': 'rbf', 'gamma': 6.426877173170205e-06}
0.663 (+/-0.315) for {'C': 13427649.611378616, 'kernel': 'rbf', 'gamma': 6.546361740672756e-06}
0.662 (+/-0.314) for {'C': 13427649.611378616, 'kernel': 'rbf', 'gamma': 6.668067692136225e-06}
0.661 (+/-0.311) for {'C': 13427649.611378616, 'kernel': 'rbf', 'gamma': 6.792036326171847e-06}
0.661 (+/-0.309) for {'C': 13427649.611378616, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.663 (+/-0.315) for {'C': 13677288.255958464, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.662 (+/-0.315) for {'C': 13677288.255958464, 'kernel': 'rbf', 'gamma': 5.861381645140292e-06}
0.661 (+/-0.309) for {'C': 13677288.255958464, 'kernel': 'rbf', 'gamma': 5.970352865838371e-06}
0.661 (+/-0.310) for {'C': 13677288.255958464, 'kernel': 'rbf', 'gamma': 6.081350012787179e-06}
0.662 (+/-0.314) for {'C': 13677288.255958464, 'kernel': 'rbf', 'gamma': 6.194410750767812e-06}
0.662 (+/-0.314) for {'C': 13677288.255958464, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.662 (+/-0.314) for {'C': 13677288.255958464, 'kernel': 'rbf', 'gamma': 6.426877173170205e-06}
0.663 (+/-0.315) for {'C': 13677288.255958464, 'kernel': 'rbf', 'gamma': 6.546361740672756e-06}
0.662 (+/-0.314) for {'C': 13677288.255958464, 'kernel': 'rbf', 'gamma': 6.668067692136225e-06}
0.661 (+/-0.311) for {'C': 13677288.255958464, 'kernel': 'rbf', 'gamma': 6.792036326171847e-06}
0.661 (+/-0.309) for {'C': 13677288.255958464, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.663 (+/-0.315) for {'C': 13931568.029453032, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.662 (+/-0.314) for {'C': 13931568.029453032, 'kernel': 'rbf', 'gamma': 5.861381645140292e-06}
0.661 (+/-0.309) for {'C': 13931568.029453032, 'kernel': 'rbf', 'gamma': 5.970352865838371e-06}
0.661 (+/-0.310) for {'C': 13931568.029453032, 'kernel': 'rbf', 'gamma': 6.081350012787179e-06}
0.662 (+/-0.314) for {'C': 13931568.029453032, 'kernel': 'rbf', 'gamma': 6.194410750767812e-06}
0.662 (+/-0.315) for {'C': 13931568.029453032, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.662 (+/-0.313) for {'C': 13931568.029453032, 'kernel': 'rbf', 'gamma': 6.426877173170205e-06}
0.663 (+/-0.315) for {'C': 13931568.029453032, 'kernel': 'rbf', 'gamma': 6.546361740672756e-06}
0.662 (+/-0.314) for {'C': 13931568.029453032, 'kernel': 'rbf', 'gamma': 6.668067692136225e-06}
0.661 (+/-0.311) for {'C': 13931568.029453032, 'kernel': 'rbf', 'gamma': 6.792036326171847e-06}
0.661 (+/-0.309) for {'C': 13931568.029453032, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.663 (+/-0.315) for {'C': 14190575.216890918, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.662 (+/-0.315) for {'C': 14190575.216890918, 'kernel': 'rbf', 'gamma': 5.861381645140292e-06}
0.661 (+/-0.309) for {'C': 14190575.216890918, 'kernel': 'rbf', 'gamma': 5.970352865838371e-06}
0.661 (+/-0.310) for {'C': 14190575.216890918, 'kernel': 'rbf', 'gamma': 6.081350012787179e-06}
0.662 (+/-0.314) for {'C': 14190575.216890918, 'kernel': 'rbf', 'gamma': 6.194410750767812e-06}
0.662 (+/-0.314) for {'C': 14190575.216890918, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.662 (+/-0.313) for {'C': 14190575.216890918, 'kernel': 'rbf', 'gamma': 6.426877173170205e-06}
0.663 (+/-0.315) for {'C': 14190575.216890918, 'kernel': 'rbf', 'gamma': 6.546361740672756e-06}
0.662 (+/-0.314) for {'C': 14190575.216890918, 'kernel': 'rbf', 'gamma': 6.668067692136225e-06}
0.661 (+/-0.311) for {'C': 14190575.216890918, 'kernel': 'rbf', 'gamma': 6.792036326171847e-06}
0.661 (+/-0.309) for {'C': 14190575.216890918, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.663 (+/-0.315) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.662 (+/-0.314) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 5.861381645140292e-06}
0.661 (+/-0.309) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 5.970352865838371e-06}
0.661 (+/-0.310) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 6.081350012787179e-06}
0.662 (+/-0.314) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 6.194410750767812e-06}
0.662 (+/-0.314) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.662 (+/-0.313) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 6.426877173170205e-06}
0.663 (+/-0.315) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 6.546361740672756e-06}
0.662 (+/-0.314) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 6.668067692136225e-06}
0.661 (+/-0.311) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 6.792036326171847e-06}
0.661 (+/-0.309) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.663 (+/-0.315) for {'C': 14723125.024327174, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.662 (+/-0.314) for {'C': 14723125.024327174, 'kernel': 'rbf', 'gamma': 5.861381645140292e-06}
0.660 (+/-0.309) for {'C': 14723125.024327174, 'kernel': 'rbf', 'gamma': 5.970352865838371e-06}
0.661 (+/-0.310) for {'C': 14723125.024327174, 'kernel': 'rbf', 'gamma': 6.081350012787179e-06}
0.662 (+/-0.314) for {'C': 14723125.024327174, 'kernel': 'rbf', 'gamma': 6.194410750767812e-06}
0.662 (+/-0.315) for {'C': 14723125.024327174, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.662 (+/-0.313) for {'C': 14723125.024327174, 'kernel': 'rbf', 'gamma': 6.426877173170205e-06}
0.663 (+/-0.315) for {'C': 14723125.024327174, 'kernel': 'rbf', 'gamma': 6.546361740672756e-06}
0.662 (+/-0.314) for {'C': 14723125.024327174, 'kernel': 'rbf', 'gamma': 6.668067692136225e-06}
0.660 (+/-0.311) for {'C': 14723125.024327174, 'kernel': 'rbf', 'gamma': 6.792036326171847e-06}
0.661 (+/-0.309) for {'C': 14723125.024327174, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.663 (+/-0.315) for {'C': 14996848.355023714, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.662 (+/-0.314) for {'C': 14996848.355023714, 'kernel': 'rbf', 'gamma': 5.861381645140292e-06}
0.660 (+/-0.309) for {'C': 14996848.355023714, 'kernel': 'rbf', 'gamma': 5.970352865838371e-06}
0.661 (+/-0.310) for {'C': 14996848.355023714, 'kernel': 'rbf', 'gamma': 6.081350012787179e-06}
0.662 (+/-0.314) for {'C': 14996848.355023714, 'kernel': 'rbf', 'gamma': 6.194410750767812e-06}
0.662 (+/-0.314) for {'C': 14996848.355023714, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.662 (+/-0.313) for {'C': 14996848.355023714, 'kernel': 'rbf', 'gamma': 6.426877173170205e-06}
0.663 (+/-0.315) for {'C': 14996848.355023714, 'kernel': 'rbf', 'gamma': 6.546361740672756e-06}
0.662 (+/-0.314) for {'C': 14996848.355023714, 'kernel': 'rbf', 'gamma': 6.668067692136225e-06}
0.660 (+/-0.311) for {'C': 14996848.355023714, 'kernel': 'rbf', 'gamma': 6.792036326171847e-06}
0.661 (+/-0.309) for {'C': 14996848.355023714, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.662 (+/-0.316) for {'C': 15275660.582380736, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.662 (+/-0.314) for {'C': 15275660.582380736, 'kernel': 'rbf', 'gamma': 5.861381645140292e-06}
0.660 (+/-0.309) for {'C': 15275660.582380736, 'kernel': 'rbf', 'gamma': 5.970352865838371e-06}
0.661 (+/-0.310) for {'C': 15275660.582380736, 'kernel': 'rbf', 'gamma': 6.081350012787179e-06}
0.662 (+/-0.314) for {'C': 15275660.582380736, 'kernel': 'rbf', 'gamma': 6.194410750767812e-06}
0.662 (+/-0.314) for {'C': 15275660.582380736, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.662 (+/-0.313) for {'C': 15275660.582380736, 'kernel': 'rbf', 'gamma': 6.426877173170205e-06}
0.663 (+/-0.315) for {'C': 15275660.582380736, 'kernel': 'rbf', 'gamma': 6.546361740672756e-06}
0.662 (+/-0.314) for {'C': 15275660.582380736, 'kernel': 'rbf', 'gamma': 6.668067692136225e-06}
0.660 (+/-0.312) for {'C': 15275660.582380736, 'kernel': 'rbf', 'gamma': 6.792036326171847e-06}
0.661 (+/-0.309) for {'C': 15275660.582380736, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.662 (+/-0.316) for {'C': 15559656.31605075, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.662 (+/-0.315) for {'C': 15559656.31605075, 'kernel': 'rbf', 'gamma': 5.861381645140292e-06}
0.660 (+/-0.309) for {'C': 15559656.31605075, 'kernel': 'rbf', 'gamma': 5.970352865838371e-06}
0.661 (+/-0.310) for {'C': 15559656.31605075, 'kernel': 'rbf', 'gamma': 6.081350012787179e-06}
0.662 (+/-0.314) for {'C': 15559656.31605075, 'kernel': 'rbf', 'gamma': 6.194410750767812e-06}
0.662 (+/-0.315) for {'C': 15559656.31605075, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.662 (+/-0.313) for {'C': 15559656.31605075, 'kernel': 'rbf', 'gamma': 6.426877173170205e-06}
0.662 (+/-0.315) for {'C': 15559656.31605075, 'kernel': 'rbf', 'gamma': 6.546361740672756e-06}
0.662 (+/-0.314) for {'C': 15559656.31605075, 'kernel': 'rbf', 'gamma': 6.668067692136225e-06}
0.660 (+/-0.312) for {'C': 15559656.31605075, 'kernel': 'rbf', 'gamma': 6.792036326171847e-06}
0.661 (+/-0.309) for {'C': 15559656.31605075, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.662 (+/-0.316) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.662 (+/-0.315) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 5.861381645140292e-06}
0.660 (+/-0.309) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 5.970352865838371e-06}
0.661 (+/-0.310) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.081350012787179e-06}
0.662 (+/-0.314) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.194410750767812e-06}
0.662 (+/-0.315) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.662 (+/-0.313) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.426877173170205e-06}
0.662 (+/-0.315) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.546361740672756e-06}
0.662 (+/-0.314) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.668067692136225e-06}
0.660 (+/-0.312) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.792036326171847e-06}
0.661 (+/-0.309) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 1.0, 'kernel': 'linear'}
0.696 (+/-0.303) for {'C': 10.0, 'kernel': 'linear'}
0.663 (+/-0.314) for {'C': 100.0, 'kernel': 'linear'}
0.660 (+/-0.314) for {'C': 1000.0, 'kernel': 'linear'}
0.659 (+/-0.312) for {'C': 10000.0, 'kernel': 'linear'}
0.634 (+/-0.325) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.20      0.17      0.18         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.99      0.99       629

本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6983958900156649
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.585 (+/-0.192) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.568 (+/-0.155) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 5.861381645140292e-06}
0.572 (+/-0.161) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 5.970352865838371e-06}
0.579 (+/-0.183) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 6.081350012787179e-06}
0.590 (+/-0.190) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 6.194410750767812e-06}
0.593 (+/-0.187) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.587 (+/-0.172) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 6.426877173170205e-06}
0.582 (+/-0.187) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 6.546361740672756e-06}
0.588 (+/-0.194) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 6.668067692136225e-06}
0.587 (+/-0.187) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 6.792036326171847e-06}
0.589 (+/-0.187) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.585 (+/-0.192) for {'C': 13427649.611378616, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.570 (+/-0.156) for {'C': 13427649.611378616, 'kernel': 'rbf', 'gamma': 5.861381645140292e-06}
0.572 (+/-0.161) for {'C': 13427649.611378616, 'kernel': 'rbf', 'gamma': 5.970352865838371e-06}
0.579 (+/-0.183) for {'C': 13427649.611378616, 'kernel': 'rbf', 'gamma': 6.081350012787179e-06}
0.590 (+/-0.190) for {'C': 13427649.611378616, 'kernel': 'rbf', 'gamma': 6.194410750767812e-06}
0.593 (+/-0.187) for {'C': 13427649.611378616, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.587 (+/-0.172) for {'C': 13427649.611378616, 'kernel': 'rbf', 'gamma': 6.426877173170205e-06}
0.582 (+/-0.187) for {'C': 13427649.611378616, 'kernel': 'rbf', 'gamma': 6.546361740672756e-06}
0.588 (+/-0.194) for {'C': 13427649.611378616, 'kernel': 'rbf', 'gamma': 6.668067692136225e-06}
0.587 (+/-0.187) for {'C': 13427649.611378616, 'kernel': 'rbf', 'gamma': 6.792036326171847e-06}
0.589 (+/-0.187) for {'C': 13427649.611378616, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.585 (+/-0.192) for {'C': 13677288.255958464, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.569 (+/-0.156) for {'C': 13677288.255958464, 'kernel': 'rbf', 'gamma': 5.861381645140292e-06}
0.572 (+/-0.161) for {'C': 13677288.255958464, 'kernel': 'rbf', 'gamma': 5.970352865838371e-06}
0.579 (+/-0.183) for {'C': 13677288.255958464, 'kernel': 'rbf', 'gamma': 6.081350012787179e-06}
0.590 (+/-0.190) for {'C': 13677288.255958464, 'kernel': 'rbf', 'gamma': 6.194410750767812e-06}
0.593 (+/-0.187) for {'C': 13677288.255958464, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.587 (+/-0.172) for {'C': 13677288.255958464, 'kernel': 'rbf', 'gamma': 6.426877173170205e-06}
0.582 (+/-0.187) for {'C': 13677288.255958464, 'kernel': 'rbf', 'gamma': 6.546361740672756e-06}
0.588 (+/-0.194) for {'C': 13677288.255958464, 'kernel': 'rbf', 'gamma': 6.668067692136225e-06}
0.587 (+/-0.187) for {'C': 13677288.255958464, 'kernel': 'rbf', 'gamma': 6.792036326171847e-06}
0.589 (+/-0.187) for {'C': 13677288.255958464, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.585 (+/-0.192) for {'C': 13931568.029453032, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.567 (+/-0.155) for {'C': 13931568.029453032, 'kernel': 'rbf', 'gamma': 5.861381645140292e-06}
0.572 (+/-0.161) for {'C': 13931568.029453032, 'kernel': 'rbf', 'gamma': 5.970352865838371e-06}
0.579 (+/-0.183) for {'C': 13931568.029453032, 'kernel': 'rbf', 'gamma': 6.081350012787179e-06}
0.590 (+/-0.190) for {'C': 13931568.029453032, 'kernel': 'rbf', 'gamma': 6.194410750767812e-06}
0.593 (+/-0.187) for {'C': 13931568.029453032, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.587 (+/-0.172) for {'C': 13931568.029453032, 'kernel': 'rbf', 'gamma': 6.426877173170205e-06}
0.582 (+/-0.187) for {'C': 13931568.029453032, 'kernel': 'rbf', 'gamma': 6.546361740672756e-06}
0.584 (+/-0.189) for {'C': 13931568.029453032, 'kernel': 'rbf', 'gamma': 6.668067692136225e-06}
0.587 (+/-0.187) for {'C': 13931568.029453032, 'kernel': 'rbf', 'gamma': 6.792036326171847e-06}
0.589 (+/-0.187) for {'C': 13931568.029453032, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.585 (+/-0.192) for {'C': 14190575.216890918, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.569 (+/-0.156) for {'C': 14190575.216890918, 'kernel': 'rbf', 'gamma': 5.861381645140292e-06}
0.572 (+/-0.161) for {'C': 14190575.216890918, 'kernel': 'rbf', 'gamma': 5.970352865838371e-06}
0.579 (+/-0.183) for {'C': 14190575.216890918, 'kernel': 'rbf', 'gamma': 6.081350012787179e-06}
0.590 (+/-0.190) for {'C': 14190575.216890918, 'kernel': 'rbf', 'gamma': 6.194410750767812e-06}
0.593 (+/-0.187) for {'C': 14190575.216890918, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.587 (+/-0.172) for {'C': 14190575.216890918, 'kernel': 'rbf', 'gamma': 6.426877173170205e-06}
0.582 (+/-0.187) for {'C': 14190575.216890918, 'kernel': 'rbf', 'gamma': 6.546361740672756e-06}
0.584 (+/-0.189) for {'C': 14190575.216890918, 'kernel': 'rbf', 'gamma': 6.668067692136225e-06}
0.587 (+/-0.187) for {'C': 14190575.216890918, 'kernel': 'rbf', 'gamma': 6.792036326171847e-06}
0.589 (+/-0.187) for {'C': 14190575.216890918, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.585 (+/-0.192) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.569 (+/-0.156) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 5.861381645140292e-06}
0.572 (+/-0.161) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 5.970352865838371e-06}
0.578 (+/-0.183) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 6.081350012787179e-06}
0.590 (+/-0.190) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 6.194410750767812e-06}
0.599 (+/-0.178) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.587 (+/-0.172) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 6.426877173170205e-06}
0.582 (+/-0.187) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 6.546361740672756e-06}
0.584 (+/-0.188) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 6.668067692136225e-06}
0.587 (+/-0.187) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 6.792036326171847e-06}
0.589 (+/-0.187) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.585 (+/-0.192) for {'C': 14723125.024327174, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.569 (+/-0.156) for {'C': 14723125.024327174, 'kernel': 'rbf', 'gamma': 5.861381645140292e-06}
0.572 (+/-0.161) for {'C': 14723125.024327174, 'kernel': 'rbf', 'gamma': 5.970352865838371e-06}
0.579 (+/-0.183) for {'C': 14723125.024327174, 'kernel': 'rbf', 'gamma': 6.081350012787179e-06}
0.590 (+/-0.190) for {'C': 14723125.024327174, 'kernel': 'rbf', 'gamma': 6.194410750767812e-06}
0.599 (+/-0.178) for {'C': 14723125.024327174, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.587 (+/-0.172) for {'C': 14723125.024327174, 'kernel': 'rbf', 'gamma': 6.426877173170205e-06}
0.582 (+/-0.187) for {'C': 14723125.024327174, 'kernel': 'rbf', 'gamma': 6.546361740672756e-06}
0.584 (+/-0.188) for {'C': 14723125.024327174, 'kernel': 'rbf', 'gamma': 6.668067692136225e-06}
0.587 (+/-0.187) for {'C': 14723125.024327174, 'kernel': 'rbf', 'gamma': 6.792036326171847e-06}
0.589 (+/-0.187) for {'C': 14723125.024327174, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.585 (+/-0.192) for {'C': 14996848.355023714, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.569 (+/-0.156) for {'C': 14996848.355023714, 'kernel': 'rbf', 'gamma': 5.861381645140292e-06}
0.572 (+/-0.161) for {'C': 14996848.355023714, 'kernel': 'rbf', 'gamma': 5.970352865838371e-06}
0.579 (+/-0.183) for {'C': 14996848.355023714, 'kernel': 'rbf', 'gamma': 6.081350012787179e-06}
0.589 (+/-0.190) for {'C': 14996848.355023714, 'kernel': 'rbf', 'gamma': 6.194410750767812e-06}
0.599 (+/-0.178) for {'C': 14996848.355023714, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.587 (+/-0.172) for {'C': 14996848.355023714, 'kernel': 'rbf', 'gamma': 6.426877173170205e-06}
0.582 (+/-0.187) for {'C': 14996848.355023714, 'kernel': 'rbf', 'gamma': 6.546361740672756e-06}
0.584 (+/-0.188) for {'C': 14996848.355023714, 'kernel': 'rbf', 'gamma': 6.668067692136225e-06}
0.587 (+/-0.187) for {'C': 14996848.355023714, 'kernel': 'rbf', 'gamma': 6.792036326171847e-06}
0.589 (+/-0.187) for {'C': 14996848.355023714, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.585 (+/-0.192) for {'C': 15275660.582380736, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.569 (+/-0.156) for {'C': 15275660.582380736, 'kernel': 'rbf', 'gamma': 5.861381645140292e-06}
0.572 (+/-0.161) for {'C': 15275660.582380736, 'kernel': 'rbf', 'gamma': 5.970352865838371e-06}
0.579 (+/-0.183) for {'C': 15275660.582380736, 'kernel': 'rbf', 'gamma': 6.081350012787179e-06}
0.589 (+/-0.190) for {'C': 15275660.582380736, 'kernel': 'rbf', 'gamma': 6.194410750767812e-06}
0.599 (+/-0.178) for {'C': 15275660.582380736, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.587 (+/-0.172) for {'C': 15275660.582380736, 'kernel': 'rbf', 'gamma': 6.426877173170205e-06}
0.582 (+/-0.187) for {'C': 15275660.582380736, 'kernel': 'rbf', 'gamma': 6.546361740672756e-06}
0.584 (+/-0.188) for {'C': 15275660.582380736, 'kernel': 'rbf', 'gamma': 6.668067692136225e-06}
0.587 (+/-0.187) for {'C': 15275660.582380736, 'kernel': 'rbf', 'gamma': 6.792036326171847e-06}
0.589 (+/-0.187) for {'C': 15275660.582380736, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.585 (+/-0.192) for {'C': 15559656.31605075, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.569 (+/-0.156) for {'C': 15559656.31605075, 'kernel': 'rbf', 'gamma': 5.861381645140292e-06}
0.572 (+/-0.161) for {'C': 15559656.31605075, 'kernel': 'rbf', 'gamma': 5.970352865838371e-06}
0.579 (+/-0.183) for {'C': 15559656.31605075, 'kernel': 'rbf', 'gamma': 6.081350012787179e-06}
0.589 (+/-0.190) for {'C': 15559656.31605075, 'kernel': 'rbf', 'gamma': 6.194410750767812e-06}
0.599 (+/-0.178) for {'C': 15559656.31605075, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.587 (+/-0.172) for {'C': 15559656.31605075, 'kernel': 'rbf', 'gamma': 6.426877173170205e-06}
0.582 (+/-0.187) for {'C': 15559656.31605075, 'kernel': 'rbf', 'gamma': 6.546361740672756e-06}
0.584 (+/-0.188) for {'C': 15559656.31605075, 'kernel': 'rbf', 'gamma': 6.668067692136225e-06}
0.587 (+/-0.187) for {'C': 15559656.31605075, 'kernel': 'rbf', 'gamma': 6.792036326171847e-06}
0.589 (+/-0.187) for {'C': 15559656.31605075, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.585 (+/-0.192) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.569 (+/-0.156) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 5.861381645140292e-06}
0.572 (+/-0.161) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 5.970352865838371e-06}
0.579 (+/-0.183) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.081350012787179e-06}
0.589 (+/-0.190) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.194410750767812e-06}
0.599 (+/-0.178) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.587 (+/-0.172) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.426877173170205e-06}
0.582 (+/-0.187) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.546361740672756e-06}
0.584 (+/-0.188) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.668067692136225e-06}
0.587 (+/-0.187) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.792036326171847e-06}
0.589 (+/-0.187) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.798 (+/-0.437) for {'C': 0.1, 'kernel': 'linear'}
0.660 (+/-0.219) for {'C': 1.0, 'kernel': 'linear'}
0.584 (+/-0.147) for {'C': 10.0, 'kernel': 'linear'}
0.587 (+/-0.195) for {'C': 100.0, 'kernel': 'linear'}
0.585 (+/-0.188) for {'C': 1000.0, 'kernel': 'linear'}
0.585 (+/-0.189) for {'C': 10000.0, 'kernel': 'linear'}
0.580 (+/-0.195) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.680 (+/-0.370) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.702 (+/-0.334) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 5.861381645140292e-06}
0.679 (+/-0.287) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 5.970352865838371e-06}
0.680 (+/-0.289) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 6.081350012787179e-06}
0.682 (+/-0.290) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 6.194410750767812e-06}
0.706 (+/-0.342) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.705 (+/-0.341) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 6.426877173170205e-06}
0.680 (+/-0.369) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 6.546361740672756e-06}
0.681 (+/-0.364) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 6.668067692136225e-06}
0.706 (+/-0.338) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 6.792036326171847e-06}
0.705 (+/-0.343) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.679 (+/-0.370) for {'C': 13427649.611378616, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.703 (+/-0.336) for {'C': 13427649.611378616, 'kernel': 'rbf', 'gamma': 5.861381645140292e-06}
0.679 (+/-0.287) for {'C': 13427649.611378616, 'kernel': 'rbf', 'gamma': 5.970352865838371e-06}
0.680 (+/-0.289) for {'C': 13427649.611378616, 'kernel': 'rbf', 'gamma': 6.081350012787179e-06}
0.682 (+/-0.290) for {'C': 13427649.611378616, 'kernel': 'rbf', 'gamma': 6.194410750767812e-06}
0.706 (+/-0.342) for {'C': 13427649.611378616, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.705 (+/-0.342) for {'C': 13427649.611378616, 'kernel': 'rbf', 'gamma': 6.426877173170205e-06}
0.680 (+/-0.369) for {'C': 13427649.611378616, 'kernel': 'rbf', 'gamma': 6.546361740672756e-06}
0.682 (+/-0.365) for {'C': 13427649.611378616, 'kernel': 'rbf', 'gamma': 6.668067692136225e-06}
0.706 (+/-0.338) for {'C': 13427649.611378616, 'kernel': 'rbf', 'gamma': 6.792036326171847e-06}
0.705 (+/-0.343) for {'C': 13427649.611378616, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.679 (+/-0.370) for {'C': 13677288.255958464, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.702 (+/-0.334) for {'C': 13677288.255958464, 'kernel': 'rbf', 'gamma': 5.861381645140292e-06}
0.679 (+/-0.287) for {'C': 13677288.255958464, 'kernel': 'rbf', 'gamma': 5.970352865838371e-06}
0.680 (+/-0.289) for {'C': 13677288.255958464, 'kernel': 'rbf', 'gamma': 6.081350012787179e-06}
0.682 (+/-0.290) for {'C': 13677288.255958464, 'kernel': 'rbf', 'gamma': 6.194410750767812e-06}
0.706 (+/-0.342) for {'C': 13677288.255958464, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.705 (+/-0.341) for {'C': 13677288.255958464, 'kernel': 'rbf', 'gamma': 6.426877173170205e-06}
0.680 (+/-0.369) for {'C': 13677288.255958464, 'kernel': 'rbf', 'gamma': 6.546361740672756e-06}
0.682 (+/-0.365) for {'C': 13677288.255958464, 'kernel': 'rbf', 'gamma': 6.668067692136225e-06}
0.706 (+/-0.338) for {'C': 13677288.255958464, 'kernel': 'rbf', 'gamma': 6.792036326171847e-06}
0.705 (+/-0.343) for {'C': 13677288.255958464, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.679 (+/-0.371) for {'C': 13931568.029453032, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.702 (+/-0.333) for {'C': 13931568.029453032, 'kernel': 'rbf', 'gamma': 5.861381645140292e-06}
0.679 (+/-0.287) for {'C': 13931568.029453032, 'kernel': 'rbf', 'gamma': 5.970352865838371e-06}
0.680 (+/-0.289) for {'C': 13931568.029453032, 'kernel': 'rbf', 'gamma': 6.081350012787179e-06}
0.682 (+/-0.290) for {'C': 13931568.029453032, 'kernel': 'rbf', 'gamma': 6.194410750767812e-06}
0.706 (+/-0.342) for {'C': 13931568.029453032, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.705 (+/-0.342) for {'C': 13931568.029453032, 'kernel': 'rbf', 'gamma': 6.426877173170205e-06}
0.680 (+/-0.369) for {'C': 13931568.029453032, 'kernel': 'rbf', 'gamma': 6.546361740672756e-06}
0.681 (+/-0.364) for {'C': 13931568.029453032, 'kernel': 'rbf', 'gamma': 6.668067692136225e-06}
0.706 (+/-0.338) for {'C': 13931568.029453032, 'kernel': 'rbf', 'gamma': 6.792036326171847e-06}
0.705 (+/-0.343) for {'C': 13931568.029453032, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.679 (+/-0.371) for {'C': 14190575.216890918, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.702 (+/-0.334) for {'C': 14190575.216890918, 'kernel': 'rbf', 'gamma': 5.861381645140292e-06}
0.679 (+/-0.287) for {'C': 14190575.216890918, 'kernel': 'rbf', 'gamma': 5.970352865838371e-06}
0.680 (+/-0.289) for {'C': 14190575.216890918, 'kernel': 'rbf', 'gamma': 6.081350012787179e-06}
0.682 (+/-0.290) for {'C': 14190575.216890918, 'kernel': 'rbf', 'gamma': 6.194410750767812e-06}
0.706 (+/-0.342) for {'C': 14190575.216890918, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.705 (+/-0.342) for {'C': 14190575.216890918, 'kernel': 'rbf', 'gamma': 6.426877173170205e-06}
0.680 (+/-0.369) for {'C': 14190575.216890918, 'kernel': 'rbf', 'gamma': 6.546361740672756e-06}
0.681 (+/-0.364) for {'C': 14190575.216890918, 'kernel': 'rbf', 'gamma': 6.668067692136225e-06}
0.706 (+/-0.338) for {'C': 14190575.216890918, 'kernel': 'rbf', 'gamma': 6.792036326171847e-06}
0.705 (+/-0.343) for {'C': 14190575.216890918, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.679 (+/-0.371) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.702 (+/-0.334) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 5.861381645140292e-06}
0.679 (+/-0.287) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 5.970352865838371e-06}
0.680 (+/-0.289) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 6.081350012787179e-06}
0.682 (+/-0.290) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 6.194410750767812e-06}
0.731 (+/-0.310) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.705 (+/-0.342) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 6.426877173170205e-06}
0.680 (+/-0.369) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 6.546361740672756e-06}
0.681 (+/-0.365) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 6.668067692136225e-06}
0.706 (+/-0.338) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 6.792036326171847e-06}
0.705 (+/-0.343) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.679 (+/-0.371) for {'C': 14723125.024327174, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.702 (+/-0.334) for {'C': 14723125.024327174, 'kernel': 'rbf', 'gamma': 5.861381645140292e-06}
0.679 (+/-0.287) for {'C': 14723125.024327174, 'kernel': 'rbf', 'gamma': 5.970352865838371e-06}
0.680 (+/-0.289) for {'C': 14723125.024327174, 'kernel': 'rbf', 'gamma': 6.081350012787179e-06}
0.682 (+/-0.290) for {'C': 14723125.024327174, 'kernel': 'rbf', 'gamma': 6.194410750767812e-06}
0.731 (+/-0.310) for {'C': 14723125.024327174, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.705 (+/-0.342) for {'C': 14723125.024327174, 'kernel': 'rbf', 'gamma': 6.426877173170205e-06}
0.680 (+/-0.369) for {'C': 14723125.024327174, 'kernel': 'rbf', 'gamma': 6.546361740672756e-06}
0.681 (+/-0.365) for {'C': 14723125.024327174, 'kernel': 'rbf', 'gamma': 6.668067692136225e-06}
0.706 (+/-0.338) for {'C': 14723125.024327174, 'kernel': 'rbf', 'gamma': 6.792036326171847e-06}
0.705 (+/-0.343) for {'C': 14723125.024327174, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.679 (+/-0.371) for {'C': 14996848.355023714, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.702 (+/-0.334) for {'C': 14996848.355023714, 'kernel': 'rbf', 'gamma': 5.861381645140292e-06}
0.679 (+/-0.287) for {'C': 14996848.355023714, 'kernel': 'rbf', 'gamma': 5.970352865838371e-06}
0.680 (+/-0.289) for {'C': 14996848.355023714, 'kernel': 'rbf', 'gamma': 6.081350012787179e-06}
0.682 (+/-0.290) for {'C': 14996848.355023714, 'kernel': 'rbf', 'gamma': 6.194410750767812e-06}
0.731 (+/-0.310) for {'C': 14996848.355023714, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.705 (+/-0.342) for {'C': 14996848.355023714, 'kernel': 'rbf', 'gamma': 6.426877173170205e-06}
0.680 (+/-0.368) for {'C': 14996848.355023714, 'kernel': 'rbf', 'gamma': 6.546361740672756e-06}
0.681 (+/-0.365) for {'C': 14996848.355023714, 'kernel': 'rbf', 'gamma': 6.668067692136225e-06}
0.706 (+/-0.338) for {'C': 14996848.355023714, 'kernel': 'rbf', 'gamma': 6.792036326171847e-06}
0.705 (+/-0.343) for {'C': 14996848.355023714, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.679 (+/-0.371) for {'C': 15275660.582380736, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.702 (+/-0.334) for {'C': 15275660.582380736, 'kernel': 'rbf', 'gamma': 5.861381645140292e-06}
0.679 (+/-0.287) for {'C': 15275660.582380736, 'kernel': 'rbf', 'gamma': 5.970352865838371e-06}
0.680 (+/-0.290) for {'C': 15275660.582380736, 'kernel': 'rbf', 'gamma': 6.081350012787179e-06}
0.682 (+/-0.290) for {'C': 15275660.582380736, 'kernel': 'rbf', 'gamma': 6.194410750767812e-06}
0.731 (+/-0.310) for {'C': 15275660.582380736, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.705 (+/-0.342) for {'C': 15275660.582380736, 'kernel': 'rbf', 'gamma': 6.426877173170205e-06}
0.680 (+/-0.369) for {'C': 15275660.582380736, 'kernel': 'rbf', 'gamma': 6.546361740672756e-06}
0.681 (+/-0.365) for {'C': 15275660.582380736, 'kernel': 'rbf', 'gamma': 6.668067692136225e-06}
0.705 (+/-0.339) for {'C': 15275660.582380736, 'kernel': 'rbf', 'gamma': 6.792036326171847e-06}
0.705 (+/-0.343) for {'C': 15275660.582380736, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.679 (+/-0.371) for {'C': 15559656.31605075, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.702 (+/-0.334) for {'C': 15559656.31605075, 'kernel': 'rbf', 'gamma': 5.861381645140292e-06}
0.679 (+/-0.288) for {'C': 15559656.31605075, 'kernel': 'rbf', 'gamma': 5.970352865838371e-06}
0.680 (+/-0.290) for {'C': 15559656.31605075, 'kernel': 'rbf', 'gamma': 6.081350012787179e-06}
0.682 (+/-0.290) for {'C': 15559656.31605075, 'kernel': 'rbf', 'gamma': 6.194410750767812e-06}
0.731 (+/-0.311) for {'C': 15559656.31605075, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.705 (+/-0.342) for {'C': 15559656.31605075, 'kernel': 'rbf', 'gamma': 6.426877173170205e-06}
0.680 (+/-0.370) for {'C': 15559656.31605075, 'kernel': 'rbf', 'gamma': 6.546361740672756e-06}
0.681 (+/-0.365) for {'C': 15559656.31605075, 'kernel': 'rbf', 'gamma': 6.668067692136225e-06}
0.705 (+/-0.339) for {'C': 15559656.31605075, 'kernel': 'rbf', 'gamma': 6.792036326171847e-06}
0.705 (+/-0.343) for {'C': 15559656.31605075, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.679 (+/-0.370) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.702 (+/-0.334) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 5.861381645140292e-06}
0.679 (+/-0.288) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 5.970352865838371e-06}
0.680 (+/-0.290) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.081350012787179e-06}
0.682 (+/-0.290) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.194410750767812e-06}
0.731 (+/-0.311) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.705 (+/-0.342) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.426877173170205e-06}
0.680 (+/-0.370) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.546361740672756e-06}
0.681 (+/-0.365) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.668067692136225e-06}
0.705 (+/-0.339) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.792036326171847e-06}
0.705 (+/-0.343) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.700 (+/-0.309) for {'C': 0.1, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 1.0, 'kernel': 'linear'}
0.694 (+/-0.301) for {'C': 10.0, 'kernel': 'linear'}
0.657 (+/-0.313) for {'C': 100.0, 'kernel': 'linear'}
0.659 (+/-0.313) for {'C': 1000.0, 'kernel': 'linear'}
0.659 (+/-0.313) for {'C': 10000.0, 'kernel': 'linear'}
0.634 (+/-0.325) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.05      0.17      0.07         6
          1       0.99      0.97      0.98       623

avg / total       0.98      0.96      0.97       629

本轮grid search结果，得到最好的参数选择是： {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.7310881977079726
第1轮loop中，tunemodel函数得到的最优参数是： ([{'C': array([14190575.21689092, 14242951.64973292, 14295521.40037034,
       14348285.18232589, 14401243.71175568, 14454397.70745927,
       14507747.89088912, 14561294.98616055, 14615039.72006164,
       14668982.82206284, 14723125.02432717]), 'kernel': ['rbf'], 'gamma': array([6.19441075e-06, 6.21727389e-06, 6.24022142e-06, 6.26325365e-06,
       6.28637088e-06, 6.30957344e-06, 6.33286164e-06, 6.35623580e-06,
       6.37969623e-06, 6.40324324e-06, 6.42687717e-06])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}], {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}, 0.7310881977079726)
这是第1次迭代微调C和gamma。
第1次迭代，得到delta： [0. 0.]
SVC模型clf_op_iter的参数是： {'kernel': 'rbf', 'decision_function_shape': 'ovr', 'class_weight': {-1: 15}, 'tol': 0.001, 'gamma': 6.309573444801928e-06, 'random_state': 0, 'cache_size': 200, 'verbose': False, 'degree': 3, 'C': 14454397.707459265, 'probability': False, 'shrinking': True, 'coef0': 0.0, 'max_iter': -1}

训练模型SVC对训练样本的预测准确率： 0.9539333805811481

测试集中，预测为舞弊样本的有： (array([   0,    1,    2, ..., 1254, 1255, 1256], dtype=int64),) 
测试集中，实际为舞弊样本的有： (array([1246, 1247, 1248, 1249, 1250, 1251, 1252, 1253, 1254, 1255, 1256],
      dtype=int64),) 
预测的舞弊样本数目： 1150

训练模型SVC对测试样本的预测准确率： 0.18143160878809356
以上是第41次特征筛选。
第41次特征筛选，AUC值是： 0.4970815701152779
X_train_iter_svc.shape is: (1257, 11)
X_test_iter_svc.shape is: (1257, 11)
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.722 (+/-0.473) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.604 (+/-0.307) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.660 (+/-0.219) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.612 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.698 (+/-0.306) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.20      0.17      0.18         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.99      0.99       629

本轮grid search结果，得到最好的参数选择是： {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6982356336054085
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.660 (+/-0.219) for {'C': 1.0, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.581 (+/-0.185) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.698 (+/-0.306) for {'C': 1.0, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.659 (+/-0.314) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6982356336054085
RBF的粗grid search最佳超参： {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001} RBF的粗grid search最高分值： 0.6982356336054085
linear的粗grid search最佳超参： {'C': 1.0, 'kernel': 'linear'} linear的粗grid search最高分值： 0.6982356336054085
粗grid search得到的parameter是：
 [{'C': array([1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03, 1.e+04, 1.e+05,
       1.e+06, 1.e+07, 1.e+08]), 'kernel': ['rbf'], 'gamma': array([1.e-08, 1.e-07, 1.e-06, 1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01,
       1.e+00, 1.e+01, 1.e+02])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.848 (+/-0.459) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.848 (+/-0.459) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.685 (+/-0.297) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.611 (+/-0.308) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.848 (+/-0.459) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.660 (+/-0.219) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.637 (+/-0.286) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.604 (+/-0.307) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.848 (+/-0.459) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.660 (+/-0.219) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.606 (+/-0.183) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.312) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.604 (+/-0.307) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.848 (+/-0.459) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.660 (+/-0.219) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.601 (+/-0.179) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.590 (+/-0.187) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.599 (+/-0.306) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.604 (+/-0.307) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.848 (+/-0.459) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.660 (+/-0.219) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.604 (+/-0.182) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.587 (+/-0.185) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.567 (+/-0.197) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.599 (+/-0.306) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.604 (+/-0.307) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.748 (+/-0.396) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.660 (+/-0.219) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.602 (+/-0.182) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.597 (+/-0.211) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.582 (+/-0.186) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.571 (+/-0.192) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.599 (+/-0.306) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.604 (+/-0.307) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.848 (+/-0.460) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.703 (+/-0.418) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.628 (+/-0.288) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.586 (+/-0.187) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.578 (+/-0.186) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.573 (+/-0.192) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.572 (+/-0.192) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.599 (+/-0.306) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.604 (+/-0.307) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.793 (+/-0.440) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.690 (+/-0.435) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.622 (+/-0.290) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.570 (+/-0.154) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.578 (+/-0.186) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.574 (+/-0.191) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.572 (+/-0.192) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.599 (+/-0.306) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.604 (+/-0.307) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.685 (+/-0.297) for {'C': 1.0, 'kernel': 'linear'}
0.604 (+/-0.181) for {'C': 10.0, 'kernel': 'linear'}
0.609 (+/-0.201) for {'C': 100.0, 'kernel': 'linear'}
0.584 (+/-0.186) for {'C': 1000.0, 'kernel': 'linear'}
0.577 (+/-0.192) for {'C': 10000.0, 'kernel': 'linear'}
0.576 (+/-0.191) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      1.00       623

avg / total       0.98      0.99      0.99       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.658 (+/-0.217) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.658 (+/-0.217) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.698 (+/-0.306) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.236) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.007) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.658 (+/-0.217) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.306) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.696 (+/-0.303) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.611 (+/-0.240) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.006) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.658 (+/-0.217) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.698 (+/-0.306) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.696 (+/-0.303) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.587 (+/-0.230) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.612 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.006) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.658 (+/-0.217) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.698 (+/-0.306) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.695 (+/-0.303) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.662 (+/-0.315) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.611 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.006) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.658 (+/-0.217) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.698 (+/-0.306) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.696 (+/-0.304) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.661 (+/-0.313) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.585 (+/-0.234) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.006) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.699 (+/-0.308) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.698 (+/-0.306) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.695 (+/-0.300) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.661 (+/-0.318) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.659 (+/-0.313) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.610 (+/-0.238) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.238) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.237) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.006) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.683 (+/-0.296) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.683 (+/-0.258) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.696 (+/-0.303) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.660 (+/-0.310) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.658 (+/-0.311) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.632 (+/-0.323) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.611 (+/-0.237) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.238) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.237) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.006) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.699 (+/-0.307) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.633 (+/-0.237) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.695 (+/-0.302) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.659 (+/-0.310) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.657 (+/-0.309) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.633 (+/-0.322) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.610 (+/-0.238) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.238) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.237) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.006) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 1.0, 'kernel': 'linear'}
0.696 (+/-0.302) for {'C': 10.0, 'kernel': 'linear'}
0.663 (+/-0.315) for {'C': 100.0, 'kernel': 'linear'}
0.660 (+/-0.314) for {'C': 1000.0, 'kernel': 'linear'}
0.635 (+/-0.324) for {'C': 10000.0, 'kernel': 'linear'}
0.634 (+/-0.325) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6991982026547944
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.669 (+/-0.296) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.667 (+/-0.292) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.656 (+/-0.384) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.642 (+/-0.204) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.199) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.591 (+/-0.295) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.642 (+/-0.204) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.624 (+/-0.186) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.280) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.604 (+/-0.307) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.642 (+/-0.204) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.624 (+/-0.186) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.582 (+/-0.153) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.594 (+/-0.308) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.604 (+/-0.307) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.642 (+/-0.204) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.624 (+/-0.186) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.578 (+/-0.146) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.578 (+/-0.187) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.599 (+/-0.306) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.604 (+/-0.307) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.747 (+/-0.502) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.642 (+/-0.204) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.624 (+/-0.186) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.578 (+/-0.146) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.575 (+/-0.186) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.573 (+/-0.192) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.599 (+/-0.306) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.604 (+/-0.307) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.647 (+/-0.460) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.656 (+/-0.231) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.622 (+/-0.197) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.584 (+/-0.154) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.575 (+/-0.187) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.580 (+/-0.186) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.571 (+/-0.192) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.599 (+/-0.306) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.604 (+/-0.307) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.848 (+/-0.460) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.542 (+/-0.143) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.593 (+/-0.280) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.583 (+/-0.189) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.577 (+/-0.186) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.573 (+/-0.192) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.572 (+/-0.192) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.599 (+/-0.306) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.604 (+/-0.307) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.793 (+/-0.440) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.553 (+/-0.296) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.609 (+/-0.286) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.568 (+/-0.155) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.578 (+/-0.186) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.574 (+/-0.191) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.572 (+/-0.192) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.599 (+/-0.306) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.604 (+/-0.307) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.798 (+/-0.437) for {'C': 0.1, 'kernel': 'linear'}
0.660 (+/-0.219) for {'C': 1.0, 'kernel': 'linear'}
0.585 (+/-0.147) for {'C': 10.0, 'kernel': 'linear'}
0.582 (+/-0.190) for {'C': 100.0, 'kernel': 'linear'}
0.581 (+/-0.185) for {'C': 1000.0, 'kernel': 'linear'}
0.576 (+/-0.191) for {'C': 10000.0, 'kernel': 'linear'}
0.575 (+/-0.191) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.682 (+/-0.294) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.698 (+/-0.306) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.494 (+/-0.012) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.306) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.305) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.611 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.007) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.698 (+/-0.306) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.304) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.693 (+/-0.304) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.611 (+/-0.241) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.006) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.698 (+/-0.306) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.304) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.693 (+/-0.304) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.608 (+/-0.241) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.612 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.006) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.698 (+/-0.306) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.304) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.692 (+/-0.303) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.657 (+/-0.312) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.611 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.006) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.617 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.698 (+/-0.306) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.697 (+/-0.304) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.692 (+/-0.304) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.656 (+/-0.312) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.608 (+/-0.242) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.006) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.575 (+/-0.229) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.698 (+/-0.308) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.697 (+/-0.303) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.693 (+/-0.303) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.656 (+/-0.311) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.658 (+/-0.312) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.610 (+/-0.238) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.238) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.237) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.006) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.700 (+/-0.309) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.655 (+/-0.246) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.692 (+/-0.299) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.680 (+/-0.364) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.657 (+/-0.310) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.632 (+/-0.323) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.611 (+/-0.237) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.238) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.237) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.006) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.699 (+/-0.307) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.604 (+/-0.160) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.694 (+/-0.304) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.679 (+/-0.363) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.657 (+/-0.309) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.633 (+/-0.322) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.610 (+/-0.238) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.238) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.237) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.006) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.700 (+/-0.309) for {'C': 0.1, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 1.0, 'kernel': 'linear'}
0.694 (+/-0.301) for {'C': 10.0, 'kernel': 'linear'}
0.657 (+/-0.313) for {'C': 100.0, 'kernel': 'linear'}
0.659 (+/-0.314) for {'C': 1000.0, 'kernel': 'linear'}
0.634 (+/-0.324) for {'C': 10000.0, 'kernel': 'linear'}
0.633 (+/-0.324) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.7
发现最优参数gamma为原先的最大/最小值，直接重新设置超参。
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-09}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-08}
0.748 (+/-0.396) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-07}
0.660 (+/-0.219) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-06}
0.602 (+/-0.182) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-05}
0.597 (+/-0.211) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.0001}
0.582 (+/-0.186) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-09}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-08}
0.723 (+/-0.359) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-07}
0.672 (+/-0.295) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-06}
0.598 (+/-0.164) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-05}
0.586 (+/-0.188) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.0001}
0.580 (+/-0.186) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-09}
0.747 (+/-0.502) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-08}
0.681 (+/-0.297) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-07}
0.653 (+/-0.286) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-06}
0.611 (+/-0.183) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-05}
0.579 (+/-0.187) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.0001}
0.579 (+/-0.186) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-09}
0.747 (+/-0.502) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-08}
0.698 (+/-0.353) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-07}
0.649 (+/-0.286) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-06}
0.597 (+/-0.192) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-05}
0.579 (+/-0.186) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 0.0001}
0.575 (+/-0.191) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-09}
0.797 (+/-0.492) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-08}
0.710 (+/-0.406) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-07}
0.645 (+/-0.287) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-06}
0.587 (+/-0.187) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-05}
0.577 (+/-0.185) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 0.0001}
0.574 (+/-0.192) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-09}
0.848 (+/-0.460) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-08}
0.703 (+/-0.418) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-07}
0.628 (+/-0.288) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-06}
0.586 (+/-0.187) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-05}
0.578 (+/-0.186) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 0.0001}
0.573 (+/-0.192) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-09}
0.823 (+/-0.451) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-08}
0.703 (+/-0.418) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-07}
0.624 (+/-0.290) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-06}
0.586 (+/-0.187) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-05}
0.577 (+/-0.186) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 0.0001}
0.573 (+/-0.192) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-10}
0.747 (+/-0.502) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-09}
0.806 (+/-0.436) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-08}
0.690 (+/-0.372) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-07}
0.624 (+/-0.290) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-06}
0.584 (+/-0.186) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-05}
0.577 (+/-0.186) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.0001}
0.573 (+/-0.192) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-10}
0.747 (+/-0.502) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-09}
0.798 (+/-0.437) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-08}
0.715 (+/-0.415) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-07}
0.624 (+/-0.290) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-06}
0.570 (+/-0.154) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-05}
0.577 (+/-0.186) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 0.0001}
0.573 (+/-0.192) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-10}
0.797 (+/-0.492) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-09}
0.793 (+/-0.440) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-08}
0.740 (+/-0.449) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-07}
0.622 (+/-0.290) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-06}
0.570 (+/-0.154) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-05}
0.578 (+/-0.186) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 0.0001}
0.574 (+/-0.191) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-10}
0.848 (+/-0.460) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-09}
0.793 (+/-0.440) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-08}
0.690 (+/-0.435) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-07}
0.622 (+/-0.290) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-06}
0.570 (+/-0.154) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-05}
0.578 (+/-0.186) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 0.0001}
0.574 (+/-0.191) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.685 (+/-0.297) for {'C': 1.0, 'kernel': 'linear'}
0.604 (+/-0.181) for {'C': 10.0, 'kernel': 'linear'}
0.609 (+/-0.201) for {'C': 100.0, 'kernel': 'linear'}
0.584 (+/-0.186) for {'C': 1000.0, 'kernel': 'linear'}
0.577 (+/-0.192) for {'C': 10000.0, 'kernel': 'linear'}
0.576 (+/-0.191) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      1.00       623

avg / total       0.98      0.99      0.99       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-09}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-08}
0.699 (+/-0.308) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-07}
0.698 (+/-0.306) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-06}
0.695 (+/-0.300) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-05}
0.661 (+/-0.318) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.0001}
0.659 (+/-0.313) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-09}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-08}
0.699 (+/-0.308) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-07}
0.698 (+/-0.306) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-06}
0.695 (+/-0.300) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-05}
0.660 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.0001}
0.658 (+/-0.313) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-09}
0.617 (+/-0.238) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-08}
0.698 (+/-0.307) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-07}
0.697 (+/-0.304) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-06}
0.679 (+/-0.288) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-05}
0.659 (+/-0.314) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.0001}
0.658 (+/-0.313) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-09}
0.617 (+/-0.238) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-08}
0.699 (+/-0.308) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-07}
0.697 (+/-0.304) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-06}
0.662 (+/-0.310) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-05}
0.659 (+/-0.313) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 0.0001}
0.633 (+/-0.324) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-09}
0.642 (+/-0.236) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-08}
0.697 (+/-0.303) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-07}
0.697 (+/-0.304) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-06}
0.660 (+/-0.309) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-05}
0.658 (+/-0.311) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 0.0001}
0.632 (+/-0.324) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-09}
0.683 (+/-0.296) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-08}
0.683 (+/-0.258) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-07}
0.696 (+/-0.303) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-06}
0.660 (+/-0.310) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-05}
0.658 (+/-0.311) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 0.0001}
0.632 (+/-0.323) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-09}
0.683 (+/-0.296) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-08}
0.660 (+/-0.221) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-07}
0.695 (+/-0.302) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-06}
0.660 (+/-0.310) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-05}
0.657 (+/-0.310) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 0.0001}
0.633 (+/-0.322) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-10}
0.617 (+/-0.238) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-09}
0.700 (+/-0.308) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-08}
0.656 (+/-0.219) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-07}
0.695 (+/-0.303) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-06}
0.660 (+/-0.310) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-05}
0.657 (+/-0.309) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.0001}
0.632 (+/-0.322) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-10}
0.617 (+/-0.238) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-09}
0.699 (+/-0.307) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-08}
0.653 (+/-0.218) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-07}
0.695 (+/-0.303) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-06}
0.659 (+/-0.309) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-05}
0.657 (+/-0.309) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 0.0001}
0.633 (+/-0.322) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-10}
0.642 (+/-0.236) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-09}
0.699 (+/-0.307) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-08}
0.651 (+/-0.219) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-07}
0.695 (+/-0.302) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-06}
0.659 (+/-0.310) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-05}
0.657 (+/-0.308) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 0.0001}
0.633 (+/-0.322) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-10}
0.683 (+/-0.296) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-09}
0.699 (+/-0.307) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-08}
0.633 (+/-0.237) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-07}
0.695 (+/-0.302) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-06}
0.659 (+/-0.310) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-05}
0.657 (+/-0.309) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 0.0001}
0.633 (+/-0.322) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 1.0, 'kernel': 'linear'}
0.696 (+/-0.302) for {'C': 10.0, 'kernel': 'linear'}
0.663 (+/-0.315) for {'C': 100.0, 'kernel': 'linear'}
0.660 (+/-0.314) for {'C': 1000.0, 'kernel': 'linear'}
0.635 (+/-0.324) for {'C': 10000.0, 'kernel': 'linear'}
0.634 (+/-0.325) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-08}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6995187154753071
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-09}
0.647 (+/-0.460) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-08}
0.656 (+/-0.231) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-07}
0.622 (+/-0.197) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-06}
0.584 (+/-0.154) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-05}
0.575 (+/-0.187) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.0001}
0.580 (+/-0.186) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-09}
0.747 (+/-0.502) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-08}
0.640 (+/-0.209) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-07}
0.614 (+/-0.292) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-06}
0.590 (+/-0.158) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-05}
0.575 (+/-0.187) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.0001}
0.579 (+/-0.186) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-09}
0.747 (+/-0.502) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-08}
0.626 (+/-0.188) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-07}
0.593 (+/-0.280) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-06}
0.600 (+/-0.182) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-05}
0.574 (+/-0.186) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.0001}
0.579 (+/-0.186) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-09}
0.848 (+/-0.459) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-08}
0.600 (+/-0.216) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-07}
0.591 (+/-0.283) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-06}
0.588 (+/-0.194) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-05}
0.576 (+/-0.186) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 0.0001}
0.575 (+/-0.191) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-09}
0.848 (+/-0.460) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-08}
0.555 (+/-0.143) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-07}
0.592 (+/-0.280) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-06}
0.584 (+/-0.189) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-05}
0.576 (+/-0.186) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 0.0001}
0.574 (+/-0.192) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-10}
0.647 (+/-0.460) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-09}
0.848 (+/-0.460) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-08}
0.543 (+/-0.144) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-07}
0.593 (+/-0.280) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-06}
0.583 (+/-0.189) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-05}
0.577 (+/-0.186) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 0.0001}
0.573 (+/-0.192) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-10}
0.747 (+/-0.502) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-09}
0.806 (+/-0.436) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-08}
0.536 (+/-0.144) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-07}
0.608 (+/-0.287) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-06}
0.583 (+/-0.189) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-05}
0.577 (+/-0.186) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 0.0001}
0.573 (+/-0.192) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-10}
0.747 (+/-0.502) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-09}
0.798 (+/-0.437) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-08}
0.529 (+/-0.146) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-07}
0.609 (+/-0.286) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-06}
0.580 (+/-0.188) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-05}
0.577 (+/-0.186) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.0001}
0.573 (+/-0.192) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-10}
0.848 (+/-0.459) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-09}
0.793 (+/-0.440) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-08}
0.554 (+/-0.296) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-07}
0.605 (+/-0.283) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-06}
0.568 (+/-0.155) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-05}
0.577 (+/-0.186) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 0.0001}
0.573 (+/-0.192) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-10}
0.848 (+/-0.460) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-09}
0.793 (+/-0.440) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-08}
0.554 (+/-0.296) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-07}
0.605 (+/-0.283) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-06}
0.568 (+/-0.155) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-05}
0.578 (+/-0.186) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 0.0001}
0.574 (+/-0.191) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-11}
0.647 (+/-0.460) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-10}
0.848 (+/-0.460) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-09}
0.793 (+/-0.440) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-08}
0.553 (+/-0.296) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-07}
0.609 (+/-0.286) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-06}
0.568 (+/-0.155) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-05}
0.578 (+/-0.186) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 0.0001}
0.574 (+/-0.191) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.798 (+/-0.437) for {'C': 0.1, 'kernel': 'linear'}
0.660 (+/-0.219) for {'C': 1.0, 'kernel': 'linear'}
0.585 (+/-0.147) for {'C': 10.0, 'kernel': 'linear'}
0.582 (+/-0.190) for {'C': 100.0, 'kernel': 'linear'}
0.581 (+/-0.185) for {'C': 1000.0, 'kernel': 'linear'}
0.576 (+/-0.191) for {'C': 10000.0, 'kernel': 'linear'}
0.575 (+/-0.191) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-09}
0.575 (+/-0.229) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-08}
0.698 (+/-0.308) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-07}
0.697 (+/-0.303) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-06}
0.693 (+/-0.303) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-05}
0.656 (+/-0.311) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.0001}
0.658 (+/-0.312) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-09}
0.617 (+/-0.238) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-08}
0.697 (+/-0.307) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-07}
0.694 (+/-0.297) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-06}
0.716 (+/-0.353) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-05}
0.656 (+/-0.311) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.0001}
0.658 (+/-0.313) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-09}
0.617 (+/-0.238) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-08}
0.697 (+/-0.308) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-07}
0.692 (+/-0.297) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-06}
0.699 (+/-0.344) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-05}
0.657 (+/-0.310) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.0001}
0.658 (+/-0.313) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-09}
0.658 (+/-0.217) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-08}
0.692 (+/-0.303) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-07}
0.691 (+/-0.296) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-06}
0.681 (+/-0.365) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-05}
0.657 (+/-0.310) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 0.0001}
0.633 (+/-0.324) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-09}
0.683 (+/-0.296) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-08}
0.678 (+/-0.277) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-07}
0.692 (+/-0.298) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-06}
0.681 (+/-0.364) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-05}
0.657 (+/-0.309) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 0.0001}
0.632 (+/-0.324) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-10}
0.575 (+/-0.229) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-09}
0.700 (+/-0.309) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-08}
0.656 (+/-0.248) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-07}
0.692 (+/-0.299) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-06}
0.680 (+/-0.364) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-05}
0.657 (+/-0.310) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 0.0001}
0.632 (+/-0.323) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-10}
0.617 (+/-0.238) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-09}
0.700 (+/-0.308) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-08}
0.652 (+/-0.183) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-07}
0.693 (+/-0.304) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-06}
0.680 (+/-0.364) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-05}
0.657 (+/-0.310) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 0.0001}
0.633 (+/-0.322) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-10}
0.617 (+/-0.238) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-09}
0.699 (+/-0.307) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-08}
0.612 (+/-0.143) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-07}
0.694 (+/-0.304) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-06}
0.679 (+/-0.364) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-05}
0.657 (+/-0.309) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.0001}
0.632 (+/-0.322) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-10}
0.658 (+/-0.217) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-09}
0.699 (+/-0.307) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-08}
0.616 (+/-0.170) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-07}
0.693 (+/-0.303) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-06}
0.679 (+/-0.363) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-05}
0.657 (+/-0.309) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 0.0001}
0.633 (+/-0.322) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-10}
0.683 (+/-0.296) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-09}
0.699 (+/-0.307) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-08}
0.615 (+/-0.177) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-07}
0.693 (+/-0.303) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-06}
0.679 (+/-0.363) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-05}
0.657 (+/-0.308) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 0.0001}
0.633 (+/-0.322) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-11}
0.575 (+/-0.229) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-10}
0.700 (+/-0.309) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-09}
0.699 (+/-0.307) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-08}
0.610 (+/-0.170) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-07}
0.694 (+/-0.304) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-06}
0.679 (+/-0.363) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-05}
0.657 (+/-0.309) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 0.0001}
0.633 (+/-0.322) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.700 (+/-0.309) for {'C': 0.1, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 1.0, 'kernel': 'linear'}
0.694 (+/-0.301) for {'C': 10.0, 'kernel': 'linear'}
0.657 (+/-0.313) for {'C': 100.0, 'kernel': 'linear'}
0.659 (+/-0.314) for {'C': 1000.0, 'kernel': 'linear'}
0.634 (+/-0.324) for {'C': 10000.0, 'kernel': 'linear'}
0.633 (+/-0.324) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.04      0.17      0.06         6
          1       0.99      0.96      0.98       623

avg / total       0.98      0.95      0.97       629

本轮grid search结果，得到最好的参数选择是： {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-05}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.7161833209662792
循环迭代之前，delta is: [8.41510681e+06 9.99000000e-06]
执行tunemodel()函数前，使用的grid search parameter是：
 [{'C': array([1000000.        , 1096478.19614319, 1202264.43461741,
       1318256.73855641, 1445439.77074593, 1584893.19246111,
       1737800.82874938, 1905460.71796325, 2089296.13085404,
       2290867.65276777, 2511886.43150958]), 'kernel': ['rbf'], 'gamma': array([1.00000000e-06, 1.58489319e-06, 2.51188643e-06, 3.98107171e-06,
       6.30957344e-06, 1.00000000e-05, 1.58489319e-05, 2.51188643e-05,
       3.98107171e-05, 6.30957344e-05, 1.00000000e-04])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.660 (+/-0.219) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.693 (+/-0.350) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.635 (+/-0.198) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.620 (+/-0.183) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.593 (+/-0.154) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.602 (+/-0.182) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.594 (+/-0.159) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.608 (+/-0.177) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.595 (+/-0.191) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.591 (+/-0.187) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.597 (+/-0.211) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.660 (+/-0.219) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.668 (+/-0.290) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.635 (+/-0.198) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.610 (+/-0.179) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.590 (+/-0.154) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.602 (+/-0.182) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.594 (+/-0.159) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.610 (+/-0.180) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.598 (+/-0.192) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.590 (+/-0.186) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.591 (+/-0.198) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.660 (+/-0.219) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.643 (+/-0.202) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.633 (+/-0.200) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.606 (+/-0.180) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.596 (+/-0.177) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.594 (+/-0.161) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.598 (+/-0.164) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.598 (+/-0.192) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.595 (+/-0.189) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.588 (+/-0.186) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.588 (+/-0.192) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.676 (+/-0.294) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.668 (+/-0.290) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.633 (+/-0.200) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.604 (+/-0.181) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.596 (+/-0.177) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.593 (+/-0.162) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.599 (+/-0.164) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.609 (+/-0.201) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.591 (+/-0.187) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.590 (+/-0.187) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.586 (+/-0.188) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.672 (+/-0.295) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.668 (+/-0.290) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.653 (+/-0.288) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.594 (+/-0.160) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.596 (+/-0.177) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.598 (+/-0.164) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.608 (+/-0.183) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.609 (+/-0.201) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.588 (+/-0.186) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.590 (+/-0.187) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.586 (+/-0.188) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.672 (+/-0.295) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.643 (+/-0.202) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.622 (+/-0.185) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.593 (+/-0.159) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.597 (+/-0.177) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.598 (+/-0.164) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.604 (+/-0.180) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.610 (+/-0.209) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.588 (+/-0.186) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.588 (+/-0.188) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.586 (+/-0.188) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.664 (+/-0.291) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.643 (+/-0.202) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.621 (+/-0.186) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.594 (+/-0.160) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.597 (+/-0.177) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.600 (+/-0.164) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.602 (+/-0.180) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.608 (+/-0.209) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.587 (+/-0.187) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.584 (+/-0.186) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.583 (+/-0.187) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.664 (+/-0.291) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.664 (+/-0.292) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.618 (+/-0.184) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.595 (+/-0.160) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.596 (+/-0.177) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.600 (+/-0.164) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.590 (+/-0.191) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.606 (+/-0.210) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.587 (+/-0.187) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.583 (+/-0.187) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.581 (+/-0.188) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.653 (+/-0.286) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.664 (+/-0.292) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.641 (+/-0.287) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.595 (+/-0.160) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.600 (+/-0.176) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.608 (+/-0.183) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.590 (+/-0.190) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.603 (+/-0.210) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.586 (+/-0.186) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.583 (+/-0.187) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.581 (+/-0.188) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.655 (+/-0.286) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.664 (+/-0.292) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.637 (+/-0.289) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.591 (+/-0.154) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.600 (+/-0.176) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.608 (+/-0.184) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.592 (+/-0.190) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.608 (+/-0.213) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.587 (+/-0.187) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.583 (+/-0.187) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.579 (+/-0.187) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.653 (+/-0.286) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.664 (+/-0.292) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.606 (+/-0.181) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.589 (+/-0.153) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.593 (+/-0.153) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.611 (+/-0.183) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.594 (+/-0.191) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.601 (+/-0.202) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.584 (+/-0.186) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.583 (+/-0.188) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.579 (+/-0.187) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.685 (+/-0.297) for {'C': 1.0, 'kernel': 'linear'}
0.604 (+/-0.181) for {'C': 10.0, 'kernel': 'linear'}
0.609 (+/-0.201) for {'C': 100.0, 'kernel': 'linear'}
0.584 (+/-0.186) for {'C': 1000.0, 'kernel': 'linear'}
0.577 (+/-0.192) for {'C': 10000.0, 'kernel': 'linear'}
0.576 (+/-0.191) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.698 (+/-0.306) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.698 (+/-0.306) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.698 (+/-0.305) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.697 (+/-0.304) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.695 (+/-0.302) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.695 (+/-0.300) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.695 (+/-0.300) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.679 (+/-0.293) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.662 (+/-0.314) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.661 (+/-0.314) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.661 (+/-0.318) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.698 (+/-0.306) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.698 (+/-0.306) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.698 (+/-0.305) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.696 (+/-0.303) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.695 (+/-0.301) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.695 (+/-0.300) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.695 (+/-0.300) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.679 (+/-0.294) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.663 (+/-0.314) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.661 (+/-0.314) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.660 (+/-0.317) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.698 (+/-0.306) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.698 (+/-0.306) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.697 (+/-0.305) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.696 (+/-0.302) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.695 (+/-0.300) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.695 (+/-0.300) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.695 (+/-0.300) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.663 (+/-0.314) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.662 (+/-0.314) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.661 (+/-0.314) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.660 (+/-0.316) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.698 (+/-0.306) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.698 (+/-0.306) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.697 (+/-0.305) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.695 (+/-0.302) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.694 (+/-0.299) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.694 (+/-0.300) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.695 (+/-0.301) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.663 (+/-0.315) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.662 (+/-0.312) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.661 (+/-0.315) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.660 (+/-0.316) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.698 (+/-0.306) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.698 (+/-0.306) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.697 (+/-0.305) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.695 (+/-0.302) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.694 (+/-0.299) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.695 (+/-0.300) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.695 (+/-0.301) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.663 (+/-0.315) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.662 (+/-0.313) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.661 (+/-0.315) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.660 (+/-0.316) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.698 (+/-0.306) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.698 (+/-0.306) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.697 (+/-0.304) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.695 (+/-0.302) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.695 (+/-0.299) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.695 (+/-0.300) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.679 (+/-0.289) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.663 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.661 (+/-0.313) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.661 (+/-0.315) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.660 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.698 (+/-0.306) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.698 (+/-0.306) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.697 (+/-0.304) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.695 (+/-0.302) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.694 (+/-0.298) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.695 (+/-0.300) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.678 (+/-0.288) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.663 (+/-0.316) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.661 (+/-0.313) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.660 (+/-0.314) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.660 (+/-0.315) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.698 (+/-0.306) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.698 (+/-0.306) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.697 (+/-0.304) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.695 (+/-0.302) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.694 (+/-0.298) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.695 (+/-0.300) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.662 (+/-0.308) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.663 (+/-0.316) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.661 (+/-0.313) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.660 (+/-0.315) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.659 (+/-0.315) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.697 (+/-0.304) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.698 (+/-0.306) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.696 (+/-0.304) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.695 (+/-0.302) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.695 (+/-0.299) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.695 (+/-0.301) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.662 (+/-0.309) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.662 (+/-0.316) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.661 (+/-0.313) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.660 (+/-0.315) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.659 (+/-0.315) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.698 (+/-0.305) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.698 (+/-0.306) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.696 (+/-0.303) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.695 (+/-0.302) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.695 (+/-0.301) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.695 (+/-0.300) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.662 (+/-0.309) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.662 (+/-0.317) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.661 (+/-0.314) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.659 (+/-0.315) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.658 (+/-0.314) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.697 (+/-0.304) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.698 (+/-0.306) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.695 (+/-0.302) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.695 (+/-0.301) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.695 (+/-0.302) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.679 (+/-0.288) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.662 (+/-0.309) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.662 (+/-0.317) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.660 (+/-0.313) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.659 (+/-0.315) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.659 (+/-0.314) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 1.0, 'kernel': 'linear'}
0.696 (+/-0.302) for {'C': 10.0, 'kernel': 'linear'}
0.663 (+/-0.315) for {'C': 100.0, 'kernel': 'linear'}
0.660 (+/-0.314) for {'C': 1000.0, 'kernel': 'linear'}
0.635 (+/-0.324) for {'C': 10000.0, 'kernel': 'linear'}
0.634 (+/-0.325) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6983958900156649
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.622 (+/-0.197) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.643 (+/-0.365) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.584 (+/-0.154) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.577 (+/-0.142) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.583 (+/-0.146) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.584 (+/-0.154) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.578 (+/-0.156) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.594 (+/-0.178) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.579 (+/-0.189) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.579 (+/-0.189) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.575 (+/-0.187) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.619 (+/-0.199) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.615 (+/-0.293) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.584 (+/-0.154) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.577 (+/-0.142) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.581 (+/-0.146) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.584 (+/-0.154) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.582 (+/-0.163) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.594 (+/-0.178) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.581 (+/-0.191) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.579 (+/-0.189) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.575 (+/-0.187) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.611 (+/-0.198) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.591 (+/-0.179) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.584 (+/-0.154) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.577 (+/-0.142) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.580 (+/-0.145) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.587 (+/-0.154) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.585 (+/-0.160) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.581 (+/-0.186) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.581 (+/-0.191) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.579 (+/-0.189) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.575 (+/-0.187) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.625 (+/-0.291) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.588 (+/-0.179) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.584 (+/-0.154) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.577 (+/-0.142) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.580 (+/-0.145) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.586 (+/-0.154) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.586 (+/-0.161) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.589 (+/-0.193) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.582 (+/-0.190) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.579 (+/-0.189) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.575 (+/-0.187) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.620 (+/-0.291) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.585 (+/-0.178) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.579 (+/-0.147) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.578 (+/-0.143) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.579 (+/-0.145) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.591 (+/-0.158) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.594 (+/-0.183) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.589 (+/-0.193) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.580 (+/-0.189) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.579 (+/-0.189) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.575 (+/-0.187) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.614 (+/-0.292) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.582 (+/-0.179) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.579 (+/-0.147) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.579 (+/-0.144) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.580 (+/-0.145) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.590 (+/-0.158) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.590 (+/-0.178) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.585 (+/-0.188) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.580 (+/-0.189) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.576 (+/-0.187) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.575 (+/-0.187) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.604 (+/-0.282) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.583 (+/-0.178) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.580 (+/-0.147) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.582 (+/-0.146) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.580 (+/-0.146) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.591 (+/-0.158) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.589 (+/-0.179) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.582 (+/-0.191) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.580 (+/-0.189) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.576 (+/-0.187) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.575 (+/-0.186) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.603 (+/-0.282) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.582 (+/-0.179) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.582 (+/-0.147) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.583 (+/-0.146) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.578 (+/-0.146) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.591 (+/-0.158) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.576 (+/-0.186) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.582 (+/-0.191) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.579 (+/-0.189) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.575 (+/-0.187) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.574 (+/-0.186) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.601 (+/-0.283) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.583 (+/-0.178) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.586 (+/-0.154) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.588 (+/-0.152) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.581 (+/-0.147) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.598 (+/-0.181) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.577 (+/-0.185) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.579 (+/-0.189) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.579 (+/-0.189) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.575 (+/-0.186) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.574 (+/-0.186) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.598 (+/-0.280) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.583 (+/-0.178) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.586 (+/-0.154) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.588 (+/-0.152) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.579 (+/-0.147) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.598 (+/-0.181) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.578 (+/-0.186) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.583 (+/-0.195) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.579 (+/-0.189) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.575 (+/-0.186) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.574 (+/-0.186) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.593 (+/-0.280) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.585 (+/-0.178) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.587 (+/-0.154) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.586 (+/-0.151) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.578 (+/-0.147) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.600 (+/-0.182) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.582 (+/-0.195) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.583 (+/-0.195) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.577 (+/-0.187) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.575 (+/-0.187) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.574 (+/-0.186) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.798 (+/-0.437) for {'C': 0.1, 'kernel': 'linear'}
0.660 (+/-0.219) for {'C': 1.0, 'kernel': 'linear'}
0.585 (+/-0.147) for {'C': 10.0, 'kernel': 'linear'}
0.582 (+/-0.190) for {'C': 100.0, 'kernel': 'linear'}
0.581 (+/-0.185) for {'C': 1000.0, 'kernel': 'linear'}
0.576 (+/-0.191) for {'C': 10000.0, 'kernel': 'linear'}
0.575 (+/-0.191) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.697 (+/-0.303) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.695 (+/-0.299) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.694 (+/-0.299) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.693 (+/-0.300) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.694 (+/-0.304) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.693 (+/-0.303) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.690 (+/-0.302) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.699 (+/-0.344) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.656 (+/-0.311) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.656 (+/-0.311) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.656 (+/-0.311) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.696 (+/-0.301) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.694 (+/-0.297) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.694 (+/-0.299) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.693 (+/-0.300) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.693 (+/-0.303) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.692 (+/-0.304) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.690 (+/-0.303) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.699 (+/-0.344) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.656 (+/-0.311) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.656 (+/-0.312) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.656 (+/-0.311) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.696 (+/-0.300) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.693 (+/-0.297) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.694 (+/-0.299) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.693 (+/-0.300) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.693 (+/-0.303) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.717 (+/-0.352) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.714 (+/-0.349) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.682 (+/-0.365) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.656 (+/-0.312) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.656 (+/-0.312) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.656 (+/-0.311) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.695 (+/-0.299) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.693 (+/-0.296) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.694 (+/-0.299) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.693 (+/-0.300) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.693 (+/-0.304) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.717 (+/-0.351) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.714 (+/-0.349) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.682 (+/-0.365) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.656 (+/-0.313) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.656 (+/-0.312) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.656 (+/-0.311) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.695 (+/-0.298) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.692 (+/-0.295) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.693 (+/-0.299) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.693 (+/-0.301) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.693 (+/-0.303) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.717 (+/-0.352) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.714 (+/-0.349) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.682 (+/-0.366) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.656 (+/-0.314) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.656 (+/-0.312) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.656 (+/-0.311) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.694 (+/-0.297) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.692 (+/-0.296) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.693 (+/-0.299) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.693 (+/-0.301) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.693 (+/-0.303) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.716 (+/-0.353) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.697 (+/-0.342) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.681 (+/-0.365) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.656 (+/-0.313) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.656 (+/-0.311) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.656 (+/-0.311) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.693 (+/-0.296) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.692 (+/-0.296) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.693 (+/-0.300) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.694 (+/-0.303) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.693 (+/-0.303) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.716 (+/-0.352) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.697 (+/-0.342) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.657 (+/-0.312) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.656 (+/-0.313) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.656 (+/-0.311) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.656 (+/-0.311) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.693 (+/-0.296) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.692 (+/-0.296) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.694 (+/-0.301) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.694 (+/-0.303) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.692 (+/-0.303) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.716 (+/-0.352) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.680 (+/-0.362) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.656 (+/-0.312) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.656 (+/-0.313) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.656 (+/-0.311) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.656 (+/-0.311) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.693 (+/-0.296) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.692 (+/-0.297) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.694 (+/-0.302) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.694 (+/-0.303) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.692 (+/-0.303) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.716 (+/-0.351) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.680 (+/-0.364) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.656 (+/-0.312) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.656 (+/-0.312) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.656 (+/-0.310) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.656 (+/-0.311) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.693 (+/-0.297) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.692 (+/-0.297) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.694 (+/-0.302) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.694 (+/-0.304) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.692 (+/-0.304) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.716 (+/-0.351) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.680 (+/-0.364) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.656 (+/-0.312) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.656 (+/-0.312) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.656 (+/-0.310) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.656 (+/-0.310) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.692 (+/-0.297) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.693 (+/-0.297) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.694 (+/-0.303) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.693 (+/-0.304) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.691 (+/-0.304) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.699 (+/-0.344) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.655 (+/-0.310) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.656 (+/-0.312) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.656 (+/-0.312) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.656 (+/-0.310) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.657 (+/-0.310) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.700 (+/-0.309) for {'C': 0.1, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 1.0, 'kernel': 'linear'}
0.694 (+/-0.301) for {'C': 10.0, 'kernel': 'linear'}
0.657 (+/-0.313) for {'C': 100.0, 'kernel': 'linear'}
0.659 (+/-0.314) for {'C': 1000.0, 'kernel': 'linear'}
0.634 (+/-0.324) for {'C': 10000.0, 'kernel': 'linear'}
0.633 (+/-0.324) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.04      0.17      0.06         6
          1       0.99      0.96      0.98       623

avg / total       0.98      0.95      0.97       629

本轮grid search结果，得到最好的参数选择是： {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.717144344133894
第0轮loop中，tunemodel函数得到的最优参数是： ([{'C': array([1096478.19614319, 1116863.24778056, 1137627.28582343,
       1158777.35615513, 1180320.63565173, 1202264.43461741,
       1224616.19926505, 1247383.51424294, 1270574.10520854,
       1294195.84144999, 1318256.73855641]), 'kernel': ['rbf'], 'gamma': array([6.30957344e-06, 6.91830971e-06, 7.58577575e-06, 8.31763771e-06,
       9.12010839e-06, 1.00000000e-05, 1.09647820e-05, 1.20226443e-05,
       1.31825674e-05, 1.44543977e-05, 1.58489319e-05])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}], {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}, 0.717144344133894)
这是第0次迭代微调C和gamma。
第0次迭代，得到delta： [3.82628758e+05 5.08219768e-21]
执行tunemodel()函数前，使用的grid search parameter是：
 [{'C': array([1096478.19614319, 1116863.24778056, 1137627.28582343,
       1158777.35615513, 1180320.63565173, 1202264.43461741,
       1224616.19926505, 1247383.51424294, 1270574.10520854,
       1294195.84144999, 1318256.73855641]), 'kernel': ['rbf'], 'gamma': array([6.30957344e-06, 6.91830971e-06, 7.58577575e-06, 8.31763771e-06,
       9.12010839e-06, 1.00000000e-05, 1.09647820e-05, 1.20226443e-05,
       1.31825674e-05, 1.44543977e-05, 1.58489319e-05])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.590 (+/-0.154) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.601 (+/-0.181) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.603 (+/-0.182) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.593 (+/-0.157) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 8.317637711026698e-06}
0.604 (+/-0.182) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.602 (+/-0.182) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.590 (+/-0.154) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.0964781961431852e-05}
0.599 (+/-0.164) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.2022644346174139e-05}
0.597 (+/-0.158) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.3182567385564057e-05}
0.601 (+/-0.159) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.4454397707459268e-05}
0.594 (+/-0.159) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.590 (+/-0.154) for {'C': 1116863.2477805612, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.601 (+/-0.181) for {'C': 1116863.2477805612, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.595 (+/-0.161) for {'C': 1116863.2477805612, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.593 (+/-0.157) for {'C': 1116863.2477805612, 'kernel': 'rbf', 'gamma': 8.317637711026698e-06}
0.604 (+/-0.182) for {'C': 1116863.2477805612, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.602 (+/-0.182) for {'C': 1116863.2477805612, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.590 (+/-0.154) for {'C': 1116863.2477805612, 'kernel': 'rbf', 'gamma': 1.0964781961431852e-05}
0.600 (+/-0.164) for {'C': 1116863.2477805612, 'kernel': 'rbf', 'gamma': 1.2022644346174139e-05}
0.597 (+/-0.158) for {'C': 1116863.2477805612, 'kernel': 'rbf', 'gamma': 1.3182567385564057e-05}
0.599 (+/-0.158) for {'C': 1116863.2477805612, 'kernel': 'rbf', 'gamma': 1.4454397707459268e-05}
0.594 (+/-0.159) for {'C': 1116863.2477805612, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.588 (+/-0.154) for {'C': 1137627.2858234306, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.601 (+/-0.181) for {'C': 1137627.2858234306, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.595 (+/-0.161) for {'C': 1137627.2858234306, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.593 (+/-0.157) for {'C': 1137627.2858234306, 'kernel': 'rbf', 'gamma': 8.317637711026698e-06}
0.602 (+/-0.181) for {'C': 1137627.2858234306, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.602 (+/-0.182) for {'C': 1137627.2858234306, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.590 (+/-0.154) for {'C': 1137627.2858234306, 'kernel': 'rbf', 'gamma': 1.0964781961431852e-05}
0.600 (+/-0.164) for {'C': 1137627.2858234306, 'kernel': 'rbf', 'gamma': 1.2022644346174139e-05}
0.597 (+/-0.158) for {'C': 1137627.2858234306, 'kernel': 'rbf', 'gamma': 1.3182567385564057e-05}
0.599 (+/-0.158) for {'C': 1137627.2858234306, 'kernel': 'rbf', 'gamma': 1.4454397707459268e-05}
0.598 (+/-0.164) for {'C': 1137627.2858234306, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.588 (+/-0.154) for {'C': 1158777.3561551254, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.601 (+/-0.181) for {'C': 1158777.3561551254, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.595 (+/-0.161) for {'C': 1158777.3561551254, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.589 (+/-0.152) for {'C': 1158777.3561551254, 'kernel': 'rbf', 'gamma': 8.317637711026698e-06}
0.606 (+/-0.183) for {'C': 1158777.3561551254, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.594 (+/-0.161) for {'C': 1158777.3561551254, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.590 (+/-0.154) for {'C': 1158777.3561551254, 'kernel': 'rbf', 'gamma': 1.0964781961431852e-05}
0.600 (+/-0.164) for {'C': 1158777.3561551254, 'kernel': 'rbf', 'gamma': 1.2022644346174139e-05}
0.597 (+/-0.158) for {'C': 1158777.3561551254, 'kernel': 'rbf', 'gamma': 1.3182567385564057e-05}
0.599 (+/-0.158) for {'C': 1158777.3561551254, 'kernel': 'rbf', 'gamma': 1.4454397707459268e-05}
0.598 (+/-0.164) for {'C': 1158777.3561551254, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.588 (+/-0.154) for {'C': 1180320.6356517284, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.601 (+/-0.181) for {'C': 1180320.6356517284, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.595 (+/-0.161) for {'C': 1180320.6356517284, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.587 (+/-0.150) for {'C': 1180320.6356517284, 'kernel': 'rbf', 'gamma': 8.317637711026698e-06}
0.602 (+/-0.181) for {'C': 1180320.6356517284, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.594 (+/-0.161) for {'C': 1180320.6356517284, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.590 (+/-0.154) for {'C': 1180320.6356517284, 'kernel': 'rbf', 'gamma': 1.0964781961431852e-05}
0.603 (+/-0.164) for {'C': 1180320.6356517284, 'kernel': 'rbf', 'gamma': 1.2022644346174139e-05}
0.597 (+/-0.158) for {'C': 1180320.6356517284, 'kernel': 'rbf', 'gamma': 1.3182567385564057e-05}
0.599 (+/-0.158) for {'C': 1180320.6356517284, 'kernel': 'rbf', 'gamma': 1.4454397707459268e-05}
0.598 (+/-0.164) for {'C': 1180320.6356517284, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.596 (+/-0.177) for {'C': 1202264.4346174113, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.601 (+/-0.181) for {'C': 1202264.4346174113, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.592 (+/-0.155) for {'C': 1202264.4346174113, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.587 (+/-0.150) for {'C': 1202264.4346174113, 'kernel': 'rbf', 'gamma': 8.317637711026698e-06}
0.606 (+/-0.183) for {'C': 1202264.4346174113, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.594 (+/-0.161) for {'C': 1202264.4346174113, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.590 (+/-0.154) for {'C': 1202264.4346174113, 'kernel': 'rbf', 'gamma': 1.0964781961431852e-05}
0.603 (+/-0.164) for {'C': 1202264.4346174113, 'kernel': 'rbf', 'gamma': 1.2022644346174139e-05}
0.597 (+/-0.158) for {'C': 1202264.4346174113, 'kernel': 'rbf', 'gamma': 1.3182567385564057e-05}
0.599 (+/-0.158) for {'C': 1202264.4346174113, 'kernel': 'rbf', 'gamma': 1.4454397707459268e-05}
0.598 (+/-0.164) for {'C': 1202264.4346174113, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.596 (+/-0.177) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.600 (+/-0.181) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.592 (+/-0.155) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.591 (+/-0.156) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 8.317637711026698e-06}
0.607 (+/-0.182) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.594 (+/-0.161) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.590 (+/-0.154) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.0964781961431852e-05}
0.603 (+/-0.164) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.2022644346174139e-05}
0.597 (+/-0.158) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.3182567385564057e-05}
0.599 (+/-0.158) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.4454397707459268e-05}
0.598 (+/-0.164) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.596 (+/-0.177) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.601 (+/-0.181) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.604 (+/-0.182) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.591 (+/-0.156) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 8.317637711026698e-06}
0.607 (+/-0.182) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.594 (+/-0.161) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.590 (+/-0.154) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.0964781961431852e-05}
0.601 (+/-0.165) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.2022644346174139e-05}
0.597 (+/-0.158) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.3182567385564057e-05}
0.599 (+/-0.158) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.4454397707459268e-05}
0.598 (+/-0.164) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.596 (+/-0.177) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.603 (+/-0.182) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.595 (+/-0.161) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.593 (+/-0.155) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 8.317637711026698e-06}
0.607 (+/-0.182) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.594 (+/-0.161) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.590 (+/-0.154) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.0964781961431852e-05}
0.603 (+/-0.164) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.2022644346174139e-05}
0.597 (+/-0.158) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.3182567385564057e-05}
0.599 (+/-0.158) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.4454397707459268e-05}
0.598 (+/-0.164) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.596 (+/-0.177) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.603 (+/-0.182) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.591 (+/-0.156) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.597 (+/-0.161) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 8.317637711026698e-06}
0.607 (+/-0.182) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.593 (+/-0.162) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.590 (+/-0.154) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 1.0964781961431852e-05}
0.604 (+/-0.164) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 1.2022644346174139e-05}
0.601 (+/-0.163) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 1.3182567385564057e-05}
0.599 (+/-0.158) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 1.4454397707459268e-05}
0.598 (+/-0.164) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.596 (+/-0.177) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.601 (+/-0.181) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.592 (+/-0.156) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.597 (+/-0.161) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 8.317637711026698e-06}
0.607 (+/-0.182) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.593 (+/-0.162) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.590 (+/-0.154) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.0964781961431852e-05}
0.604 (+/-0.164) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.2022644346174139e-05}
0.601 (+/-0.163) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.3182567385564057e-05}
0.599 (+/-0.158) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.4454397707459268e-05}
0.599 (+/-0.164) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.685 (+/-0.297) for {'C': 1.0, 'kernel': 'linear'}
0.604 (+/-0.181) for {'C': 10.0, 'kernel': 'linear'}
0.609 (+/-0.201) for {'C': 100.0, 'kernel': 'linear'}
0.584 (+/-0.186) for {'C': 1000.0, 'kernel': 'linear'}
0.577 (+/-0.192) for {'C': 10000.0, 'kernel': 'linear'}
0.576 (+/-0.191) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.695 (+/-0.301) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.695 (+/-0.302) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.695 (+/-0.301) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.695 (+/-0.303) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 8.317637711026698e-06}
0.695 (+/-0.303) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.695 (+/-0.300) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.695 (+/-0.300) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.0964781961431852e-05}
0.695 (+/-0.301) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.2022644346174139e-05}
0.695 (+/-0.302) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.3182567385564057e-05}
0.696 (+/-0.302) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.4454397707459268e-05}
0.695 (+/-0.300) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.695 (+/-0.301) for {'C': 1116863.2477805612, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.695 (+/-0.302) for {'C': 1116863.2477805612, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.695 (+/-0.301) for {'C': 1116863.2477805612, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.695 (+/-0.303) for {'C': 1116863.2477805612, 'kernel': 'rbf', 'gamma': 8.317637711026698e-06}
0.695 (+/-0.303) for {'C': 1116863.2477805612, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.695 (+/-0.300) for {'C': 1116863.2477805612, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.695 (+/-0.300) for {'C': 1116863.2477805612, 'kernel': 'rbf', 'gamma': 1.0964781961431852e-05}
0.696 (+/-0.302) for {'C': 1116863.2477805612, 'kernel': 'rbf', 'gamma': 1.2022644346174139e-05}
0.695 (+/-0.302) for {'C': 1116863.2477805612, 'kernel': 'rbf', 'gamma': 1.3182567385564057e-05}
0.696 (+/-0.302) for {'C': 1116863.2477805612, 'kernel': 'rbf', 'gamma': 1.4454397707459268e-05}
0.695 (+/-0.300) for {'C': 1116863.2477805612, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.694 (+/-0.299) for {'C': 1137627.2858234306, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.695 (+/-0.302) for {'C': 1137627.2858234306, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.695 (+/-0.301) for {'C': 1137627.2858234306, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.695 (+/-0.303) for {'C': 1137627.2858234306, 'kernel': 'rbf', 'gamma': 8.317637711026698e-06}
0.695 (+/-0.302) for {'C': 1137627.2858234306, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.695 (+/-0.300) for {'C': 1137627.2858234306, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.695 (+/-0.300) for {'C': 1137627.2858234306, 'kernel': 'rbf', 'gamma': 1.0964781961431852e-05}
0.696 (+/-0.302) for {'C': 1137627.2858234306, 'kernel': 'rbf', 'gamma': 1.2022644346174139e-05}
0.695 (+/-0.303) for {'C': 1137627.2858234306, 'kernel': 'rbf', 'gamma': 1.3182567385564057e-05}
0.696 (+/-0.302) for {'C': 1137627.2858234306, 'kernel': 'rbf', 'gamma': 1.4454397707459268e-05}
0.695 (+/-0.300) for {'C': 1137627.2858234306, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.694 (+/-0.299) for {'C': 1158777.3561551254, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.695 (+/-0.302) for {'C': 1158777.3561551254, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.695 (+/-0.301) for {'C': 1158777.3561551254, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.695 (+/-0.303) for {'C': 1158777.3561551254, 'kernel': 'rbf', 'gamma': 8.317637711026698e-06}
0.695 (+/-0.304) for {'C': 1158777.3561551254, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.695 (+/-0.300) for {'C': 1158777.3561551254, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.695 (+/-0.300) for {'C': 1158777.3561551254, 'kernel': 'rbf', 'gamma': 1.0964781961431852e-05}
0.696 (+/-0.302) for {'C': 1158777.3561551254, 'kernel': 'rbf', 'gamma': 1.2022644346174139e-05}
0.695 (+/-0.303) for {'C': 1158777.3561551254, 'kernel': 'rbf', 'gamma': 1.3182567385564057e-05}
0.696 (+/-0.302) for {'C': 1158777.3561551254, 'kernel': 'rbf', 'gamma': 1.4454397707459268e-05}
0.695 (+/-0.300) for {'C': 1158777.3561551254, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.694 (+/-0.299) for {'C': 1180320.6356517284, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.695 (+/-0.302) for {'C': 1180320.6356517284, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.695 (+/-0.301) for {'C': 1180320.6356517284, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.695 (+/-0.302) for {'C': 1180320.6356517284, 'kernel': 'rbf', 'gamma': 8.317637711026698e-06}
0.695 (+/-0.302) for {'C': 1180320.6356517284, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.695 (+/-0.300) for {'C': 1180320.6356517284, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.695 (+/-0.300) for {'C': 1180320.6356517284, 'kernel': 'rbf', 'gamma': 1.0964781961431852e-05}
0.696 (+/-0.302) for {'C': 1180320.6356517284, 'kernel': 'rbf', 'gamma': 1.2022644346174139e-05}
0.695 (+/-0.303) for {'C': 1180320.6356517284, 'kernel': 'rbf', 'gamma': 1.3182567385564057e-05}
0.696 (+/-0.302) for {'C': 1180320.6356517284, 'kernel': 'rbf', 'gamma': 1.4454397707459268e-05}
0.695 (+/-0.300) for {'C': 1180320.6356517284, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.695 (+/-0.300) for {'C': 1202264.4346174113, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.695 (+/-0.302) for {'C': 1202264.4346174113, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.695 (+/-0.301) for {'C': 1202264.4346174113, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.695 (+/-0.302) for {'C': 1202264.4346174113, 'kernel': 'rbf', 'gamma': 8.317637711026698e-06}
0.695 (+/-0.304) for {'C': 1202264.4346174113, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.695 (+/-0.300) for {'C': 1202264.4346174113, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.695 (+/-0.300) for {'C': 1202264.4346174113, 'kernel': 'rbf', 'gamma': 1.0964781961431852e-05}
0.696 (+/-0.302) for {'C': 1202264.4346174113, 'kernel': 'rbf', 'gamma': 1.2022644346174139e-05}
0.695 (+/-0.303) for {'C': 1202264.4346174113, 'kernel': 'rbf', 'gamma': 1.3182567385564057e-05}
0.696 (+/-0.302) for {'C': 1202264.4346174113, 'kernel': 'rbf', 'gamma': 1.4454397707459268e-05}
0.695 (+/-0.300) for {'C': 1202264.4346174113, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.694 (+/-0.299) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.695 (+/-0.302) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.695 (+/-0.300) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.695 (+/-0.302) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 8.317637711026698e-06}
0.696 (+/-0.304) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.695 (+/-0.300) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.695 (+/-0.300) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.0964781961431852e-05}
0.696 (+/-0.302) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.2022644346174139e-05}
0.695 (+/-0.303) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.3182567385564057e-05}
0.696 (+/-0.302) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.4454397707459268e-05}
0.695 (+/-0.301) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.694 (+/-0.299) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.695 (+/-0.302) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.695 (+/-0.300) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.695 (+/-0.302) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 8.317637711026698e-06}
0.696 (+/-0.304) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.695 (+/-0.300) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.695 (+/-0.300) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.0964781961431852e-05}
0.695 (+/-0.301) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.2022644346174139e-05}
0.695 (+/-0.303) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.3182567385564057e-05}
0.696 (+/-0.302) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.4454397707459268e-05}
0.695 (+/-0.301) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.694 (+/-0.299) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.695 (+/-0.302) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.695 (+/-0.301) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.695 (+/-0.303) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 8.317637711026698e-06}
0.696 (+/-0.304) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.695 (+/-0.300) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.695 (+/-0.300) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.0964781961431852e-05}
0.696 (+/-0.302) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.2022644346174139e-05}
0.695 (+/-0.303) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.3182567385564057e-05}
0.696 (+/-0.302) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.4454397707459268e-05}
0.695 (+/-0.301) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.694 (+/-0.299) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.695 (+/-0.302) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.695 (+/-0.300) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.695 (+/-0.304) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 8.317637711026698e-06}
0.696 (+/-0.304) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.695 (+/-0.299) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.695 (+/-0.300) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 1.0964781961431852e-05}
0.696 (+/-0.303) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 1.2022644346174139e-05}
0.695 (+/-0.303) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 1.3182567385564057e-05}
0.696 (+/-0.302) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 1.4454397707459268e-05}
0.695 (+/-0.301) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.694 (+/-0.299) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.695 (+/-0.302) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.695 (+/-0.301) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.695 (+/-0.304) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 8.317637711026698e-06}
0.696 (+/-0.304) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.694 (+/-0.300) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.695 (+/-0.300) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.0964781961431852e-05}
0.696 (+/-0.303) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.2022644346174139e-05}
0.695 (+/-0.303) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.3182567385564057e-05}
0.696 (+/-0.302) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.4454397707459268e-05}
0.695 (+/-0.301) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 1.0, 'kernel': 'linear'}
0.696 (+/-0.302) for {'C': 10.0, 'kernel': 'linear'}
0.663 (+/-0.315) for {'C': 100.0, 'kernel': 'linear'}
0.660 (+/-0.314) for {'C': 1000.0, 'kernel': 'linear'}
0.635 (+/-0.324) for {'C': 10000.0, 'kernel': 'linear'}
0.634 (+/-0.325) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6983958900156649
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.581 (+/-0.146) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.582 (+/-0.152) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.585 (+/-0.154) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.578 (+/-0.146) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 8.317637711026698e-06}
0.584 (+/-0.154) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.584 (+/-0.154) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.581 (+/-0.154) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.0964781961431852e-05}
0.587 (+/-0.159) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.2022644346174139e-05}
0.586 (+/-0.161) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.3182567385564057e-05}
0.590 (+/-0.163) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.4454397707459268e-05}
0.582 (+/-0.163) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.582 (+/-0.145) for {'C': 1116863.2477805612, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.582 (+/-0.152) for {'C': 1116863.2477805612, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.584 (+/-0.154) for {'C': 1116863.2477805612, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.578 (+/-0.146) for {'C': 1116863.2477805612, 'kernel': 'rbf', 'gamma': 8.317637711026698e-06}
0.584 (+/-0.154) for {'C': 1116863.2477805612, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.583 (+/-0.155) for {'C': 1116863.2477805612, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.581 (+/-0.154) for {'C': 1116863.2477805612, 'kernel': 'rbf', 'gamma': 1.0964781961431852e-05}
0.587 (+/-0.159) for {'C': 1116863.2477805612, 'kernel': 'rbf', 'gamma': 1.2022644346174139e-05}
0.590 (+/-0.158) for {'C': 1116863.2477805612, 'kernel': 'rbf', 'gamma': 1.3182567385564057e-05}
0.587 (+/-0.162) for {'C': 1116863.2477805612, 'kernel': 'rbf', 'gamma': 1.4454397707459268e-05}
0.582 (+/-0.163) for {'C': 1116863.2477805612, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.581 (+/-0.146) for {'C': 1137627.2858234306, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.582 (+/-0.152) for {'C': 1137627.2858234306, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.584 (+/-0.155) for {'C': 1137627.2858234306, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.578 (+/-0.146) for {'C': 1137627.2858234306, 'kernel': 'rbf', 'gamma': 8.317637711026698e-06}
0.584 (+/-0.154) for {'C': 1137627.2858234306, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.583 (+/-0.155) for {'C': 1137627.2858234306, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.581 (+/-0.155) for {'C': 1137627.2858234306, 'kernel': 'rbf', 'gamma': 1.0964781961431852e-05}
0.587 (+/-0.160) for {'C': 1137627.2858234306, 'kernel': 'rbf', 'gamma': 1.2022644346174139e-05}
0.590 (+/-0.158) for {'C': 1137627.2858234306, 'kernel': 'rbf', 'gamma': 1.3182567385564057e-05}
0.587 (+/-0.162) for {'C': 1137627.2858234306, 'kernel': 'rbf', 'gamma': 1.4454397707459268e-05}
0.582 (+/-0.163) for {'C': 1137627.2858234306, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.580 (+/-0.145) for {'C': 1158777.3561551254, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.582 (+/-0.152) for {'C': 1158777.3561551254, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.584 (+/-0.154) for {'C': 1158777.3561551254, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.578 (+/-0.146) for {'C': 1158777.3561551254, 'kernel': 'rbf', 'gamma': 8.317637711026698e-06}
0.584 (+/-0.154) for {'C': 1158777.3561551254, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.583 (+/-0.155) for {'C': 1158777.3561551254, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.580 (+/-0.155) for {'C': 1158777.3561551254, 'kernel': 'rbf', 'gamma': 1.0964781961431852e-05}
0.587 (+/-0.160) for {'C': 1158777.3561551254, 'kernel': 'rbf', 'gamma': 1.2022644346174139e-05}
0.589 (+/-0.159) for {'C': 1158777.3561551254, 'kernel': 'rbf', 'gamma': 1.3182567385564057e-05}
0.587 (+/-0.162) for {'C': 1158777.3561551254, 'kernel': 'rbf', 'gamma': 1.4454397707459268e-05}
0.585 (+/-0.160) for {'C': 1158777.3561551254, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.580 (+/-0.145) for {'C': 1180320.6356517284, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.582 (+/-0.152) for {'C': 1180320.6356517284, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.584 (+/-0.154) for {'C': 1180320.6356517284, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.578 (+/-0.146) for {'C': 1180320.6356517284, 'kernel': 'rbf', 'gamma': 8.317637711026698e-06}
0.584 (+/-0.154) for {'C': 1180320.6356517284, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.583 (+/-0.155) for {'C': 1180320.6356517284, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.580 (+/-0.155) for {'C': 1180320.6356517284, 'kernel': 'rbf', 'gamma': 1.0964781961431852e-05}
0.589 (+/-0.161) for {'C': 1180320.6356517284, 'kernel': 'rbf', 'gamma': 1.2022644346174139e-05}
0.589 (+/-0.159) for {'C': 1180320.6356517284, 'kernel': 'rbf', 'gamma': 1.3182567385564057e-05}
0.587 (+/-0.162) for {'C': 1180320.6356517284, 'kernel': 'rbf', 'gamma': 1.4454397707459268e-05}
0.585 (+/-0.160) for {'C': 1180320.6356517284, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.580 (+/-0.145) for {'C': 1202264.4346174113, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.582 (+/-0.152) for {'C': 1202264.4346174113, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.585 (+/-0.154) for {'C': 1202264.4346174113, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.578 (+/-0.146) for {'C': 1202264.4346174113, 'kernel': 'rbf', 'gamma': 8.317637711026698e-06}
0.584 (+/-0.154) for {'C': 1202264.4346174113, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.587 (+/-0.154) for {'C': 1202264.4346174113, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.580 (+/-0.155) for {'C': 1202264.4346174113, 'kernel': 'rbf', 'gamma': 1.0964781961431852e-05}
0.589 (+/-0.162) for {'C': 1202264.4346174113, 'kernel': 'rbf', 'gamma': 1.2022644346174139e-05}
0.590 (+/-0.158) for {'C': 1202264.4346174113, 'kernel': 'rbf', 'gamma': 1.3182567385564057e-05}
0.587 (+/-0.162) for {'C': 1202264.4346174113, 'kernel': 'rbf', 'gamma': 1.4454397707459268e-05}
0.585 (+/-0.160) for {'C': 1202264.4346174113, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.580 (+/-0.145) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.580 (+/-0.151) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.587 (+/-0.154) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.578 (+/-0.146) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 8.317637711026698e-06}
0.585 (+/-0.154) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.587 (+/-0.155) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.580 (+/-0.155) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.0964781961431852e-05}
0.589 (+/-0.162) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.2022644346174139e-05}
0.589 (+/-0.159) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.3182567385564057e-05}
0.587 (+/-0.162) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.4454397707459268e-05}
0.585 (+/-0.161) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.580 (+/-0.145) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.581 (+/-0.153) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.585 (+/-0.155) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.578 (+/-0.146) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 8.317637711026698e-06}
0.585 (+/-0.154) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.586 (+/-0.154) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.579 (+/-0.155) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.0964781961431852e-05}
0.589 (+/-0.162) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.2022644346174139e-05}
0.589 (+/-0.159) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.3182567385564057e-05}
0.587 (+/-0.162) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.4454397707459268e-05}
0.585 (+/-0.161) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.580 (+/-0.145) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.583 (+/-0.155) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.585 (+/-0.155) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.578 (+/-0.146) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 8.317637711026698e-06}
0.585 (+/-0.154) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.586 (+/-0.154) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.579 (+/-0.155) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.0964781961431852e-05}
0.588 (+/-0.162) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.2022644346174139e-05}
0.588 (+/-0.159) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.3182567385564057e-05}
0.587 (+/-0.162) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.4454397707459268e-05}
0.585 (+/-0.161) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.580 (+/-0.145) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.583 (+/-0.155) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.584 (+/-0.155) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.578 (+/-0.146) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 8.317637711026698e-06}
0.585 (+/-0.154) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.586 (+/-0.154) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.579 (+/-0.155) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 1.0964781961431852e-05}
0.588 (+/-0.162) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 1.2022644346174139e-05}
0.588 (+/-0.159) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 1.3182567385564057e-05}
0.587 (+/-0.162) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 1.4454397707459268e-05}
0.585 (+/-0.161) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.580 (+/-0.145) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.581 (+/-0.153) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.583 (+/-0.156) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.577 (+/-0.146) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 8.317637711026698e-06}
0.585 (+/-0.154) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.586 (+/-0.154) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.579 (+/-0.155) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.0964781961431852e-05}
0.588 (+/-0.162) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.2022644346174139e-05}
0.588 (+/-0.159) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.3182567385564057e-05}
0.586 (+/-0.162) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.4454397707459268e-05}
0.586 (+/-0.161) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.798 (+/-0.437) for {'C': 0.1, 'kernel': 'linear'}
0.660 (+/-0.219) for {'C': 1.0, 'kernel': 'linear'}
0.585 (+/-0.147) for {'C': 10.0, 'kernel': 'linear'}
0.582 (+/-0.190) for {'C': 100.0, 'kernel': 'linear'}
0.581 (+/-0.185) for {'C': 1000.0, 'kernel': 'linear'}
0.576 (+/-0.191) for {'C': 10000.0, 'kernel': 'linear'}
0.575 (+/-0.191) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.693 (+/-0.303) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.693 (+/-0.303) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.693 (+/-0.303) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.693 (+/-0.304) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 8.317637711026698e-06}
0.693 (+/-0.304) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.692 (+/-0.304) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.692 (+/-0.302) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.0964781961431852e-05}
0.693 (+/-0.304) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.2022644346174139e-05}
0.692 (+/-0.303) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.3182567385564057e-05}
0.692 (+/-0.305) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.4454397707459268e-05}
0.690 (+/-0.303) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.693 (+/-0.304) for {'C': 1116863.2477805612, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.693 (+/-0.303) for {'C': 1116863.2477805612, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.693 (+/-0.303) for {'C': 1116863.2477805612, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.693 (+/-0.304) for {'C': 1116863.2477805612, 'kernel': 'rbf', 'gamma': 8.317637711026698e-06}
0.693 (+/-0.304) for {'C': 1116863.2477805612, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.692 (+/-0.304) for {'C': 1116863.2477805612, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.692 (+/-0.302) for {'C': 1116863.2477805612, 'kernel': 'rbf', 'gamma': 1.0964781961431852e-05}
0.692 (+/-0.304) for {'C': 1116863.2477805612, 'kernel': 'rbf', 'gamma': 1.2022644346174139e-05}
0.717 (+/-0.351) for {'C': 1116863.2477805612, 'kernel': 'rbf', 'gamma': 1.3182567385564057e-05}
0.691 (+/-0.305) for {'C': 1116863.2477805612, 'kernel': 'rbf', 'gamma': 1.4454397707459268e-05}
0.690 (+/-0.303) for {'C': 1116863.2477805612, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.693 (+/-0.304) for {'C': 1137627.2858234306, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.693 (+/-0.303) for {'C': 1137627.2858234306, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.692 (+/-0.303) for {'C': 1137627.2858234306, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.693 (+/-0.304) for {'C': 1137627.2858234306, 'kernel': 'rbf', 'gamma': 8.317637711026698e-06}
0.693 (+/-0.304) for {'C': 1137627.2858234306, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.692 (+/-0.303) for {'C': 1137627.2858234306, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.692 (+/-0.302) for {'C': 1137627.2858234306, 'kernel': 'rbf', 'gamma': 1.0964781961431852e-05}
0.692 (+/-0.304) for {'C': 1137627.2858234306, 'kernel': 'rbf', 'gamma': 1.2022644346174139e-05}
0.717 (+/-0.351) for {'C': 1137627.2858234306, 'kernel': 'rbf', 'gamma': 1.3182567385564057e-05}
0.691 (+/-0.305) for {'C': 1137627.2858234306, 'kernel': 'rbf', 'gamma': 1.4454397707459268e-05}
0.690 (+/-0.303) for {'C': 1137627.2858234306, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.693 (+/-0.303) for {'C': 1158777.3561551254, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.693 (+/-0.303) for {'C': 1158777.3561551254, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.693 (+/-0.303) for {'C': 1158777.3561551254, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.693 (+/-0.304) for {'C': 1158777.3561551254, 'kernel': 'rbf', 'gamma': 8.317637711026698e-06}
0.693 (+/-0.304) for {'C': 1158777.3561551254, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.692 (+/-0.303) for {'C': 1158777.3561551254, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.692 (+/-0.301) for {'C': 1158777.3561551254, 'kernel': 'rbf', 'gamma': 1.0964781961431852e-05}
0.692 (+/-0.304) for {'C': 1158777.3561551254, 'kernel': 'rbf', 'gamma': 1.2022644346174139e-05}
0.716 (+/-0.351) for {'C': 1158777.3561551254, 'kernel': 'rbf', 'gamma': 1.3182567385564057e-05}
0.691 (+/-0.305) for {'C': 1158777.3561551254, 'kernel': 'rbf', 'gamma': 1.4454397707459268e-05}
0.715 (+/-0.348) for {'C': 1158777.3561551254, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.693 (+/-0.303) for {'C': 1180320.6356517284, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.693 (+/-0.303) for {'C': 1180320.6356517284, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.693 (+/-0.303) for {'C': 1180320.6356517284, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.693 (+/-0.304) for {'C': 1180320.6356517284, 'kernel': 'rbf', 'gamma': 8.317637711026698e-06}
0.693 (+/-0.305) for {'C': 1180320.6356517284, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.692 (+/-0.303) for {'C': 1180320.6356517284, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.692 (+/-0.301) for {'C': 1180320.6356517284, 'kernel': 'rbf', 'gamma': 1.0964781961431852e-05}
0.692 (+/-0.304) for {'C': 1180320.6356517284, 'kernel': 'rbf', 'gamma': 1.2022644346174139e-05}
0.716 (+/-0.351) for {'C': 1180320.6356517284, 'kernel': 'rbf', 'gamma': 1.3182567385564057e-05}
0.691 (+/-0.305) for {'C': 1180320.6356517284, 'kernel': 'rbf', 'gamma': 1.4454397707459268e-05}
0.714 (+/-0.349) for {'C': 1180320.6356517284, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.693 (+/-0.303) for {'C': 1202264.4346174113, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.693 (+/-0.303) for {'C': 1202264.4346174113, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.693 (+/-0.303) for {'C': 1202264.4346174113, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.692 (+/-0.303) for {'C': 1202264.4346174113, 'kernel': 'rbf', 'gamma': 8.317637711026698e-06}
0.693 (+/-0.305) for {'C': 1202264.4346174113, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.717 (+/-0.352) for {'C': 1202264.4346174113, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.691 (+/-0.301) for {'C': 1202264.4346174113, 'kernel': 'rbf', 'gamma': 1.0964781961431852e-05}
0.692 (+/-0.304) for {'C': 1202264.4346174113, 'kernel': 'rbf', 'gamma': 1.2022644346174139e-05}
0.717 (+/-0.351) for {'C': 1202264.4346174113, 'kernel': 'rbf', 'gamma': 1.3182567385564057e-05}
0.691 (+/-0.305) for {'C': 1202264.4346174113, 'kernel': 'rbf', 'gamma': 1.4454397707459268e-05}
0.714 (+/-0.349) for {'C': 1202264.4346174113, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.693 (+/-0.303) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.692 (+/-0.303) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.693 (+/-0.303) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.692 (+/-0.303) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 8.317637711026698e-06}
0.693 (+/-0.305) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.717 (+/-0.352) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.691 (+/-0.301) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.0964781961431852e-05}
0.692 (+/-0.304) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.2022644346174139e-05}
0.716 (+/-0.351) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.3182567385564057e-05}
0.691 (+/-0.305) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.4454397707459268e-05}
0.714 (+/-0.349) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.693 (+/-0.304) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.692 (+/-0.303) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.693 (+/-0.303) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.692 (+/-0.303) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 8.317637711026698e-06}
0.693 (+/-0.305) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.717 (+/-0.351) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.691 (+/-0.301) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.0964781961431852e-05}
0.692 (+/-0.304) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.2022644346174139e-05}
0.716 (+/-0.351) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.3182567385564057e-05}
0.691 (+/-0.306) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.4454397707459268e-05}
0.714 (+/-0.349) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.693 (+/-0.304) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.692 (+/-0.304) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.693 (+/-0.303) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.692 (+/-0.304) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 8.317637711026698e-06}
0.693 (+/-0.305) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.717 (+/-0.351) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.691 (+/-0.301) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.0964781961431852e-05}
0.692 (+/-0.303) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.2022644346174139e-05}
0.716 (+/-0.350) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.3182567385564057e-05}
0.691 (+/-0.307) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.4454397707459268e-05}
0.714 (+/-0.349) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.693 (+/-0.304) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.692 (+/-0.304) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.692 (+/-0.303) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.692 (+/-0.304) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 8.317637711026698e-06}
0.693 (+/-0.305) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.717 (+/-0.351) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.691 (+/-0.301) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 1.0964781961431852e-05}
0.692 (+/-0.303) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 1.2022644346174139e-05}
0.716 (+/-0.350) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 1.3182567385564057e-05}
0.691 (+/-0.307) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 1.4454397707459268e-05}
0.714 (+/-0.349) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.693 (+/-0.304) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.692 (+/-0.304) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.692 (+/-0.302) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.692 (+/-0.304) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 8.317637711026698e-06}
0.693 (+/-0.305) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.717 (+/-0.351) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.691 (+/-0.301) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.0964781961431852e-05}
0.692 (+/-0.303) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.2022644346174139e-05}
0.716 (+/-0.350) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.3182567385564057e-05}
0.690 (+/-0.306) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.4454397707459268e-05}
0.714 (+/-0.349) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.700 (+/-0.309) for {'C': 0.1, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 1.0, 'kernel': 'linear'}
0.694 (+/-0.301) for {'C': 10.0, 'kernel': 'linear'}
0.657 (+/-0.313) for {'C': 100.0, 'kernel': 'linear'}
0.659 (+/-0.314) for {'C': 1000.0, 'kernel': 'linear'}
0.634 (+/-0.324) for {'C': 10000.0, 'kernel': 'linear'}
0.633 (+/-0.324) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.04      0.17      0.06         6
          1       0.99      0.96      0.98       623

avg / total       0.98      0.95      0.97       629

本轮grid search结果，得到最好的参数选择是： {'C': 1202264.4346174113, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.717144344133894
第1轮loop中，tunemodel函数得到的最优参数是： ([{'C': array([1180320.63565173, 1184677.11758149, 1189049.6789851 ,
       1193438.37921079, 1197843.27782586, 1202264.43461741,
       1206701.90959327, 1211155.76298273, 1215626.05523738,
       1220112.84703193, 1224616.19926505]), 'kernel': ['rbf'], 'gamma': array([9.12010839e-06, 9.28966387e-06, 9.46237161e-06, 9.63829024e-06,
       9.81747943e-06, 1.00000000e-05, 1.01859139e-05, 1.03752842e-05,
       1.05681751e-05, 1.07646521e-05, 1.09647820e-05])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}], {'C': 1202264.4346174113, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}, 0.717144344133894)
这是第1次迭代微调C和gamma。
第1次迭代，得到delta： [2.79396772e-09 0.00000000e+00]
SVC模型clf_op_iter的参数是： {'kernel': 'rbf', 'decision_function_shape': 'ovr', 'class_weight': {-1: 15}, 'tol': 0.001, 'gamma': 9.999999999999996e-06, 'random_state': 0, 'cache_size': 200, 'verbose': False, 'degree': 3, 'C': 1202264.4346174113, 'probability': False, 'shrinking': True, 'coef0': 0.0, 'max_iter': -1}

训练模型SVC对训练样本的预测准确率： 0.9447200566973778

测试集中，预测为舞弊样本的有： (array([   1,    2,    3,    4,    7,    8,   10,   11,   12,   13,   14,
         17,   19,   20,   21,   22,   23,   24,   25,   27,   28,   29,
         30,   31,   32,   33,   34,   35,   36,   37,   38,   39,   40,
         41,   42,   43,   44,   45,   46,   47,   49,   50,   51,   52,
         53,   54,   57,   58,   59,   60,   61,   62,   63,   64,   65,
         66,   67,   68,   69,   70,   71,   72,   73,   74,   75,   76,
         77,   78,   79,   80,   81,   82,   83,   84,   87,   88,   89,
         90,   91,   92,   93,  102,  107,  108,  109,  110,  111,  112,
        113,  114,  115,  116,  117,  118,  119,  120,  121,  127,  128,
        129,  133,  135,  136,  140,  141,  142,  143,  144,  145,  146,
        147,  148,  149,  150,  151,  153,  154,  155,  156,  157,  158,
        162,  163,  164,  165,  166,  176,  177,  178,  179,  180,  181,
        182,  183,  184,  185,  186,  187,  188,  189,  190,  191,  192,
        193,  196,  197,  198,  199,  200,  201,  202,  203,  204,  205,
        206,  207,  208,  209,  210,  211,  212,  213,  214,  215,  216,
        217,  218,  219,  223,  225,  226,  227,  228,  229,  230,  231,
        232,  233,  234,  235,  236,  237,  238,  239,  242,  243,  244,
        245,  246,  247,  251,  252,  253,  254,  255,  256,  257,  258,
        259,  260,  261,  262,  264,  267,  268,  269,  270,  274,  275,
        276,  277,  278,  279,  280,  281,  282,  283,  284,  285,  286,
        287,  288,  289,  290,  292,  293,  295,  296,  297,  298,  299,
        300,  301,  302,  306,  307,  308,  309,  310,  313,  316,  319,
        320,  321,  322,  323,  324,  325,  330,  331,  332,  333,  334,
        335,  336,  337,  338,  339,  340,  342,  343,  344,  346,  347,
        348,  349,  351,  352,  353,  354,  356,  357,  358,  359,  360,
        361,  362,  363,  364,  365,  366,  367,  368,  369,  370,  371,
        372,  373,  374,  376,  377,  378,  381,  382,  383,  384,  385,
        386,  387,  388,  391,  392,  393,  394,  395,  398,  399,  403,
        404,  406,  409,  410,  411,  412,  413,  414,  417,  419,  420,
        421,  422,  423,  426,  427,  429,  430,  431,  432,  433,  434,
        436,  437,  438,  439,  440,  441,  442,  443,  444,  445,  446,
        447,  448,  449,  450,  451,  453,  454,  455,  456,  457,  460,
        461,  462,  463,  464,  465,  466,  467,  468,  469,  471,  472,
        473,  477,  478,  479,  480,  481,  482,  483,  484,  485,  486,
        487,  488,  489,  490,  491,  492,  493,  495,  496,  498,  499,
        500,  501,  502,  503,  504,  505,  506,  507,  508,  509,  510,
        511,  512,  513,  514,  515,  516,  517,  518,  519,  521,  523,
        525,  529,  530,  531,  532,  534,  535,  537,  539,  540,  541,
        542,  543,  544,  545,  546,  547,  549,  550,  551,  552,  555,
        557,  558,  559,  560,  561,  562,  563,  564,  565,  566,  567,
        568,  570,  571,  572,  573,  578,  582,  583,  584,  586,  587,
        589,  590,  591,  592,  593,  594,  595,  599,  600,  601,  602,
        603,  604,  608,  610,  611,  612,  613,  614,  615,  616,  617,
        618,  619,  620,  621,  622,  623,  624,  627,  628,  629,  630,
        631,  633,  634,  635,  641,  642,  643,  645,  646,  649,  651,
        652,  653,  654,  655,  656,  657,  658,  659,  660,  661,  662,
        663,  664,  665,  666,  667,  668,  669,  670,  673,  674,  675,
        676,  677,  678,  680,  681,  682,  683,  684,  685,  686,  687,
        688,  692,  694,  695,  696,  697,  698,  699,  701,  702,  703,
        706,  707,  708,  709,  710,  711,  712,  713,  714,  715,  716,
        717,  718,  719,  720,  721,  722,  723,  724,  725,  727,  729,
        733,  734,  735,  736,  737,  739,  740,  741,  742,  743,  750,
        751,  752,  753,  754,  755,  756,  757,  758,  759,  760,  761,
        767,  768,  769,  770,  773,  774,  775,  776,  777,  778,  784,
        785,  786,  787,  788,  789,  790,  792,  793,  794,  795,  796,
        797,  803,  804,  805,  806,  807,  808,  809,  810,  811,  812,
        813,  814,  815,  816,  817,  818,  819,  820,  822,  823,  824,
        825,  826,  827,  828,  829,  830,  831,  835,  839,  840,  841,
        843,  844,  845,  846,  847,  848,  849,  850,  851,  852,  853,
        854,  855,  856,  857,  858,  859,  860,  861,  862,  864,  865,
        866,  868,  872,  873,  874,  875,  876,  877,  878,  879,  880,
        881,  882,  883,  884,  885,  886,  887,  888,  889,  890,  892,
        893,  897,  898,  900,  901,  902,  903,  904,  905,  906,  908,
        910,  911,  912,  913,  914,  917,  919,  920,  922,  923,  924,
        925,  926,  927,  928,  929,  930,  931,  932,  933,  934,  935,
        941,  945,  946,  947,  948,  949,  950,  951,  952,  953,  954,
        955,  956,  957,  958,  959,  960,  961,  962,  963,  964,  965,
        966,  967,  970,  971,  972,  976,  977,  978,  979,  980,  981,
        982,  983,  984,  985,  992,  993,  994,  995,  998,  999, 1000,
       1001, 1002, 1003, 1004, 1005, 1006, 1007, 1008, 1009, 1010, 1011,
       1012, 1013, 1014, 1015, 1016, 1017, 1020, 1021, 1022, 1027, 1028,
       1030, 1034, 1035, 1036, 1037, 1039, 1040, 1041, 1042, 1046, 1047,
       1048, 1049, 1050, 1051, 1055, 1056, 1057, 1058, 1060, 1061, 1064,
       1065, 1066, 1067, 1068, 1071, 1073, 1074, 1075, 1076, 1079, 1080,
       1081, 1082, 1083, 1084, 1085, 1086, 1089, 1090, 1093, 1094, 1095,
       1096, 1097, 1098, 1099, 1100, 1101, 1102, 1103, 1104, 1105, 1106,
       1107, 1110, 1111, 1112, 1113, 1114, 1115, 1117, 1118, 1119, 1121,
       1122, 1123, 1124, 1125, 1127, 1129, 1131, 1132, 1134, 1135, 1137,
       1138, 1139, 1140, 1141, 1142, 1144, 1148, 1151, 1152, 1153, 1154,
       1156, 1158, 1160, 1162, 1163, 1164, 1167, 1168, 1169, 1170, 1171,
       1172, 1175, 1177, 1180, 1183, 1184, 1186, 1188, 1189, 1191, 1192,
       1194, 1196, 1199, 1200, 1204, 1205, 1207, 1208, 1210, 1211, 1214,
       1215, 1216, 1217, 1218, 1219, 1220, 1222, 1226, 1227, 1228, 1230,
       1233, 1234, 1235, 1236, 1237, 1238, 1239, 1240, 1241, 1242, 1243,
       1244, 1245, 1246, 1247, 1248, 1249, 1250, 1251, 1252, 1254, 1255,
       1256], dtype=int64),) 
测试集中，实际为舞弊样本的有： (array([1246, 1247, 1248, 1249, 1250, 1251, 1252, 1253, 1254, 1255, 1256],
      dtype=int64),) 
预测的舞弊样本数目： 936

训练模型SVC对测试样本的预测准确率： 0.3330970942593905
以上是第42次特征筛选。
第42次特征筛选，AUC值是： 0.5829563694732233
X_train_iter_svc.shape is: (1257, 10)
X_test_iter_svc.shape is: (1257, 10)
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.722 (+/-0.473) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.602 (+/-0.306) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.660 (+/-0.219) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.612 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.698 (+/-0.306) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.20      0.17      0.18         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.99      0.99       629

本轮grid search结果，得到最好的参数选择是： {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6982356336054085
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.660 (+/-0.219) for {'C': 1.0, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.577 (+/-0.186) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.698 (+/-0.306) for {'C': 1.0, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.658 (+/-0.312) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6982356336054085
RBF的粗grid search最佳超参： {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001} RBF的粗grid search最高分值： 0.6982356336054085
linear的粗grid search最佳超参： {'C': 1.0, 'kernel': 'linear'} linear的粗grid search最高分值： 0.6982356336054085
粗grid search得到的parameter是：
 [{'C': array([1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03, 1.e+04, 1.e+05,
       1.e+06, 1.e+07, 1.e+08]), 'kernel': ['rbf'], 'gamma': array([1.e-08, 1.e-07, 1.e-06, 1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01,
       1.e+00, 1.e+01, 1.e+02])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.848 (+/-0.459) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.848 (+/-0.459) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.690 (+/-0.300) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.611 (+/-0.308) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.848 (+/-0.459) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.660 (+/-0.219) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.604 (+/-0.159) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.601 (+/-0.307) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.848 (+/-0.459) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.660 (+/-0.219) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.594 (+/-0.157) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.567 (+/-0.197) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.602 (+/-0.306) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.848 (+/-0.459) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.660 (+/-0.219) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.594 (+/-0.157) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.589 (+/-0.188) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.574 (+/-0.192) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.602 (+/-0.306) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.848 (+/-0.459) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.660 (+/-0.219) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.594 (+/-0.157) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.589 (+/-0.187) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.565 (+/-0.196) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.574 (+/-0.191) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.602 (+/-0.306) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.765 (+/-0.428) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.660 (+/-0.219) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.595 (+/-0.154) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.591 (+/-0.191) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.590 (+/-0.193) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.572 (+/-0.191) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.574 (+/-0.191) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.602 (+/-0.306) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.848 (+/-0.460) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.691 (+/-0.371) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.616 (+/-0.182) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.587 (+/-0.186) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.581 (+/-0.187) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.581 (+/-0.199) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.573 (+/-0.191) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.574 (+/-0.191) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.602 (+/-0.306) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.793 (+/-0.440) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.642 (+/-0.370) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.600 (+/-0.181) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.593 (+/-0.192) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.581 (+/-0.187) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.586 (+/-0.192) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.573 (+/-0.191) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.574 (+/-0.191) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.602 (+/-0.306) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.690 (+/-0.300) for {'C': 1.0, 'kernel': 'linear'}
0.599 (+/-0.162) for {'C': 10.0, 'kernel': 'linear'}
0.599 (+/-0.192) for {'C': 100.0, 'kernel': 'linear'}
0.580 (+/-0.187) for {'C': 1000.0, 'kernel': 'linear'}
0.590 (+/-0.191) for {'C': 10000.0, 'kernel': 'linear'}
0.588 (+/-0.193) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      1.00       623

avg / total       0.98      0.99      0.99       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.658 (+/-0.217) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.658 (+/-0.217) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.699 (+/-0.307) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.236) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.007) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.658 (+/-0.217) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.306) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.696 (+/-0.303) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.611 (+/-0.240) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.006) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.658 (+/-0.217) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.698 (+/-0.306) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.695 (+/-0.303) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.587 (+/-0.231) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.612 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.006) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.658 (+/-0.217) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.698 (+/-0.306) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.695 (+/-0.303) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.661 (+/-0.314) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.611 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.006) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.658 (+/-0.217) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.698 (+/-0.306) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.695 (+/-0.303) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.661 (+/-0.314) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.584 (+/-0.234) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.006) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.699 (+/-0.308) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.698 (+/-0.306) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.696 (+/-0.303) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.661 (+/-0.315) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.660 (+/-0.314) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.611 (+/-0.238) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.237) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.237) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.006) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.683 (+/-0.296) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.686 (+/-0.265) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.697 (+/-0.305) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.661 (+/-0.314) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.658 (+/-0.311) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.634 (+/-0.323) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.611 (+/-0.238) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.237) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.237) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.006) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.699 (+/-0.307) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.660 (+/-0.231) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.695 (+/-0.303) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.661 (+/-0.315) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.658 (+/-0.312) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.659 (+/-0.311) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.611 (+/-0.237) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.237) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.237) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.006) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.699 (+/-0.307) for {'C': 1.0, 'kernel': 'linear'}
0.696 (+/-0.303) for {'C': 10.0, 'kernel': 'linear'}
0.663 (+/-0.314) for {'C': 100.0, 'kernel': 'linear'}
0.659 (+/-0.313) for {'C': 1000.0, 'kernel': 'linear'}
0.660 (+/-0.314) for {'C': 10000.0, 'kernel': 'linear'}
0.659 (+/-0.313) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6991982026547944
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.673 (+/-0.294) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.667 (+/-0.292) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.631 (+/-0.319) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.667 (+/-0.292) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.199) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.592 (+/-0.295) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.667 (+/-0.292) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.628 (+/-0.188) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.589 (+/-0.154) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.601 (+/-0.307) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.667 (+/-0.292) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.628 (+/-0.188) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.579 (+/-0.146) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.571 (+/-0.193) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.602 (+/-0.306) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.642 (+/-0.204) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.628 (+/-0.188) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.579 (+/-0.146) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.577 (+/-0.187) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.574 (+/-0.192) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.602 (+/-0.306) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.747 (+/-0.502) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.642 (+/-0.204) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.626 (+/-0.188) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.579 (+/-0.146) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.575 (+/-0.187) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.571 (+/-0.191) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.574 (+/-0.191) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.602 (+/-0.306) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.547 (+/-0.301) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.681 (+/-0.307) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.618 (+/-0.196) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.582 (+/-0.146) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.575 (+/-0.186) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.587 (+/-0.193) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.572 (+/-0.191) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.574 (+/-0.191) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.602 (+/-0.306) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.831 (+/-0.449) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.538 (+/-0.143) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.570 (+/-0.145) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.577 (+/-0.185) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.582 (+/-0.187) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.581 (+/-0.199) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.573 (+/-0.191) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.574 (+/-0.191) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.602 (+/-0.306) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.793 (+/-0.440) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.552 (+/-0.297) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.581 (+/-0.163) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.585 (+/-0.193) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.581 (+/-0.187) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.586 (+/-0.192) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.573 (+/-0.191) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.574 (+/-0.191) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.602 (+/-0.306) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.798 (+/-0.437) for {'C': 0.1, 'kernel': 'linear'}
0.660 (+/-0.219) for {'C': 1.0, 'kernel': 'linear'}
0.585 (+/-0.147) for {'C': 10.0, 'kernel': 'linear'}
0.579 (+/-0.189) for {'C': 100.0, 'kernel': 'linear'}
0.577 (+/-0.186) for {'C': 1000.0, 'kernel': 'linear'}
0.588 (+/-0.191) for {'C': 10000.0, 'kernel': 'linear'}
0.587 (+/-0.193) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.682 (+/-0.294) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.698 (+/-0.306) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.494 (+/-0.012) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.306) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.305) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.611 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.007) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.698 (+/-0.306) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.304) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.693 (+/-0.303) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.610 (+/-0.240) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.006) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.698 (+/-0.306) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.304) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.693 (+/-0.304) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.607 (+/-0.241) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.612 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.006) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.698 (+/-0.306) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.304) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.693 (+/-0.304) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.656 (+/-0.311) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.611 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.006) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.617 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.698 (+/-0.306) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.697 (+/-0.304) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.692 (+/-0.304) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.656 (+/-0.311) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.608 (+/-0.242) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.006) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.525 (+/-0.150) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.698 (+/-0.309) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.696 (+/-0.301) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.693 (+/-0.305) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.657 (+/-0.310) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.659 (+/-0.313) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.611 (+/-0.238) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.237) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.237) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.006) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.700 (+/-0.309) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.652 (+/-0.227) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.692 (+/-0.299) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.680 (+/-0.365) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.658 (+/-0.312) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.634 (+/-0.323) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.611 (+/-0.238) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.237) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.237) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.006) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.699 (+/-0.307) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.575 (+/-0.206) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.693 (+/-0.303) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.680 (+/-0.366) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.658 (+/-0.312) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.659 (+/-0.311) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.611 (+/-0.237) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.237) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.237) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.006) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.683 (+/-0.296) for {'C': 0.1, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 1.0, 'kernel': 'linear'}
0.694 (+/-0.301) for {'C': 10.0, 'kernel': 'linear'}
0.656 (+/-0.311) for {'C': 100.0, 'kernel': 'linear'}
0.658 (+/-0.312) for {'C': 1000.0, 'kernel': 'linear'}
0.659 (+/-0.313) for {'C': 10000.0, 'kernel': 'linear'}
0.659 (+/-0.313) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6998392282958199
发现最优参数gamma为原先的最大/最小值，直接重新设置超参。
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-09}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-08}
0.765 (+/-0.428) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-07}
0.660 (+/-0.219) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-06}
0.595 (+/-0.154) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-05}
0.591 (+/-0.191) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.0001}
0.590 (+/-0.193) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-09}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-08}
0.773 (+/-0.417) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-07}
0.669 (+/-0.295) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-06}
0.596 (+/-0.159) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-05}
0.584 (+/-0.187) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.0001}
0.589 (+/-0.194) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-09}
0.747 (+/-0.502) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-08}
0.735 (+/-0.392) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-07}
0.630 (+/-0.189) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-06}
0.598 (+/-0.159) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-05}
0.583 (+/-0.186) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.0001}
0.588 (+/-0.193) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-09}
0.747 (+/-0.502) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-08}
0.798 (+/-0.438) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-07}
0.630 (+/-0.189) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-06}
0.594 (+/-0.191) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-05}
0.579 (+/-0.186) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 0.0001}
0.586 (+/-0.193) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-09}
0.797 (+/-0.492) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-08}
0.726 (+/-0.399) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-07}
0.630 (+/-0.189) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-06}
0.593 (+/-0.188) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-05}
0.577 (+/-0.185) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 0.0001}
0.585 (+/-0.193) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-09}
0.848 (+/-0.460) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-08}
0.690 (+/-0.372) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-07}
0.616 (+/-0.182) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-06}
0.587 (+/-0.186) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-05}
0.581 (+/-0.187) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 0.0001}
0.581 (+/-0.199) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-09}
0.823 (+/-0.451) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-08}
0.686 (+/-0.374) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-07}
0.602 (+/-0.180) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-06}
0.590 (+/-0.187) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-05}
0.582 (+/-0.187) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 0.0001}
0.580 (+/-0.199) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-10}
0.747 (+/-0.502) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-09}
0.815 (+/-0.434) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-08}
0.677 (+/-0.372) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-07}
0.600 (+/-0.181) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-06}
0.590 (+/-0.187) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-05}
0.581 (+/-0.188) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.0001}
0.580 (+/-0.199) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-10}
0.747 (+/-0.502) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-09}
0.806 (+/-0.436) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-08}
0.656 (+/-0.367) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-07}
0.600 (+/-0.181) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-06}
0.594 (+/-0.192) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-05}
0.581 (+/-0.188) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 0.0001}
0.580 (+/-0.199) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-10}
0.797 (+/-0.492) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-09}
0.798 (+/-0.437) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-08}
0.646 (+/-0.370) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-07}
0.600 (+/-0.181) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-06}
0.593 (+/-0.192) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-05}
0.581 (+/-0.188) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 0.0001}
0.585 (+/-0.193) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-10}
0.848 (+/-0.460) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-09}
0.793 (+/-0.440) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-08}
0.642 (+/-0.370) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-07}
0.600 (+/-0.181) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-06}
0.593 (+/-0.192) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-05}
0.581 (+/-0.187) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 0.0001}
0.586 (+/-0.192) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.690 (+/-0.300) for {'C': 1.0, 'kernel': 'linear'}
0.599 (+/-0.162) for {'C': 10.0, 'kernel': 'linear'}
0.599 (+/-0.192) for {'C': 100.0, 'kernel': 'linear'}
0.580 (+/-0.187) for {'C': 1000.0, 'kernel': 'linear'}
0.590 (+/-0.191) for {'C': 10000.0, 'kernel': 'linear'}
0.588 (+/-0.193) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      1.00       623

avg / total       0.98      0.99      0.99       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-09}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-08}
0.699 (+/-0.308) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-07}
0.698 (+/-0.306) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-06}
0.696 (+/-0.303) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-05}
0.661 (+/-0.315) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.0001}
0.660 (+/-0.314) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-09}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-08}
0.699 (+/-0.309) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-07}
0.698 (+/-0.305) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-06}
0.695 (+/-0.302) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-05}
0.660 (+/-0.315) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.0001}
0.659 (+/-0.315) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-09}
0.617 (+/-0.238) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-08}
0.699 (+/-0.307) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-07}
0.697 (+/-0.305) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-06}
0.679 (+/-0.291) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-05}
0.660 (+/-0.314) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.0001}
0.659 (+/-0.314) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-09}
0.617 (+/-0.238) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-08}
0.700 (+/-0.309) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-07}
0.697 (+/-0.305) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-06}
0.662 (+/-0.312) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-05}
0.659 (+/-0.314) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 0.0001}
0.659 (+/-0.313) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-09}
0.642 (+/-0.236) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-08}
0.682 (+/-0.291) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-07}
0.697 (+/-0.305) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-06}
0.662 (+/-0.314) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-05}
0.658 (+/-0.311) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 0.0001}
0.658 (+/-0.311) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-09}
0.683 (+/-0.296) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-08}
0.677 (+/-0.244) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-07}
0.697 (+/-0.305) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-06}
0.661 (+/-0.314) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-05}
0.658 (+/-0.311) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 0.0001}
0.634 (+/-0.323) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-09}
0.683 (+/-0.296) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-08}
0.655 (+/-0.211) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-07}
0.696 (+/-0.303) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-06}
0.661 (+/-0.315) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-05}
0.658 (+/-0.312) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 0.0001}
0.633 (+/-0.322) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-10}
0.617 (+/-0.238) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-09}
0.700 (+/-0.308) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-08}
0.653 (+/-0.209) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-07}
0.695 (+/-0.303) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-06}
0.661 (+/-0.315) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-05}
0.658 (+/-0.312) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.0001}
0.633 (+/-0.322) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-10}
0.617 (+/-0.238) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-09}
0.700 (+/-0.308) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-08}
0.664 (+/-0.232) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-07}
0.695 (+/-0.303) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-06}
0.661 (+/-0.315) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-05}
0.658 (+/-0.311) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 0.0001}
0.634 (+/-0.321) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-10}
0.642 (+/-0.236) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-09}
0.699 (+/-0.307) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-08}
0.662 (+/-0.231) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-07}
0.695 (+/-0.303) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-06}
0.661 (+/-0.315) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-05}
0.658 (+/-0.311) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 0.0001}
0.659 (+/-0.310) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-10}
0.683 (+/-0.296) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-09}
0.699 (+/-0.307) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-08}
0.658 (+/-0.232) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-07}
0.695 (+/-0.303) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-06}
0.661 (+/-0.315) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-05}
0.658 (+/-0.312) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 0.0001}
0.659 (+/-0.311) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.699 (+/-0.307) for {'C': 1.0, 'kernel': 'linear'}
0.696 (+/-0.303) for {'C': 10.0, 'kernel': 'linear'}
0.663 (+/-0.314) for {'C': 100.0, 'kernel': 'linear'}
0.659 (+/-0.313) for {'C': 1000.0, 'kernel': 'linear'}
0.660 (+/-0.314) for {'C': 10000.0, 'kernel': 'linear'}
0.659 (+/-0.313) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-07}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6996789718855635
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-09}
0.547 (+/-0.301) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-08}
0.681 (+/-0.307) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-07}
0.618 (+/-0.196) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-06}
0.582 (+/-0.146) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-05}
0.575 (+/-0.186) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.0001}
0.587 (+/-0.193) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-09}
0.747 (+/-0.502) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-08}
0.666 (+/-0.298) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-07}
0.616 (+/-0.291) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-06}
0.583 (+/-0.154) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-05}
0.575 (+/-0.186) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.0001}
0.586 (+/-0.193) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-09}
0.747 (+/-0.502) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-08}
0.658 (+/-0.292) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-07}
0.582 (+/-0.178) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-06}
0.584 (+/-0.153) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-05}
0.576 (+/-0.186) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.0001}
0.586 (+/-0.193) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-09}
0.797 (+/-0.492) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-08}
0.623 (+/-0.293) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-07}
0.566 (+/-0.145) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-06}
0.581 (+/-0.180) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-05}
0.576 (+/-0.185) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 0.0001}
0.586 (+/-0.193) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-09}
0.848 (+/-0.460) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-08}
0.556 (+/-0.140) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-07}
0.568 (+/-0.146) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-06}
0.577 (+/-0.185) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-05}
0.577 (+/-0.185) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 0.0001}
0.585 (+/-0.193) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-10}
0.547 (+/-0.301) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-09}
0.831 (+/-0.449) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-08}
0.541 (+/-0.143) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-07}
0.570 (+/-0.145) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-06}
0.577 (+/-0.185) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-05}
0.582 (+/-0.187) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 0.0001}
0.581 (+/-0.199) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-10}
0.747 (+/-0.502) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-09}
0.815 (+/-0.434) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-08}
0.534 (+/-0.144) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-07}
0.569 (+/-0.146) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-06}
0.581 (+/-0.187) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-05}
0.581 (+/-0.187) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 0.0001}
0.580 (+/-0.199) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-10}
0.747 (+/-0.502) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-09}
0.806 (+/-0.436) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-08}
0.529 (+/-0.146) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-07}
0.577 (+/-0.158) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-06}
0.581 (+/-0.187) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-05}
0.581 (+/-0.188) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.0001}
0.580 (+/-0.199) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-10}
0.797 (+/-0.492) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-09}
0.798 (+/-0.437) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-08}
0.553 (+/-0.296) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-07}
0.578 (+/-0.156) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-06}
0.585 (+/-0.193) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-05}
0.581 (+/-0.188) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 0.0001}
0.580 (+/-0.199) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-10}
0.848 (+/-0.460) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-09}
0.798 (+/-0.437) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-08}
0.552 (+/-0.297) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-07}
0.576 (+/-0.154) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-06}
0.585 (+/-0.193) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-05}
0.581 (+/-0.188) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 0.0001}
0.585 (+/-0.193) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-11}
0.547 (+/-0.301) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-10}
0.831 (+/-0.449) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-09}
0.793 (+/-0.440) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-08}
0.552 (+/-0.297) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-07}
0.581 (+/-0.163) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-06}
0.585 (+/-0.193) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-05}
0.581 (+/-0.187) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 0.0001}
0.586 (+/-0.192) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.798 (+/-0.437) for {'C': 0.1, 'kernel': 'linear'}
0.660 (+/-0.219) for {'C': 1.0, 'kernel': 'linear'}
0.585 (+/-0.147) for {'C': 10.0, 'kernel': 'linear'}
0.579 (+/-0.189) for {'C': 100.0, 'kernel': 'linear'}
0.577 (+/-0.186) for {'C': 1000.0, 'kernel': 'linear'}
0.588 (+/-0.191) for {'C': 10000.0, 'kernel': 'linear'}
0.587 (+/-0.193) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-09}
0.525 (+/-0.150) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-08}
0.698 (+/-0.309) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-07}
0.696 (+/-0.301) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-06}
0.693 (+/-0.305) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-05}
0.657 (+/-0.310) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.0001}
0.659 (+/-0.313) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-09}
0.617 (+/-0.238) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-08}
0.698 (+/-0.308) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-07}
0.694 (+/-0.297) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-06}
0.692 (+/-0.306) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-05}
0.656 (+/-0.311) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.0001}
0.658 (+/-0.314) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-09}
0.617 (+/-0.238) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-08}
0.698 (+/-0.307) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-07}
0.692 (+/-0.295) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-06}
0.699 (+/-0.345) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-05}
0.657 (+/-0.311) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.0001}
0.659 (+/-0.313) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-09}
0.642 (+/-0.236) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-08}
0.695 (+/-0.300) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-07}
0.691 (+/-0.295) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-06}
0.706 (+/-0.338) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-05}
0.657 (+/-0.311) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 0.0001}
0.659 (+/-0.313) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-09}
0.683 (+/-0.296) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-08}
0.683 (+/-0.276) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-07}
0.691 (+/-0.298) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-06}
0.681 (+/-0.364) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-05}
0.658 (+/-0.312) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 0.0001}
0.658 (+/-0.311) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-10}
0.525 (+/-0.150) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-09}
0.700 (+/-0.309) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-08}
0.658 (+/-0.238) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-07}
0.692 (+/-0.299) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-06}
0.680 (+/-0.365) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-05}
0.658 (+/-0.312) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 0.0001}
0.634 (+/-0.323) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-10}
0.617 (+/-0.238) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-09}
0.700 (+/-0.308) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-08}
0.632 (+/-0.212) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-07}
0.692 (+/-0.298) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-06}
0.680 (+/-0.365) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-05}
0.658 (+/-0.312) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 0.0001}
0.633 (+/-0.322) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-10}
0.617 (+/-0.238) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-09}
0.700 (+/-0.308) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-08}
0.603 (+/-0.195) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-07}
0.692 (+/-0.302) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-06}
0.680 (+/-0.366) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-05}
0.658 (+/-0.312) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.0001}
0.633 (+/-0.322) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-10}
0.642 (+/-0.236) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-09}
0.699 (+/-0.307) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-08}
0.588 (+/-0.210) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-07}
0.693 (+/-0.302) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-06}
0.680 (+/-0.367) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-05}
0.658 (+/-0.311) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 0.0001}
0.634 (+/-0.321) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-10}
0.683 (+/-0.296) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-09}
0.699 (+/-0.307) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-08}
0.585 (+/-0.209) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-07}
0.693 (+/-0.302) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-06}
0.679 (+/-0.366) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-05}
0.658 (+/-0.311) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 0.0001}
0.659 (+/-0.310) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-11}
0.525 (+/-0.150) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-10}
0.700 (+/-0.309) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-09}
0.699 (+/-0.307) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-08}
0.578 (+/-0.209) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-07}
0.693 (+/-0.303) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-06}
0.680 (+/-0.366) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-05}
0.658 (+/-0.312) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 0.0001}
0.659 (+/-0.311) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.683 (+/-0.296) for {'C': 0.1, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 1.0, 'kernel': 'linear'}
0.694 (+/-0.301) for {'C': 10.0, 'kernel': 'linear'}
0.656 (+/-0.311) for {'C': 100.0, 'kernel': 'linear'}
0.658 (+/-0.312) for {'C': 1000.0, 'kernel': 'linear'}
0.659 (+/-0.313) for {'C': 10000.0, 'kernel': 'linear'}
0.659 (+/-0.313) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.07      0.17      0.10         6
          1       0.99      0.98      0.98       623

avg / total       0.98      0.97      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-05}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.7060876824140491
循环迭代之前，delta is: [6.01892829e+06 9.99000000e-06]
执行tunemodel()函数前，使用的grid search parameter是：
 [{'C': array([2511886.43150958, 2754228.70333817, 3019951.72040201,
       3311311.21482591, 3630780.54770102, 3981071.70553498,
       4365158.32240166, 4786300.92322638, 5248074.60249773,
       5754399.37337156, 6309573.44480193]), 'kernel': ['rbf'], 'gamma': array([1.00000000e-06, 1.58489319e-06, 2.51188643e-06, 3.98107171e-06,
       6.30957344e-06, 1.00000000e-05, 1.58489319e-05, 2.51188643e-05,
       3.98107171e-05, 6.30957344e-05, 1.00000000e-04])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.630 (+/-0.189) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.666 (+/-0.298) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.597 (+/-0.161) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.604 (+/-0.180) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.597 (+/-0.156) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.598 (+/-0.159) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.616 (+/-0.236) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.588 (+/-0.185) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.588 (+/-0.185) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.584 (+/-0.190) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.583 (+/-0.186) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.630 (+/-0.189) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.666 (+/-0.298) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.595 (+/-0.160) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.591 (+/-0.153) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.599 (+/-0.157) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.585 (+/-0.169) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.616 (+/-0.236) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.588 (+/-0.185) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.584 (+/-0.184) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.584 (+/-0.190) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.583 (+/-0.186) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.630 (+/-0.189) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.665 (+/-0.299) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.594 (+/-0.159) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.592 (+/-0.154) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.594 (+/-0.153) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.593 (+/-0.190) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.616 (+/-0.236) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.588 (+/-0.185) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.583 (+/-0.185) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.584 (+/-0.190) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.582 (+/-0.186) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.630 (+/-0.189) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.660 (+/-0.295) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.594 (+/-0.159) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.592 (+/-0.154) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.603 (+/-0.175) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.594 (+/-0.191) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.618 (+/-0.236) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.588 (+/-0.185) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.583 (+/-0.185) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.584 (+/-0.190) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.579 (+/-0.186) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.630 (+/-0.189) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.655 (+/-0.295) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.593 (+/-0.159) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.594 (+/-0.154) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.602 (+/-0.175) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.597 (+/-0.193) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.605 (+/-0.198) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.585 (+/-0.184) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.581 (+/-0.185) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.584 (+/-0.190) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.579 (+/-0.186) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.630 (+/-0.189) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.644 (+/-0.288) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.590 (+/-0.155) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.592 (+/-0.152) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.606 (+/-0.179) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.594 (+/-0.191) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.598 (+/-0.192) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.585 (+/-0.184) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.582 (+/-0.185) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.585 (+/-0.190) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.579 (+/-0.186) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.630 (+/-0.189) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.643 (+/-0.289) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.590 (+/-0.155) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.593 (+/-0.152) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.596 (+/-0.191) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.594 (+/-0.191) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.599 (+/-0.192) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.585 (+/-0.184) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.581 (+/-0.185) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.585 (+/-0.190) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.578 (+/-0.185) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.630 (+/-0.189) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.635 (+/-0.288) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.589 (+/-0.153) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.595 (+/-0.153) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.596 (+/-0.191) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.595 (+/-0.191) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.599 (+/-0.192) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.583 (+/-0.184) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.582 (+/-0.185) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.588 (+/-0.194) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.577 (+/-0.186) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.630 (+/-0.189) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.630 (+/-0.288) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.594 (+/-0.159) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.593 (+/-0.153) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.597 (+/-0.191) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.595 (+/-0.191) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.599 (+/-0.192) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.583 (+/-0.184) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.572 (+/-0.161) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.588 (+/-0.194) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.578 (+/-0.185) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.630 (+/-0.189) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.629 (+/-0.288) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.590 (+/-0.155) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.599 (+/-0.175) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.597 (+/-0.191) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.597 (+/-0.193) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.595 (+/-0.189) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.582 (+/-0.185) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.571 (+/-0.160) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.588 (+/-0.194) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.577 (+/-0.185) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.630 (+/-0.189) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.627 (+/-0.289) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.593 (+/-0.155) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.598 (+/-0.175) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.594 (+/-0.190) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.593 (+/-0.188) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.595 (+/-0.189) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.582 (+/-0.185) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.571 (+/-0.161) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.588 (+/-0.194) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.577 (+/-0.185) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.690 (+/-0.300) for {'C': 1.0, 'kernel': 'linear'}
0.599 (+/-0.162) for {'C': 10.0, 'kernel': 'linear'}
0.599 (+/-0.192) for {'C': 100.0, 'kernel': 'linear'}
0.580 (+/-0.187) for {'C': 1000.0, 'kernel': 'linear'}
0.590 (+/-0.191) for {'C': 10000.0, 'kernel': 'linear'}
0.588 (+/-0.193) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.697 (+/-0.305) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.698 (+/-0.307) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.696 (+/-0.303) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.695 (+/-0.302) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.695 (+/-0.301) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.679 (+/-0.291) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.664 (+/-0.316) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.661 (+/-0.312) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.661 (+/-0.313) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.659 (+/-0.315) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.660 (+/-0.314) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.697 (+/-0.305) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.698 (+/-0.307) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.695 (+/-0.302) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.695 (+/-0.302) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.695 (+/-0.301) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.662 (+/-0.311) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.663 (+/-0.316) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.661 (+/-0.312) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.660 (+/-0.312) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.659 (+/-0.315) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.660 (+/-0.314) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.697 (+/-0.305) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.698 (+/-0.306) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.695 (+/-0.301) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.695 (+/-0.302) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.679 (+/-0.290) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.662 (+/-0.311) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.663 (+/-0.316) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.661 (+/-0.312) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.660 (+/-0.313) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.659 (+/-0.315) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.659 (+/-0.314) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.697 (+/-0.305) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.698 (+/-0.306) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.695 (+/-0.302) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.695 (+/-0.302) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.679 (+/-0.291) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.662 (+/-0.312) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.663 (+/-0.317) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.661 (+/-0.313) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.660 (+/-0.313) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.659 (+/-0.315) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.659 (+/-0.313) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.697 (+/-0.305) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.697 (+/-0.306) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.695 (+/-0.302) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.695 (+/-0.303) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.679 (+/-0.290) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.662 (+/-0.314) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.663 (+/-0.316) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.660 (+/-0.313) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.660 (+/-0.312) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.659 (+/-0.315) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.659 (+/-0.313) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.697 (+/-0.305) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.697 (+/-0.305) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.695 (+/-0.303) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.695 (+/-0.302) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.679 (+/-0.290) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.662 (+/-0.312) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.662 (+/-0.316) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.660 (+/-0.313) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.660 (+/-0.312) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.659 (+/-0.315) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.659 (+/-0.314) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.697 (+/-0.305) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.696 (+/-0.304) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.695 (+/-0.303) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.695 (+/-0.303) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.662 (+/-0.311) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.662 (+/-0.312) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.662 (+/-0.316) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.661 (+/-0.313) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.660 (+/-0.312) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.659 (+/-0.315) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.659 (+/-0.313) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.697 (+/-0.305) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.696 (+/-0.303) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.695 (+/-0.301) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.695 (+/-0.303) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.662 (+/-0.311) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.662 (+/-0.313) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.662 (+/-0.316) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.660 (+/-0.313) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.660 (+/-0.312) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.659 (+/-0.316) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.658 (+/-0.312) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.697 (+/-0.305) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.696 (+/-0.303) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.695 (+/-0.302) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.695 (+/-0.303) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.662 (+/-0.312) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.662 (+/-0.314) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.662 (+/-0.316) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.660 (+/-0.312) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.659 (+/-0.311) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.659 (+/-0.316) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.659 (+/-0.312) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.697 (+/-0.305) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.696 (+/-0.303) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.695 (+/-0.303) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.695 (+/-0.302) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.662 (+/-0.312) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.662 (+/-0.314) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.662 (+/-0.316) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.660 (+/-0.312) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.659 (+/-0.310) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.659 (+/-0.316) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.658 (+/-0.313) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.697 (+/-0.305) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.696 (+/-0.303) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.695 (+/-0.303) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.695 (+/-0.302) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.662 (+/-0.313) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.662 (+/-0.314) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.661 (+/-0.316) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.660 (+/-0.312) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.659 (+/-0.311) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.659 (+/-0.316) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.658 (+/-0.311) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.699 (+/-0.307) for {'C': 1.0, 'kernel': 'linear'}
0.696 (+/-0.303) for {'C': 10.0, 'kernel': 'linear'}
0.663 (+/-0.314) for {'C': 100.0, 'kernel': 'linear'}
0.659 (+/-0.313) for {'C': 1000.0, 'kernel': 'linear'}
0.660 (+/-0.314) for {'C': 10000.0, 'kernel': 'linear'}
0.659 (+/-0.313) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6985561464259213
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.582 (+/-0.178) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.585 (+/-0.178) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.579 (+/-0.142) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.587 (+/-0.152) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.590 (+/-0.156) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.584 (+/-0.153) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.583 (+/-0.189) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.577 (+/-0.185) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.579 (+/-0.189) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.575 (+/-0.187) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.576 (+/-0.186) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.583 (+/-0.179) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.586 (+/-0.178) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.579 (+/-0.142) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.587 (+/-0.152) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.597 (+/-0.149) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.572 (+/-0.159) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.583 (+/-0.189) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.577 (+/-0.185) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.576 (+/-0.187) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.575 (+/-0.186) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.577 (+/-0.185) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.581 (+/-0.180) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.577 (+/-0.152) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.579 (+/-0.142) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.587 (+/-0.152) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.592 (+/-0.146) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.583 (+/-0.179) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.583 (+/-0.189) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.578 (+/-0.185) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.576 (+/-0.187) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.575 (+/-0.187) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.577 (+/-0.185) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.572 (+/-0.154) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.574 (+/-0.145) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.578 (+/-0.142) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.586 (+/-0.152) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.600 (+/-0.170) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.583 (+/-0.179) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.583 (+/-0.189) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.575 (+/-0.187) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.576 (+/-0.187) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.575 (+/-0.187) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.575 (+/-0.186) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.567 (+/-0.145) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.575 (+/-0.145) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.578 (+/-0.142) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.587 (+/-0.152) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.599 (+/-0.171) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.582 (+/-0.179) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.583 (+/-0.189) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.575 (+/-0.187) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.576 (+/-0.187) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.575 (+/-0.186) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.575 (+/-0.186) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.566 (+/-0.145) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.575 (+/-0.144) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.580 (+/-0.144) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.587 (+/-0.153) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.604 (+/-0.175) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.581 (+/-0.180) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.584 (+/-0.189) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.574 (+/-0.187) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.576 (+/-0.187) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.577 (+/-0.187) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.576 (+/-0.185) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.567 (+/-0.145) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.575 (+/-0.144) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.580 (+/-0.144) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.588 (+/-0.152) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.593 (+/-0.187) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.577 (+/-0.185) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.584 (+/-0.189) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.574 (+/-0.187) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.575 (+/-0.186) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.577 (+/-0.187) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.576 (+/-0.185) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.568 (+/-0.146) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.575 (+/-0.144) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.584 (+/-0.146) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.588 (+/-0.152) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.593 (+/-0.187) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.577 (+/-0.185) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.584 (+/-0.189) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.574 (+/-0.187) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.577 (+/-0.187) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.577 (+/-0.187) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.576 (+/-0.186) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.568 (+/-0.146) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.572 (+/-0.143) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.588 (+/-0.153) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.587 (+/-0.152) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.593 (+/-0.187) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.577 (+/-0.185) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.584 (+/-0.189) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.576 (+/-0.188) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.568 (+/-0.162) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.577 (+/-0.187) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.576 (+/-0.185) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.568 (+/-0.146) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.575 (+/-0.144) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.587 (+/-0.153) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.594 (+/-0.176) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.593 (+/-0.187) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.577 (+/-0.185) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.584 (+/-0.189) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.576 (+/-0.188) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.568 (+/-0.162) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.577 (+/-0.187) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.576 (+/-0.185) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.568 (+/-0.146) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.575 (+/-0.144) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.589 (+/-0.154) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.593 (+/-0.176) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.590 (+/-0.186) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.577 (+/-0.185) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.584 (+/-0.189) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.576 (+/-0.188) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.568 (+/-0.162) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.577 (+/-0.187) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.577 (+/-0.185) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.798 (+/-0.437) for {'C': 0.1, 'kernel': 'linear'}
0.660 (+/-0.219) for {'C': 1.0, 'kernel': 'linear'}
0.585 (+/-0.147) for {'C': 10.0, 'kernel': 'linear'}
0.579 (+/-0.189) for {'C': 100.0, 'kernel': 'linear'}
0.577 (+/-0.186) for {'C': 1000.0, 'kernel': 'linear'}
0.588 (+/-0.191) for {'C': 10000.0, 'kernel': 'linear'}
0.587 (+/-0.193) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.692 (+/-0.295) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.692 (+/-0.297) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.694 (+/-0.302) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.694 (+/-0.303) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.716 (+/-0.351) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.699 (+/-0.345) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.681 (+/-0.363) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.681 (+/-0.364) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.656 (+/-0.311) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.656 (+/-0.311) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.657 (+/-0.311) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.692 (+/-0.297) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.693 (+/-0.298) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.694 (+/-0.302) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.694 (+/-0.303) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.741 (+/-0.317) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.682 (+/-0.367) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.681 (+/-0.363) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.680 (+/-0.364) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.656 (+/-0.311) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.656 (+/-0.312) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.658 (+/-0.311) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.692 (+/-0.297) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.693 (+/-0.298) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.694 (+/-0.302) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.694 (+/-0.303) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.724 (+/-0.314) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.707 (+/-0.340) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.681 (+/-0.364) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.681 (+/-0.365) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.656 (+/-0.311) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.656 (+/-0.311) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.658 (+/-0.311) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.691 (+/-0.296) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.693 (+/-0.298) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.693 (+/-0.301) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.694 (+/-0.303) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.724 (+/-0.314) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.707 (+/-0.340) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.680 (+/-0.364) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.656 (+/-0.311) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.656 (+/-0.310) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.656 (+/-0.311) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.657 (+/-0.311) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.691 (+/-0.295) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.693 (+/-0.298) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.693 (+/-0.302) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.694 (+/-0.303) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.724 (+/-0.313) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.706 (+/-0.339) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.680 (+/-0.364) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.655 (+/-0.311) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.656 (+/-0.310) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.656 (+/-0.312) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.657 (+/-0.311) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.691 (+/-0.295) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.693 (+/-0.298) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.693 (+/-0.302) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.694 (+/-0.303) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.724 (+/-0.313) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.706 (+/-0.338) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.680 (+/-0.365) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.655 (+/-0.310) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.656 (+/-0.310) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.657 (+/-0.312) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.657 (+/-0.311) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.691 (+/-0.296) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.693 (+/-0.298) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.693 (+/-0.302) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.694 (+/-0.303) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.707 (+/-0.340) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.681 (+/-0.363) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.680 (+/-0.365) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.655 (+/-0.310) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.656 (+/-0.310) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.657 (+/-0.312) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.657 (+/-0.311) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.692 (+/-0.297) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.693 (+/-0.298) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.694 (+/-0.302) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.694 (+/-0.304) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.707 (+/-0.340) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.681 (+/-0.363) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.680 (+/-0.365) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.655 (+/-0.310) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.656 (+/-0.310) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.657 (+/-0.312) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.658 (+/-0.311) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.691 (+/-0.298) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.692 (+/-0.297) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.694 (+/-0.303) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.693 (+/-0.304) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.707 (+/-0.340) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.680 (+/-0.364) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.680 (+/-0.366) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.655 (+/-0.310) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.656 (+/-0.309) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.656 (+/-0.312) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.658 (+/-0.311) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.691 (+/-0.298) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.693 (+/-0.298) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.694 (+/-0.302) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.693 (+/-0.303) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.707 (+/-0.340) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.680 (+/-0.364) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.680 (+/-0.366) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.655 (+/-0.310) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.656 (+/-0.309) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.656 (+/-0.311) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.657 (+/-0.312) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.691 (+/-0.298) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.693 (+/-0.298) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.694 (+/-0.303) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.693 (+/-0.303) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.706 (+/-0.340) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.681 (+/-0.364) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.680 (+/-0.366) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.656 (+/-0.309) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.656 (+/-0.308) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.657 (+/-0.311) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.658 (+/-0.312) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.683 (+/-0.296) for {'C': 0.1, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 1.0, 'kernel': 'linear'}
0.694 (+/-0.301) for {'C': 10.0, 'kernel': 'linear'}
0.656 (+/-0.311) for {'C': 100.0, 'kernel': 'linear'}
0.658 (+/-0.312) for {'C': 1000.0, 'kernel': 'linear'}
0.659 (+/-0.313) for {'C': 10000.0, 'kernel': 'linear'}
0.659 (+/-0.313) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.05      0.17      0.07         6
          1       0.99      0.97      0.98       623

avg / total       0.98      0.96      0.97       629

本轮grid search结果，得到最好的参数选择是： {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.7405417800313299
第0轮loop中，tunemodel函数得到的最优参数是： ([{'C': array([2511886.43150958, 2558585.88690565, 2606153.5499989 ,
       2654605.56197554, 2703958.36410884, 2754228.70333816,
       2805433.63795172, 2857590.54337495, 2910717.11806661,
       2964831.38952434, 3019951.72040201]), 'kernel': ['rbf'], 'gamma': array([3.98107171e-06, 4.36515832e-06, 4.78630092e-06, 5.24807460e-06,
       5.75439937e-06, 6.30957344e-06, 6.91830971e-06, 7.58577575e-06,
       8.31763771e-06, 9.12010839e-06, 1.00000000e-05])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}], {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}, 0.7405417800313299)
这是第0次迭代微调C和gamma。
第0次迭代，得到delta： [1.22684300e+06 3.69042656e-06]
执行tunemodel()函数前，使用的grid search parameter是：
 [{'C': array([2511886.43150958, 2558585.88690565, 2606153.5499989 ,
       2654605.56197554, 2703958.36410884, 2754228.70333816,
       2805433.63795172, 2857590.54337495, 2910717.11806661,
       2964831.38952434, 3019951.72040201]), 'kernel': ['rbf'], 'gamma': array([3.98107171e-06, 4.36515832e-06, 4.78630092e-06, 5.24807460e-06,
       5.75439937e-06, 6.30957344e-06, 6.91830971e-06, 7.58577575e-06,
       8.31763771e-06, 9.12010839e-06, 1.00000000e-05])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.604 (+/-0.180) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.608 (+/-0.180) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.621 (+/-0.290) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.597 (+/-0.161) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.597 (+/-0.159) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.597 (+/-0.156) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.638 (+/-0.289) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.601 (+/-0.157) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.611 (+/-0.169) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.594 (+/-0.154) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.598 (+/-0.159) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.591 (+/-0.153) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.608 (+/-0.180) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.621 (+/-0.290) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.597 (+/-0.161) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.597 (+/-0.159) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.597 (+/-0.156) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.643 (+/-0.290) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.601 (+/-0.157) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.604 (+/-0.160) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.602 (+/-0.176) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.598 (+/-0.159) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.592 (+/-0.154) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.608 (+/-0.180) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.621 (+/-0.290) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.597 (+/-0.161) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.597 (+/-0.158) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.597 (+/-0.156) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.643 (+/-0.290) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.601 (+/-0.157) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.612 (+/-0.179) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.602 (+/-0.176) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.598 (+/-0.159) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.591 (+/-0.153) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.608 (+/-0.180) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.625 (+/-0.292) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.597 (+/-0.161) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.597 (+/-0.158) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.599 (+/-0.157) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.644 (+/-0.288) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.601 (+/-0.156) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.615 (+/-0.184) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.602 (+/-0.176) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.598 (+/-0.159) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.591 (+/-0.153) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.608 (+/-0.180) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.620 (+/-0.291) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.597 (+/-0.161) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.597 (+/-0.158) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.599 (+/-0.157) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.644 (+/-0.288) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.601 (+/-0.156) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.612 (+/-0.179) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.602 (+/-0.176) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.585 (+/-0.169) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.591 (+/-0.153) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.606 (+/-0.181) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.625 (+/-0.291) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.601 (+/-0.165) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.597 (+/-0.158) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.599 (+/-0.157) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.644 (+/-0.288) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.601 (+/-0.156) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.610 (+/-0.180) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.601 (+/-0.175) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.585 (+/-0.169) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.591 (+/-0.153) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.606 (+/-0.181) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.600 (+/-0.181) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.601 (+/-0.165) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.597 (+/-0.158) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.597 (+/-0.156) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.644 (+/-0.288) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.601 (+/-0.156) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.612 (+/-0.179) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.601 (+/-0.175) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.585 (+/-0.169) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.592 (+/-0.154) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.615 (+/-0.211) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.598 (+/-0.178) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.601 (+/-0.165) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.597 (+/-0.158) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.599 (+/-0.159) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.644 (+/-0.288) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.601 (+/-0.156) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.612 (+/-0.179) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.611 (+/-0.185) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.594 (+/-0.191) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.592 (+/-0.154) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.615 (+/-0.211) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.603 (+/-0.182) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.601 (+/-0.165) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.597 (+/-0.158) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.592 (+/-0.153) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.652 (+/-0.295) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.605 (+/-0.162) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.599 (+/-0.192) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.601 (+/-0.175) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.593 (+/-0.190) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.592 (+/-0.154) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.615 (+/-0.211) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.598 (+/-0.178) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.600 (+/-0.163) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.597 (+/-0.158) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.592 (+/-0.153) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.652 (+/-0.295) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.603 (+/-0.160) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.612 (+/-0.179) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.601 (+/-0.175) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.593 (+/-0.190) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.592 (+/-0.154) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.615 (+/-0.211) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.599 (+/-0.178) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.600 (+/-0.163) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.597 (+/-0.158) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.594 (+/-0.153) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.652 (+/-0.295) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.605 (+/-0.163) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.612 (+/-0.179) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.601 (+/-0.175) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.593 (+/-0.190) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.690 (+/-0.300) for {'C': 1.0, 'kernel': 'linear'}
0.599 (+/-0.162) for {'C': 10.0, 'kernel': 'linear'}
0.599 (+/-0.192) for {'C': 100.0, 'kernel': 'linear'}
0.580 (+/-0.187) for {'C': 1000.0, 'kernel': 'linear'}
0.590 (+/-0.191) for {'C': 10000.0, 'kernel': 'linear'}
0.588 (+/-0.193) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.695 (+/-0.302) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.696 (+/-0.305) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.695 (+/-0.301) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.696 (+/-0.304) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.696 (+/-0.304) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.695 (+/-0.301) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.696 (+/-0.307) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.696 (+/-0.305) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.696 (+/-0.306) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.695 (+/-0.303) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.679 (+/-0.291) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.695 (+/-0.302) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.696 (+/-0.305) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.695 (+/-0.301) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.696 (+/-0.304) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.696 (+/-0.304) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.695 (+/-0.301) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.696 (+/-0.308) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.696 (+/-0.305) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.679 (+/-0.294) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.695 (+/-0.303) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.679 (+/-0.291) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.695 (+/-0.302) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.696 (+/-0.305) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.695 (+/-0.301) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.695 (+/-0.304) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.695 (+/-0.304) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.695 (+/-0.301) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.696 (+/-0.308) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.696 (+/-0.305) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.680 (+/-0.294) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.695 (+/-0.303) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.679 (+/-0.291) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.695 (+/-0.302) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.696 (+/-0.305) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.695 (+/-0.301) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.695 (+/-0.304) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.695 (+/-0.304) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.695 (+/-0.301) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.697 (+/-0.308) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.696 (+/-0.304) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.680 (+/-0.294) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.695 (+/-0.303) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.679 (+/-0.291) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.695 (+/-0.302) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.696 (+/-0.305) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.694 (+/-0.300) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.695 (+/-0.304) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.695 (+/-0.304) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.695 (+/-0.301) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.697 (+/-0.308) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.696 (+/-0.304) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.680 (+/-0.294) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.695 (+/-0.304) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.662 (+/-0.311) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.695 (+/-0.302) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.696 (+/-0.306) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.695 (+/-0.301) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.696 (+/-0.304) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.695 (+/-0.304) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.695 (+/-0.301) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.697 (+/-0.308) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.696 (+/-0.304) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.679 (+/-0.294) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.695 (+/-0.303) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.662 (+/-0.311) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.695 (+/-0.302) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.696 (+/-0.306) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.694 (+/-0.301) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.696 (+/-0.304) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.695 (+/-0.304) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.695 (+/-0.301) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.697 (+/-0.308) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.696 (+/-0.304) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.680 (+/-0.294) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.695 (+/-0.303) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.662 (+/-0.311) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.695 (+/-0.302) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.696 (+/-0.307) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.694 (+/-0.301) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.696 (+/-0.304) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.695 (+/-0.304) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.695 (+/-0.301) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.697 (+/-0.308) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.696 (+/-0.304) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.680 (+/-0.294) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.696 (+/-0.306) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.662 (+/-0.312) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.695 (+/-0.302) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.696 (+/-0.307) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.695 (+/-0.302) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.696 (+/-0.304) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.695 (+/-0.304) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.678 (+/-0.288) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.697 (+/-0.308) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.696 (+/-0.305) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.663 (+/-0.314) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.695 (+/-0.303) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.662 (+/-0.311) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.695 (+/-0.302) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.696 (+/-0.307) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.694 (+/-0.301) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.696 (+/-0.304) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.695 (+/-0.304) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.678 (+/-0.288) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.697 (+/-0.308) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.696 (+/-0.304) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.680 (+/-0.294) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.695 (+/-0.303) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.662 (+/-0.311) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.695 (+/-0.302) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.696 (+/-0.307) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.694 (+/-0.302) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.696 (+/-0.304) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.695 (+/-0.304) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.679 (+/-0.290) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.697 (+/-0.308) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.696 (+/-0.305) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.680 (+/-0.294) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.695 (+/-0.303) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.662 (+/-0.311) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.699 (+/-0.307) for {'C': 1.0, 'kernel': 'linear'}
0.696 (+/-0.303) for {'C': 10.0, 'kernel': 'linear'}
0.663 (+/-0.314) for {'C': 100.0, 'kernel': 'linear'}
0.659 (+/-0.313) for {'C': 1000.0, 'kernel': 'linear'}
0.660 (+/-0.314) for {'C': 10000.0, 'kernel': 'linear'}
0.659 (+/-0.313) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6985561464259213
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.587 (+/-0.152) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.582 (+/-0.145) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.579 (+/-0.146) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.585 (+/-0.152) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.588 (+/-0.159) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.590 (+/-0.156) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.594 (+/-0.162) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.588 (+/-0.159) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.592 (+/-0.171) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.582 (+/-0.154) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.584 (+/-0.153) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.587 (+/-0.152) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.582 (+/-0.145) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.577 (+/-0.145) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.585 (+/-0.152) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.587 (+/-0.160) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.590 (+/-0.156) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.599 (+/-0.167) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.588 (+/-0.159) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.584 (+/-0.158) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.590 (+/-0.178) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.584 (+/-0.153) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.587 (+/-0.152) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.582 (+/-0.145) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.577 (+/-0.145) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.585 (+/-0.152) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.589 (+/-0.160) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.590 (+/-0.156) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.599 (+/-0.167) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.588 (+/-0.159) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.592 (+/-0.181) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.590 (+/-0.178) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.584 (+/-0.152) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.587 (+/-0.152) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.582 (+/-0.145) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.581 (+/-0.152) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.585 (+/-0.152) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.588 (+/-0.160) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.593 (+/-0.157) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.600 (+/-0.166) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.589 (+/-0.159) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.592 (+/-0.181) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.590 (+/-0.178) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.584 (+/-0.152) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.587 (+/-0.152) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.582 (+/-0.145) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.576 (+/-0.145) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.584 (+/-0.153) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.588 (+/-0.161) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.593 (+/-0.157) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.600 (+/-0.166) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.588 (+/-0.159) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.592 (+/-0.181) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.590 (+/-0.178) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.572 (+/-0.159) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.587 (+/-0.152) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.581 (+/-0.145) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.583 (+/-0.152) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.584 (+/-0.153) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.587 (+/-0.161) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.597 (+/-0.149) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.600 (+/-0.166) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.588 (+/-0.159) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.591 (+/-0.181) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.590 (+/-0.178) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.572 (+/-0.159) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.587 (+/-0.152) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.581 (+/-0.145) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.581 (+/-0.152) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.583 (+/-0.153) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.587 (+/-0.161) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.597 (+/-0.149) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.600 (+/-0.166) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.588 (+/-0.159) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.592 (+/-0.181) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.590 (+/-0.178) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.574 (+/-0.153) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.587 (+/-0.152) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.581 (+/-0.145) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.579 (+/-0.148) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.583 (+/-0.153) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.587 (+/-0.161) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.600 (+/-0.152) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.600 (+/-0.166) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.589 (+/-0.161) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.592 (+/-0.181) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.590 (+/-0.178) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.583 (+/-0.179) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.587 (+/-0.152) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.586 (+/-0.147) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.583 (+/-0.155) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.583 (+/-0.153) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.587 (+/-0.161) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.593 (+/-0.146) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.608 (+/-0.186) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.591 (+/-0.165) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.579 (+/-0.189) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.590 (+/-0.178) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.583 (+/-0.179) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.587 (+/-0.152) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.586 (+/-0.147) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.579 (+/-0.148) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.584 (+/-0.155) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.587 (+/-0.161) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.593 (+/-0.146) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.608 (+/-0.186) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.591 (+/-0.165) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.592 (+/-0.181) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.590 (+/-0.178) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.583 (+/-0.179) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.587 (+/-0.152) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.586 (+/-0.147) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.579 (+/-0.148) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.584 (+/-0.155) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.587 (+/-0.161) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.592 (+/-0.146) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.608 (+/-0.186) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.593 (+/-0.169) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.592 (+/-0.181) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.590 (+/-0.178) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.583 (+/-0.179) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.798 (+/-0.437) for {'C': 0.1, 'kernel': 'linear'}
0.660 (+/-0.219) for {'C': 1.0, 'kernel': 'linear'}
0.585 (+/-0.147) for {'C': 10.0, 'kernel': 'linear'}
0.579 (+/-0.189) for {'C': 100.0, 'kernel': 'linear'}
0.577 (+/-0.186) for {'C': 1000.0, 'kernel': 'linear'}
0.588 (+/-0.191) for {'C': 10000.0, 'kernel': 'linear'}
0.587 (+/-0.193) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.694 (+/-0.303) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.693 (+/-0.304) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.692 (+/-0.302) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.693 (+/-0.305) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.692 (+/-0.304) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.716 (+/-0.351) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.717 (+/-0.353) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.691 (+/-0.307) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.691 (+/-0.305) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.692 (+/-0.303) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.699 (+/-0.345) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.694 (+/-0.303) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.693 (+/-0.304) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.692 (+/-0.302) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.693 (+/-0.305) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.692 (+/-0.304) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.716 (+/-0.351) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.717 (+/-0.353) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.691 (+/-0.307) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.674 (+/-0.290) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.692 (+/-0.303) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.699 (+/-0.345) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.694 (+/-0.303) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.693 (+/-0.304) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.692 (+/-0.302) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.693 (+/-0.305) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.692 (+/-0.304) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.716 (+/-0.352) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.717 (+/-0.353) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.691 (+/-0.307) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.674 (+/-0.291) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.692 (+/-0.303) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.699 (+/-0.346) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.694 (+/-0.303) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.693 (+/-0.304) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.692 (+/-0.302) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.693 (+/-0.305) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.692 (+/-0.304) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.716 (+/-0.352) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.717 (+/-0.353) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.691 (+/-0.307) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.674 (+/-0.291) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.692 (+/-0.303) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.699 (+/-0.346) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.694 (+/-0.303) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.693 (+/-0.304) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.692 (+/-0.302) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.693 (+/-0.305) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.692 (+/-0.305) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.716 (+/-0.352) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.717 (+/-0.353) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.690 (+/-0.307) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.674 (+/-0.291) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.691 (+/-0.304) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.682 (+/-0.367) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.694 (+/-0.303) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.693 (+/-0.304) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.692 (+/-0.302) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.693 (+/-0.305) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.692 (+/-0.304) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.741 (+/-0.317) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.717 (+/-0.353) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.690 (+/-0.307) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.674 (+/-0.291) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.691 (+/-0.304) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.682 (+/-0.367) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.694 (+/-0.303) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.693 (+/-0.304) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.692 (+/-0.302) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.693 (+/-0.304) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.692 (+/-0.304) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.741 (+/-0.317) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.717 (+/-0.353) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.690 (+/-0.306) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.674 (+/-0.290) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.691 (+/-0.304) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.707 (+/-0.340) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.694 (+/-0.303) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.693 (+/-0.304) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.692 (+/-0.302) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.693 (+/-0.304) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.692 (+/-0.304) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.741 (+/-0.317) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.717 (+/-0.353) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.690 (+/-0.307) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.674 (+/-0.291) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.691 (+/-0.304) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.707 (+/-0.340) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.694 (+/-0.303) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.718 (+/-0.352) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.692 (+/-0.302) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.693 (+/-0.304) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.692 (+/-0.304) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.724 (+/-0.314) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.717 (+/-0.353) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.690 (+/-0.307) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.657 (+/-0.310) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.691 (+/-0.304) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.707 (+/-0.340) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.694 (+/-0.303) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.718 (+/-0.352) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.692 (+/-0.302) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.693 (+/-0.304) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.691 (+/-0.305) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.724 (+/-0.314) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.717 (+/-0.353) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.690 (+/-0.307) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.674 (+/-0.291) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.691 (+/-0.304) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.707 (+/-0.340) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.694 (+/-0.303) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.718 (+/-0.352) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.692 (+/-0.302) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.693 (+/-0.304) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.691 (+/-0.305) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.724 (+/-0.314) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.717 (+/-0.353) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.690 (+/-0.307) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.674 (+/-0.291) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.691 (+/-0.304) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.707 (+/-0.340) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.683 (+/-0.296) for {'C': 0.1, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 1.0, 'kernel': 'linear'}
0.694 (+/-0.301) for {'C': 10.0, 'kernel': 'linear'}
0.656 (+/-0.311) for {'C': 100.0, 'kernel': 'linear'}
0.658 (+/-0.312) for {'C': 1000.0, 'kernel': 'linear'}
0.659 (+/-0.313) for {'C': 10000.0, 'kernel': 'linear'}
0.659 (+/-0.313) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.05      0.17      0.07         6
          1       0.99      0.97      0.98       623

avg / total       0.98      0.96      0.97       629

本轮grid search结果，得到最好的参数选择是： {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.74070255173551
第1轮loop中，tunemodel函数得到的最优参数是： ([{'C': array([2805433.63795172, 2815788.29971032, 2826181.17981016,
       2836612.41931252, 2847082.15979929, 2857590.54337495,
       2868137.71266847, 2878723.81083525, 2889348.98155908,
       2900013.36905407, 2910717.11806661]), 'kernel': ['rbf'], 'gamma': array([5.75439937e-06, 5.86138165e-06, 5.97035287e-06, 6.08135001e-06,
       6.19441075e-06, 6.30957344e-06, 6.42687717e-06, 6.54636174e-06,
       6.66806769e-06, 6.79203633e-06, 6.91830971e-06])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}], {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}, 0.74070255173551)
这是第1次迭代微调C和gamma。
第1次迭代，得到delta： [103361.84003678      0.        ]
SVC模型clf_op_iter的参数是： {'kernel': 'rbf', 'decision_function_shape': 'ovr', 'class_weight': {-1: 15}, 'tol': 0.001, 'gamma': 6.309573444801928e-06, 'random_state': 0, 'cache_size': 200, 'verbose': False, 'degree': 3, 'C': 2857590.5433749487, 'probability': False, 'shrinking': True, 'coef0': 0.0, 'max_iter': -1}

训练模型SVC对训练样本的预测准确率： 0.9397590361445783

测试集中，预测为舞弊样本的有： (array([   8,   10,   11,   12,   13,   14,   17,   19,   20,   21,   22,
         24,   25,   30,   31,   32,   34,   35,   36,   37,   38,   39,
         40,   41,   42,   43,   44,   45,   46,   47,   51,   52,   53,
         54,   57,   58,   59,   60,   61,   62,   64,   65,   66,   67,
         71,   72,   73,   74,   75,   78,   79,   80,   88,   90,   91,
         92,   93,  107,  108,  109,  110,  111,  112,  113,  114,  115,
        116,  117,  118,  119,  120,  121,  123,  124,  126,  127,  128,
        129,  135,  136,  141,  142,  143,  144,  145,  146,  147,  148,
        149,  150,  151,  154,  155,  156,  157,  158,  162,  163,  165,
        166,  176,  177,  178,  179,  180,  181,  182,  183,  184,  185,
        186,  187,  190,  191,  192,  193,  195,  196,  197,  198,  199,
        200,  201,  202,  203,  204,  205,  206,  207,  208,  209,  212,
        213,  214,  215,  216,  225,  230,  231,  232,  233,  235,  236,
        237,  238,  239,  242,  243,  244,  245,  246,  247,  251,  252,
        253,  254,  255,  256,  257,  258,  259,  260,  261,  262,  264,
        267,  268,  269,  270,  275,  276,  277,  278,  280,  281,  283,
        284,  285,  286,  287,  288,  289,  295,  296,  298,  299,  300,
        301,  302,  306,  307,  309,  313,  316,  319,  320,  322,  323,
        324,  325,  330,  331,  332,  333,  334,  337,  338,  339,  340,
        347,  348,  349,  351,  353,  354,  356,  357,  358,  359,  360,
        361,  362,  363,  364,  365,  366,  367,  368,  369,  370,  372,
        383,  384,  385,  386,  387,  388,  391,  392,  395,  399,  403,
        404,  410,  411,  413,  414,  419,  420,  421,  422,  423,  429,
        430,  431,  432,  433,  434,  436,  438,  439,  440,  441,  442,
        443,  444,  446,  447,  448,  449,  450,  454,  455,  456,  457,
        460,  461,  462,  463,  464,  465,  466,  467,  468,  469,  471,
        472,  473,  474,  475,  476,  477,  478,  479,  480,  481,  482,
        483,  484,  485,  486,  487,  488,  489,  490,  491,  492,  493,
        496,  500,  501,  502,  503,  508,  509,  510,  511,  513,  514,
        515,  516,  517,  529,  532,  534,  535,  540,  541,  543,  544,
        545,  546,  549,  550,  551,  552,  555,  557,  558,  560,  561,
        562,  563,  564,  565,  566,  567,  568,  570,  571,  572,  573,
        578,  582,  583,  584,  586,  587,  589,  590,  591,  592,  593,
        594,  595,  599,  600,  601,  610,  611,  612,  613,  614,  615,
        616,  617,  618,  619,  620,  621,  622,  623,  624,  627,  628,
        629,  630,  631,  633,  634,  635,  641,  643,  646,  651,  652,
        653,  654,  655,  656,  657,  658,  659,  660,  661,  662,  663,
        664,  665,  666,  667,  668,  669,  674,  675,  676,  677,  684,
        686,  687,  688,  692,  694,  695,  696,  697,  698,  699,  701,
        702,  703,  706,  707,  708,  709,  710,  711,  712,  713,  714,
        715,  716,  717,  718,  719,  720,  721,  722,  723,  724,  725,
        727,  729,  735,  736,  742,  743,  750,  751,  752,  753,  754,
        756,  757,  758,  759,  760,  761,  769,  770,  774,  775,  776,
        777,  778,  785,  786,  787,  788,  789,  790,  792,  793,  794,
        795,  797,  806,  807,  810,  811,  812,  814,  815,  816,  817,
        818,  819,  820,  825,  826,  827,  828,  829,  830,  835,  840,
        841,  843,  844,  845,  850,  851,  853,  854,  856,  857,  859,
        860,  861,  862,  864,  872,  874,  875,  876,  877,  878,  879,
        880,  881,  882,  883,  884,  885,  886,  892,  893,  897,  898,
        900,  901,  902,  903,  904,  905,  906,  910,  911,  912,  913,
        914,  919,  923,  924,  925,  927,  928,  929,  930,  931,  932,
        933,  934,  935,  946,  947,  948,  949,  950,  951,  952,  953,
        954,  955,  956,  958,  959,  960,  961,  962,  963,  964,  965,
        966,  967,  970,  971,  976,  977,  978,  979,  980,  981,  982,
        983,  984,  985,  992,  993,  994,  995,  998, 1002, 1003, 1004,
       1005, 1006, 1008, 1009, 1011, 1012, 1013, 1014, 1015, 1016, 1017,
       1021, 1022, 1030, 1035, 1036, 1037, 1039, 1040, 1041, 1046, 1047,
       1048, 1049, 1050, 1051, 1055, 1056, 1057, 1060, 1061, 1065, 1067,
       1068, 1071, 1073, 1074, 1083, 1085, 1086, 1089, 1092, 1093, 1094,
       1095, 1096, 1097, 1098, 1100, 1101, 1102, 1103, 1105, 1106, 1107,
       1110, 1112, 1113, 1114, 1117, 1118, 1119, 1121, 1122, 1123, 1124,
       1125, 1127, 1129, 1131, 1132, 1134, 1135, 1137, 1140, 1141, 1142,
       1144, 1148, 1153, 1154, 1156, 1158, 1160, 1164, 1167, 1168, 1171,
       1172, 1175, 1180, 1183, 1184, 1191, 1194, 1196, 1200, 1204, 1205,
       1207, 1208, 1211, 1215, 1216, 1217, 1218, 1219, 1220, 1222, 1227,
       1230, 1231, 1232, 1233, 1234, 1236, 1237, 1238, 1239, 1240, 1241,
       1242, 1243, 1244, 1245, 1246, 1247, 1248, 1249, 1250, 1251, 1252,
       1254, 1255, 1256], dtype=int64),) 
测试集中，实际为舞弊样本的有： (array([1246, 1247, 1248, 1249, 1250, 1251, 1252, 1253, 1254, 1255, 1256],
      dtype=int64),) 
预测的舞弊样本数目： 740

训练模型SVC对测试样本的预测准确率： 0.47200566973777464
以上是第43次特征筛选。
第43次特征筛选，AUC值是： 0.6616080548664818
X_train_iter_svc.shape is: (1257, 9)
X_test_iter_svc.shape is: (1257, 9)
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.722 (+/-0.473) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.572 (+/-0.294) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.685 (+/-0.297) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.585 (+/-0.229) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.698 (+/-0.306) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6983958900156649
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.660 (+/-0.219) for {'C': 1.0, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.565 (+/-0.151) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.698 (+/-0.306) for {'C': 1.0, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.653 (+/-0.324) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6982356336054085
RBF的粗grid search最佳超参： {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001} RBF的粗grid search最高分值： 0.6983958900156649
linear的粗grid search最佳超参： {'C': 1.0, 'kernel': 'linear'} linear的粗grid search最高分值： 0.6982356336054085
粗grid search得到的parameter是：
 [{'C': array([1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03, 1.e+04, 1.e+05,
       1.e+06, 1.e+07, 1.e+08]), 'kernel': ['rbf'], 'gamma': array([1.e-08, 1.e-07, 1.e-06, 1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01,
       1.e+00, 1.e+01, 1.e+02])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.848 (+/-0.459) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.848 (+/-0.459) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.685 (+/-0.297) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.714 (+/-0.474) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.848 (+/-0.459) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.685 (+/-0.297) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.660 (+/-0.390) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.319) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.848 (+/-0.459) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.685 (+/-0.297) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.620 (+/-0.225) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.610 (+/-0.321) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.572 (+/-0.294) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.848 (+/-0.459) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.660 (+/-0.219) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.620 (+/-0.225) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.598 (+/-0.303) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.580 (+/-0.225) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.570 (+/-0.293) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.848 (+/-0.459) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.660 (+/-0.219) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.620 (+/-0.225) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.575 (+/-0.185) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.587 (+/-0.192) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.563 (+/-0.195) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.570 (+/-0.293) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.773 (+/-0.417) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.690 (+/-0.300) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.637 (+/-0.210) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.585 (+/-0.178) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.601 (+/-0.182) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.568 (+/-0.198) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.563 (+/-0.195) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.570 (+/-0.293) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.848 (+/-0.459) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.599 (+/-0.244) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.677 (+/-0.300) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.610 (+/-0.294) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.572 (+/-0.142) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.578 (+/-0.156) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.577 (+/-0.192) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.563 (+/-0.195) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.570 (+/-0.293) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.790 (+/-0.443) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.638 (+/-0.380) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.664 (+/-0.318) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.604 (+/-0.297) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.575 (+/-0.155) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.579 (+/-0.156) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.574 (+/-0.193) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.563 (+/-0.195) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.570 (+/-0.293) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.685 (+/-0.297) for {'C': 1.0, 'kernel': 'linear'}
0.641 (+/-0.211) for {'C': 10.0, 'kernel': 'linear'}
0.584 (+/-0.188) for {'C': 100.0, 'kernel': 'linear'}
0.576 (+/-0.185) for {'C': 1000.0, 'kernel': 'linear'}
0.599 (+/-0.183) for {'C': 10000.0, 'kernel': 'linear'}
0.602 (+/-0.181) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.658 (+/-0.217) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.002) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.498 (+/-0.005) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.658 (+/-0.217) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.698 (+/-0.306) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.005) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.658 (+/-0.217) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.306) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.236) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.004) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.002) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.658 (+/-0.217) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.698 (+/-0.306) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.234) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.585 (+/-0.229) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.005) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.002) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.658 (+/-0.217) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.698 (+/-0.306) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.234) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.636 (+/-0.231) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.586 (+/-0.233) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.585 (+/-0.228) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.005) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.002) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.658 (+/-0.217) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.698 (+/-0.306) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.234) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.660 (+/-0.308) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.628 (+/-0.226) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.585 (+/-0.231) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.585 (+/-0.227) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.005) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.002) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.683 (+/-0.296) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.699 (+/-0.307) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.673 (+/-0.238) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.677 (+/-0.288) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.694 (+/-0.305) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.592 (+/-0.262) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.585 (+/-0.231) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.585 (+/-0.227) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.005) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.002) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.658 (+/-0.217) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.628 (+/-0.272) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.682 (+/-0.294) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.677 (+/-0.284) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.672 (+/-0.289) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.687 (+/-0.312) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.266) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.585 (+/-0.231) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.585 (+/-0.227) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.005) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.002) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.699 (+/-0.306) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.619 (+/-0.210) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.665 (+/-0.315) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.675 (+/-0.282) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.687 (+/-0.309) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.688 (+/-0.311) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.265) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.585 (+/-0.231) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.585 (+/-0.227) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.005) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.002) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 1.0, 'kernel': 'linear'}
0.656 (+/-0.214) for {'C': 10.0, 'kernel': 'linear'}
0.662 (+/-0.310) for {'C': 100.0, 'kernel': 'linear'}
0.660 (+/-0.309) for {'C': 1000.0, 'kernel': 'linear'}
0.692 (+/-0.308) for {'C': 10000.0, 'kernel': 'linear'}
0.691 (+/-0.313) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6990374309506142
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.291) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.646 (+/-0.204) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.689 (+/-0.436) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.646 (+/-0.204) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.643 (+/-0.202) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.656 (+/-0.449) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.646 (+/-0.204) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.639 (+/-0.202) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.574 (+/-0.187) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.596 (+/-0.307) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.646 (+/-0.204) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.202) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.580 (+/-0.180) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.568 (+/-0.175) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.572 (+/-0.294) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.655 (+/-0.213) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.639 (+/-0.202) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.581 (+/-0.180) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.572 (+/-0.186) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.560 (+/-0.195) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.570 (+/-0.293) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.747 (+/-0.502) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.680 (+/-0.295) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.630 (+/-0.189) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.582 (+/-0.181) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.574 (+/-0.185) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.562 (+/-0.164) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.563 (+/-0.195) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.570 (+/-0.293) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.748 (+/-0.389) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.676 (+/-0.294) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.600 (+/-0.300) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.564 (+/-0.140) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.574 (+/-0.157) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.568 (+/-0.198) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.563 (+/-0.195) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.570 (+/-0.293) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.823 (+/-0.451) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.586 (+/-0.312) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.637 (+/-0.298) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.561 (+/-0.155) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.563 (+/-0.144) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.573 (+/-0.161) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.577 (+/-0.192) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.563 (+/-0.195) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.570 (+/-0.293) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.790 (+/-0.443) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.556 (+/-0.298) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.608 (+/-0.198) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.560 (+/-0.155) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.570 (+/-0.160) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.572 (+/-0.161) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.574 (+/-0.193) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.563 (+/-0.195) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.570 (+/-0.293) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.798 (+/-0.437) for {'C': 0.1, 'kernel': 'linear'}
0.660 (+/-0.219) for {'C': 1.0, 'kernel': 'linear'}
0.600 (+/-0.179) for {'C': 10.0, 'kernel': 'linear'}
0.577 (+/-0.179) for {'C': 100.0, 'kernel': 'linear'}
0.565 (+/-0.151) for {'C': 1000.0, 'kernel': 'linear'}
0.575 (+/-0.158) for {'C': 10000.0, 'kernel': 'linear'}
0.576 (+/-0.157) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.657 (+/-0.214) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.698 (+/-0.306) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.006) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.498 (+/-0.005) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.306) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.681 (+/-0.293) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.611 (+/-0.234) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.494 (+/-0.009) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.698 (+/-0.306) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.306) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.627 (+/-0.219) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.608 (+/-0.243) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.004) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.002) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.698 (+/-0.306) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.698 (+/-0.306) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.675 (+/-0.286) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.609 (+/-0.241) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.585 (+/-0.229) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.005) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.002) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.698 (+/-0.306) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.698 (+/-0.306) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.675 (+/-0.286) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.633 (+/-0.235) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.582 (+/-0.234) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.585 (+/-0.228) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.005) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.002) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.617 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.698 (+/-0.306) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.681 (+/-0.293) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.676 (+/-0.287) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.658 (+/-0.312) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.597 (+/-0.234) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.585 (+/-0.231) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.585 (+/-0.227) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.005) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.002) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.683 (+/-0.295) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.698 (+/-0.307) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.674 (+/-0.281) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.671 (+/-0.289) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.684 (+/-0.310) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.590 (+/-0.266) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.585 (+/-0.231) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.585 (+/-0.227) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.005) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.002) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.683 (+/-0.296) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.605 (+/-0.239) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.679 (+/-0.292) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.671 (+/-0.281) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.295) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.680 (+/-0.296) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.266) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.585 (+/-0.231) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.585 (+/-0.227) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.005) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.002) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.699 (+/-0.306) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.506 (+/-0.278) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.678 (+/-0.290) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.668 (+/-0.281) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.680 (+/-0.303) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.681 (+/-0.291) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.265) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.585 (+/-0.231) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.585 (+/-0.227) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.005) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.002) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.658 (+/-0.216) for {'C': 0.1, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 1.0, 'kernel': 'linear'}
0.679 (+/-0.291) for {'C': 10.0, 'kernel': 'linear'}
0.675 (+/-0.286) for {'C': 100.0, 'kernel': 'linear'}
0.653 (+/-0.324) for {'C': 1000.0, 'kernel': 'linear'}
0.686 (+/-0.314) for {'C': 10000.0, 'kernel': 'linear'}
0.686 (+/-0.313) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6990374309506142
发现最优参数C为原先的最大/最小值，直接重新设置超参。
发现最优参数gamma为原先的最大/最小值，直接重新设置超参。
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-09}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.848 (+/-0.459) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.685 (+/-0.297) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-09}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.848 (+/-0.459) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.660 (+/-0.219) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.620 (+/-0.225) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-09}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.848 (+/-0.459) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.660 (+/-0.219) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.620 (+/-0.225) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.575 (+/-0.185) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-09}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.773 (+/-0.417) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.690 (+/-0.300) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.637 (+/-0.210) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.585 (+/-0.178) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.601 (+/-0.182) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-09}
0.848 (+/-0.459) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.599 (+/-0.244) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.677 (+/-0.300) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.610 (+/-0.294) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.572 (+/-0.142) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.578 (+/-0.156) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-10}
0.848 (+/-0.459) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-09}
0.790 (+/-0.443) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.638 (+/-0.380) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.664 (+/-0.318) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.604 (+/-0.297) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.575 (+/-0.155) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.579 (+/-0.156) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 1e-11}
0.848 (+/-0.459) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 1e-10}
0.790 (+/-0.443) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 1e-09}
0.787 (+/-0.446) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.638 (+/-0.381) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.664 (+/-0.318) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.604 (+/-0.297) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.576 (+/-0.156) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.577 (+/-0.157) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 1e-12}
0.848 (+/-0.459) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 1e-11}
0.790 (+/-0.443) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 1e-10}
0.787 (+/-0.446) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 1e-09}
0.787 (+/-0.446) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.636 (+/-0.382) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.664 (+/-0.318) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.604 (+/-0.297) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.576 (+/-0.156) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.578 (+/-0.157) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 1e-13}
0.848 (+/-0.459) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 1e-12}
0.790 (+/-0.443) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 1e-11}
0.787 (+/-0.446) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 1e-10}
0.787 (+/-0.446) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 1e-09}
0.787 (+/-0.446) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.638 (+/-0.381) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.664 (+/-0.318) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.604 (+/-0.297) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.576 (+/-0.156) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.577 (+/-0.157) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.848 (+/-0.459) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 1e-13}
0.790 (+/-0.443) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 1e-12}
0.787 (+/-0.446) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 1e-11}
0.787 (+/-0.446) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 1e-10}
0.787 (+/-0.446) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 1e-09}
0.787 (+/-0.446) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.638 (+/-0.381) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.664 (+/-0.318) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.604 (+/-0.297) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.576 (+/-0.156) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.576 (+/-0.157) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.790 (+/-0.443) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 1e-13}
0.787 (+/-0.446) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 1e-12}
0.787 (+/-0.446) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 1e-11}
0.787 (+/-0.446) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 1e-10}
0.787 (+/-0.446) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 1e-09}
0.787 (+/-0.446) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.638 (+/-0.381) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.664 (+/-0.318) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.604 (+/-0.297) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.576 (+/-0.156) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.578 (+/-0.157) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.685 (+/-0.297) for {'C': 1.0, 'kernel': 'linear'}
0.641 (+/-0.211) for {'C': 10.0, 'kernel': 'linear'}
0.584 (+/-0.188) for {'C': 100.0, 'kernel': 'linear'}
0.576 (+/-0.185) for {'C': 1000.0, 'kernel': 'linear'}
0.599 (+/-0.183) for {'C': 10000.0, 'kernel': 'linear'}
0.602 (+/-0.181) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-09}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.658 (+/-0.217) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.698 (+/-0.306) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-09}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.658 (+/-0.217) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.698 (+/-0.306) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.234) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-09}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.658 (+/-0.217) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.698 (+/-0.306) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.234) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.660 (+/-0.308) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-09}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.683 (+/-0.296) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.699 (+/-0.307) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.673 (+/-0.238) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.677 (+/-0.288) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.694 (+/-0.305) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-09}
0.658 (+/-0.217) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.628 (+/-0.272) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.682 (+/-0.294) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.677 (+/-0.284) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.672 (+/-0.289) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.687 (+/-0.312) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-10}
0.658 (+/-0.217) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-09}
0.699 (+/-0.306) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.619 (+/-0.210) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.665 (+/-0.315) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.675 (+/-0.282) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.687 (+/-0.309) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.688 (+/-0.311) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 1e-11}
0.658 (+/-0.217) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 1e-10}
0.699 (+/-0.306) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 1e-09}
0.699 (+/-0.305) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.616 (+/-0.212) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.665 (+/-0.315) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.675 (+/-0.282) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.686 (+/-0.310) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.687 (+/-0.310) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 1e-12}
0.658 (+/-0.217) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 1e-11}
0.699 (+/-0.306) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 1e-10}
0.699 (+/-0.305) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 1e-09}
0.699 (+/-0.305) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.615 (+/-0.211) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.665 (+/-0.315) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.675 (+/-0.282) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.686 (+/-0.308) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.687 (+/-0.311) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 1e-13}
0.658 (+/-0.217) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 1e-12}
0.699 (+/-0.306) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 1e-11}
0.699 (+/-0.305) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 1e-10}
0.699 (+/-0.305) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 1e-09}
0.699 (+/-0.305) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.615 (+/-0.212) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.665 (+/-0.315) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.675 (+/-0.282) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.686 (+/-0.310) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.687 (+/-0.310) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.658 (+/-0.217) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 1e-13}
0.699 (+/-0.306) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 1e-12}
0.699 (+/-0.305) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 1e-11}
0.699 (+/-0.305) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 1e-10}
0.699 (+/-0.305) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 1e-09}
0.699 (+/-0.305) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.615 (+/-0.212) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.665 (+/-0.315) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.675 (+/-0.282) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.686 (+/-0.310) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.687 (+/-0.308) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.699 (+/-0.306) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 1e-13}
0.699 (+/-0.305) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 1e-12}
0.699 (+/-0.305) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 1e-11}
0.699 (+/-0.305) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 1e-10}
0.699 (+/-0.305) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 1e-09}
0.699 (+/-0.305) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.617 (+/-0.208) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.665 (+/-0.315) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.675 (+/-0.282) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.686 (+/-0.309) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.687 (+/-0.311) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 1.0, 'kernel': 'linear'}
0.656 (+/-0.214) for {'C': 10.0, 'kernel': 'linear'}
0.662 (+/-0.310) for {'C': 100.0, 'kernel': 'linear'}
0.660 (+/-0.309) for {'C': 1000.0, 'kernel': 'linear'}
0.692 (+/-0.308) for {'C': 10000.0, 'kernel': 'linear'}
0.691 (+/-0.313) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6990374309506142
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-09}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.646 (+/-0.204) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.202) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-09}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.655 (+/-0.213) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.639 (+/-0.202) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.581 (+/-0.180) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-09}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.747 (+/-0.502) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.680 (+/-0.295) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.630 (+/-0.189) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.582 (+/-0.181) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.574 (+/-0.185) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-09}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.748 (+/-0.389) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.676 (+/-0.294) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.600 (+/-0.300) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.564 (+/-0.140) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.574 (+/-0.157) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-09}
0.823 (+/-0.451) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.586 (+/-0.312) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.637 (+/-0.298) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.561 (+/-0.155) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.563 (+/-0.144) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.573 (+/-0.161) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-10}
0.823 (+/-0.451) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-09}
0.790 (+/-0.443) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.556 (+/-0.298) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.608 (+/-0.198) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.560 (+/-0.155) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.570 (+/-0.160) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.572 (+/-0.161) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 1e-11}
0.823 (+/-0.451) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 1e-10}
0.790 (+/-0.443) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 1e-09}
0.790 (+/-0.443) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.552 (+/-0.298) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.607 (+/-0.199) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.561 (+/-0.155) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.572 (+/-0.160) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.572 (+/-0.161) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 1e-12}
0.823 (+/-0.451) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 1e-11}
0.790 (+/-0.443) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 1e-10}
0.790 (+/-0.443) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 1e-09}
0.790 (+/-0.443) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.552 (+/-0.298) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.607 (+/-0.199) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.561 (+/-0.155) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.572 (+/-0.160) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.572 (+/-0.161) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 1e-13}
0.823 (+/-0.451) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 1e-12}
0.790 (+/-0.443) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 1e-11}
0.790 (+/-0.443) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 1e-10}
0.790 (+/-0.443) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 1e-09}
0.790 (+/-0.443) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.552 (+/-0.298) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.607 (+/-0.199) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.561 (+/-0.155) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.572 (+/-0.160) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.572 (+/-0.161) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.823 (+/-0.451) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 1e-13}
0.790 (+/-0.443) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 1e-12}
0.790 (+/-0.443) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 1e-11}
0.790 (+/-0.443) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 1e-10}
0.790 (+/-0.443) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 1e-09}
0.790 (+/-0.443) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.552 (+/-0.298) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.607 (+/-0.199) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.561 (+/-0.155) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.572 (+/-0.160) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.572 (+/-0.161) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.790 (+/-0.443) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 1e-13}
0.790 (+/-0.443) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 1e-12}
0.790 (+/-0.443) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 1e-11}
0.790 (+/-0.443) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 1e-10}
0.790 (+/-0.443) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 1e-09}
0.790 (+/-0.443) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.554 (+/-0.296) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.605 (+/-0.200) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.561 (+/-0.155) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.572 (+/-0.160) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.572 (+/-0.161) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.798 (+/-0.437) for {'C': 0.1, 'kernel': 'linear'}
0.660 (+/-0.219) for {'C': 1.0, 'kernel': 'linear'}
0.600 (+/-0.179) for {'C': 10.0, 'kernel': 'linear'}
0.577 (+/-0.179) for {'C': 100.0, 'kernel': 'linear'}
0.565 (+/-0.151) for {'C': 1000.0, 'kernel': 'linear'}
0.575 (+/-0.158) for {'C': 10000.0, 'kernel': 'linear'}
0.576 (+/-0.157) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-09}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.698 (+/-0.306) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.698 (+/-0.306) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-09}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.698 (+/-0.306) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.698 (+/-0.306) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.675 (+/-0.286) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-09}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.617 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.698 (+/-0.306) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.681 (+/-0.293) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.676 (+/-0.287) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.658 (+/-0.312) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-09}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.683 (+/-0.295) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.698 (+/-0.307) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.674 (+/-0.281) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.671 (+/-0.289) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.684 (+/-0.310) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-09}
0.683 (+/-0.296) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.605 (+/-0.239) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.679 (+/-0.292) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.671 (+/-0.281) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.295) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.680 (+/-0.296) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-10}
0.683 (+/-0.296) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-09}
0.699 (+/-0.306) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.506 (+/-0.278) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.678 (+/-0.290) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.668 (+/-0.281) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.680 (+/-0.303) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.681 (+/-0.291) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 1e-11}
0.683 (+/-0.296) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 1e-10}
0.699 (+/-0.306) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 1e-09}
0.699 (+/-0.306) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.499 (+/-0.283) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.678 (+/-0.289) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.668 (+/-0.282) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.680 (+/-0.303) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.681 (+/-0.292) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 1e-12}
0.683 (+/-0.296) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 1e-11}
0.699 (+/-0.306) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 1e-10}
0.699 (+/-0.306) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 1e-09}
0.699 (+/-0.306) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.498 (+/-0.283) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.678 (+/-0.289) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.668 (+/-0.282) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.680 (+/-0.303) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.681 (+/-0.292) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 1e-13}
0.683 (+/-0.296) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 1e-12}
0.699 (+/-0.306) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 1e-11}
0.699 (+/-0.306) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 1e-10}
0.699 (+/-0.306) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 1e-09}
0.699 (+/-0.306) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.498 (+/-0.284) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.678 (+/-0.289) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.668 (+/-0.282) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.680 (+/-0.303) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.681 (+/-0.292) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.683 (+/-0.296) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 1e-13}
0.699 (+/-0.306) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 1e-12}
0.699 (+/-0.306) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 1e-11}
0.699 (+/-0.306) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 1e-10}
0.699 (+/-0.306) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 1e-09}
0.699 (+/-0.306) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.498 (+/-0.284) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.678 (+/-0.289) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.668 (+/-0.282) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.680 (+/-0.303) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.681 (+/-0.292) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.699 (+/-0.306) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 1e-13}
0.699 (+/-0.306) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 1e-12}
0.699 (+/-0.306) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 1e-11}
0.699 (+/-0.306) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 1e-10}
0.699 (+/-0.306) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 1e-09}
0.699 (+/-0.306) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.520 (+/-0.313) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.678 (+/-0.288) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.668 (+/-0.282) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.680 (+/-0.303) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.681 (+/-0.292) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.658 (+/-0.216) for {'C': 0.1, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 1.0, 'kernel': 'linear'}
0.679 (+/-0.291) for {'C': 10.0, 'kernel': 'linear'}
0.675 (+/-0.286) for {'C': 100.0, 'kernel': 'linear'}
0.653 (+/-0.324) for {'C': 1000.0, 'kernel': 'linear'}
0.686 (+/-0.314) for {'C': 10000.0, 'kernel': 'linear'}
0.686 (+/-0.313) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6990374309506142
循环迭代之前，delta is: [0. 0.]
SVC模型clf_op_iter的参数是： {'kernel': 'rbf', 'decision_function_shape': 'ovr', 'class_weight': {-1: 15}, 'tol': 0.001, 'gamma': 1e-08, 'random_state': 0, 'cache_size': 200, 'verbose': False, 'degree': 3, 'C': 100000000.0, 'probability': False, 'shrinking': True, 'coef0': 0.0, 'max_iter': -1}

训练模型SVC对训练样本的预测准确率： 0.9043231750531537

测试集中，预测为舞弊样本的有： (array([ 370, 1247, 1248], dtype=int64),) 
测试集中，实际为舞弊样本的有： (array([1246, 1247, 1248, 1249, 1250, 1251, 1252, 1253, 1254, 1255, 1256],
      dtype=int64),) 
预测的舞弊样本数目： 3

训练模型SVC对测试样本的预测准确率： 0.9036144578313253
以上是第44次特征筛选。
第44次特征筛选，AUC值是： 0.5905078067999416
X_train_iter_svc.shape is: (1257, 8)
X_test_iter_svc.shape is: (1257, 8)
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.639 (+/-0.327) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.550 (+/-0.157) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.630 (+/-0.189) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      1.00       623

avg / total       0.98      0.99      0.99       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.586 (+/-0.229) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.681 (+/-0.293) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.33      0.17      0.22         6
          1       0.99      1.00      0.99       623

avg / total       0.99      0.99      0.99       629

本轮grid search结果，得到最好的参数选择是： {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6810881977079727
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.630 (+/-0.189) for {'C': 1.0, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.573 (+/-0.146) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.33      0.17      0.22         6
          1       0.99      1.00      0.99       623

avg / total       0.99      0.99      0.99       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.698 (+/-0.306) for {'C': 1.0, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.670 (+/-0.306) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.33      0.17      0.22         6
          1       0.99      1.00      0.99       623

avg / total       0.99      0.99      0.99       629

本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.697594607964383
RBF的粗grid search最佳超参： {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001} RBF的粗grid search最高分值： 0.6810881977079727
linear的粗grid search最佳超参： {'C': 1.0, 'kernel': 'linear'} linear的粗grid search最高分值： 0.697594607964383
粗grid search得到的parameter是：
 [{'C': array([1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03, 1.e+04, 1.e+05,
       1.e+06, 1.e+07, 1.e+08]), 'kernel': ['rbf'], 'gamma': array([1.e-08, 1.e-07, 1.e-06, 1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01,
       1.e+00, 1.e+01, 1.e+02])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.327) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.689 (+/-0.436) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.635 (+/-0.198) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.689 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.689 (+/-0.436) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.630 (+/-0.189) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.635 (+/-0.326) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.564 (+/-0.195) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.689 (+/-0.436) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.630 (+/-0.189) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.603 (+/-0.195) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.609 (+/-0.322) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.550 (+/-0.157) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.689 (+/-0.436) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.630 (+/-0.189) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.599 (+/-0.192) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.577 (+/-0.153) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.580 (+/-0.225) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.547 (+/-0.154) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.689 (+/-0.436) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.643 (+/-0.202) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.603 (+/-0.195) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.573 (+/-0.152) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.621 (+/-0.312) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.561 (+/-0.195) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.547 (+/-0.154) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.639 (+/-0.285) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.634 (+/-0.190) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.620 (+/-0.184) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.577 (+/-0.155) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.594 (+/-0.178) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.547 (+/-0.171) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.561 (+/-0.195) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.547 (+/-0.154) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.797 (+/-0.492) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.624 (+/-0.212) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.623 (+/-0.184) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.580 (+/-0.155) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.595 (+/-0.177) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.592 (+/-0.168) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.553 (+/-0.167) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.561 (+/-0.195) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.547 (+/-0.154) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.779 (+/-0.452) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.648 (+/-0.320) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.614 (+/-0.182) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.577 (+/-0.154) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.618 (+/-0.201) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.592 (+/-0.172) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.552 (+/-0.167) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.561 (+/-0.195) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.547 (+/-0.154) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.643 (+/-0.202) for {'C': 1.0, 'kernel': 'linear'}
0.599 (+/-0.192) for {'C': 10.0, 'kernel': 'linear'}
0.593 (+/-0.178) for {'C': 100.0, 'kernel': 'linear'}
0.587 (+/-0.179) for {'C': 1000.0, 'kernel': 'linear'}
0.607 (+/-0.184) for {'C': 10000.0, 'kernel': 'linear'}
0.612 (+/-0.181) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      1.00       623

avg / total       0.98      0.99      0.99       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.002) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.497 (+/-0.006) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.214) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.004) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.003) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.681 (+/-0.293) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.589 (+/-0.226) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.007) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.003) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.681 (+/-0.293) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.639 (+/-0.233) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.586 (+/-0.229) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.007) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.003) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.681 (+/-0.293) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.233) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.677 (+/-0.288) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.587 (+/-0.232) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.586 (+/-0.227) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.007) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.003) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.681 (+/-0.293) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.639 (+/-0.233) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.677 (+/-0.287) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.628 (+/-0.227) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.585 (+/-0.230) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.586 (+/-0.227) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.007) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.003) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.681 (+/-0.294) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.681 (+/-0.293) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.681 (+/-0.292) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.677 (+/-0.287) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.693 (+/-0.303) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.566 (+/-0.245) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.585 (+/-0.229) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.586 (+/-0.227) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.007) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.003) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.642 (+/-0.236) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.681 (+/-0.255) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.681 (+/-0.293) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.693 (+/-0.298) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.677 (+/-0.297) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.690 (+/-0.311) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.590 (+/-0.261) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.585 (+/-0.230) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.586 (+/-0.227) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.007) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.003) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.682 (+/-0.293) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.658 (+/-0.243) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.680 (+/-0.290) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.692 (+/-0.297) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.693 (+/-0.314) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.688 (+/-0.312) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.589 (+/-0.260) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.585 (+/-0.230) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.586 (+/-0.227) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.007) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.003) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.681 (+/-0.293) for {'C': 1.0, 'kernel': 'linear'}
0.639 (+/-0.233) for {'C': 10.0, 'kernel': 'linear'}
0.678 (+/-0.289) for {'C': 100.0, 'kernel': 'linear'}
0.677 (+/-0.290) for {'C': 1000.0, 'kernel': 'linear'}
0.693 (+/-0.310) for {'C': 10000.0, 'kernel': 'linear'}
0.694 (+/-0.310) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.36      0.83      0.50         6
          1       1.00      0.99      0.99       623

avg / total       0.99      0.98      0.99       629

本轮grid search结果，得到最好的参数选择是： {'C': 100000.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6935866518262017
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.618 (+/-0.214) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.630 (+/-0.192) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.327) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.618 (+/-0.172) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.623 (+/-0.183) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.631 (+/-0.395) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.618 (+/-0.172) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.167) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.581 (+/-0.180) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.549 (+/-0.155) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.625 (+/-0.179) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.610 (+/-0.163) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.582 (+/-0.180) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.568 (+/-0.175) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.550 (+/-0.157) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.618 (+/-0.172) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.614 (+/-0.167) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.582 (+/-0.180) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.579 (+/-0.178) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.555 (+/-0.199) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.547 (+/-0.154) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.747 (+/-0.502) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.622 (+/-0.174) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.614 (+/-0.169) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.582 (+/-0.180) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.570 (+/-0.152) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.571 (+/-0.191) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.561 (+/-0.195) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.547 (+/-0.154) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.597 (+/-0.177) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.610 (+/-0.166) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.583 (+/-0.182) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.567 (+/-0.144) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.569 (+/-0.148) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.547 (+/-0.171) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.561 (+/-0.195) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.547 (+/-0.154) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.798 (+/-0.437) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.530 (+/-0.146) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.586 (+/-0.155) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.561 (+/-0.146) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.571 (+/-0.147) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.584 (+/-0.176) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.553 (+/-0.167) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.561 (+/-0.195) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.547 (+/-0.154) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.775 (+/-0.457) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.550 (+/-0.298) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.578 (+/-0.149) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.558 (+/-0.144) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.592 (+/-0.189) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.586 (+/-0.177) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.552 (+/-0.167) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.561 (+/-0.195) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.547 (+/-0.154) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.764 (+/-0.421) for {'C': 0.1, 'kernel': 'linear'}
0.630 (+/-0.189) for {'C': 1.0, 'kernel': 'linear'}
0.603 (+/-0.179) for {'C': 10.0, 'kernel': 'linear'}
0.568 (+/-0.153) for {'C': 100.0, 'kernel': 'linear'}
0.573 (+/-0.146) for {'C': 1000.0, 'kernel': 'linear'}
0.579 (+/-0.159) for {'C': 10000.0, 'kernel': 'linear'}
0.585 (+/-0.161) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      1.00       623

avg / total       0.98      0.99      0.99       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.640 (+/-0.234) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.306) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.006) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.497 (+/-0.006) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.681 (+/-0.293) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.214) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.611 (+/-0.234) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.494 (+/-0.009) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.003) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.681 (+/-0.293) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.681 (+/-0.292) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.652 (+/-0.207) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.583 (+/-0.232) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.007) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.003) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.306) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.680 (+/-0.292) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.676 (+/-0.286) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.609 (+/-0.241) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.585 (+/-0.230) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.007) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.003) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.681 (+/-0.293) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.681 (+/-0.292) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.676 (+/-0.286) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.675 (+/-0.290) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.555 (+/-0.218) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.586 (+/-0.227) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.007) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.003) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.617 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.681 (+/-0.293) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.681 (+/-0.293) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.676 (+/-0.286) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.674 (+/-0.290) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.596 (+/-0.236) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.585 (+/-0.230) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.586 (+/-0.227) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.007) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.003) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.679 (+/-0.293) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.697 (+/-0.303) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.692 (+/-0.298) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.672 (+/-0.294) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.684 (+/-0.313) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.564 (+/-0.249) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.585 (+/-0.229) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.586 (+/-0.227) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.007) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.003) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.658 (+/-0.216) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.581 (+/-0.278) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.694 (+/-0.298) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.687 (+/-0.287) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.667 (+/-0.305) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.682 (+/-0.294) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.590 (+/-0.261) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.585 (+/-0.230) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.586 (+/-0.227) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.007) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.003) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.682 (+/-0.293) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.537 (+/-0.183) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.692 (+/-0.292) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.686 (+/-0.286) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.683 (+/-0.315) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.682 (+/-0.295) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.589 (+/-0.260) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.585 (+/-0.230) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.586 (+/-0.227) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.007) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.003) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.658 (+/-0.216) for {'C': 0.1, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 1.0, 'kernel': 'linear'}
0.679 (+/-0.291) for {'C': 10.0, 'kernel': 'linear'}
0.675 (+/-0.285) for {'C': 100.0, 'kernel': 'linear'}
0.670 (+/-0.306) for {'C': 1000.0, 'kernel': 'linear'}
0.685 (+/-0.315) for {'C': 10000.0, 'kernel': 'linear'}
0.687 (+/-0.313) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.33      0.17      0.22         6
          1       0.99      1.00      0.99       623

avg / total       0.99      0.99      0.99       629

本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.697594607964383
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.327) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.689 (+/-0.436) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.635 (+/-0.198) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.689 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.689 (+/-0.436) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.630 (+/-0.189) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.635 (+/-0.326) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.564 (+/-0.195) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.689 (+/-0.436) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.630 (+/-0.189) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.603 (+/-0.195) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.609 (+/-0.322) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.550 (+/-0.157) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.689 (+/-0.436) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.630 (+/-0.189) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.599 (+/-0.192) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.577 (+/-0.153) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.580 (+/-0.225) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.547 (+/-0.154) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.689 (+/-0.436) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.643 (+/-0.202) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.603 (+/-0.195) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.573 (+/-0.152) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.621 (+/-0.312) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.561 (+/-0.195) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.547 (+/-0.154) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.639 (+/-0.285) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.634 (+/-0.190) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.620 (+/-0.184) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.577 (+/-0.155) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.594 (+/-0.178) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.547 (+/-0.171) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.561 (+/-0.195) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.547 (+/-0.154) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.797 (+/-0.492) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.624 (+/-0.212) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.623 (+/-0.184) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.580 (+/-0.155) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.595 (+/-0.177) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.592 (+/-0.168) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.553 (+/-0.167) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.561 (+/-0.195) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.547 (+/-0.154) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.779 (+/-0.452) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.648 (+/-0.320) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.614 (+/-0.182) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.577 (+/-0.154) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.618 (+/-0.201) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.592 (+/-0.172) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.552 (+/-0.167) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.561 (+/-0.195) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.547 (+/-0.154) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.789 (+/-0.443) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.648 (+/-0.209) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.643 (+/-0.202) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.643 (+/-0.202) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.639 (+/-0.201) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.630 (+/-0.189) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.623 (+/-0.183) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.616 (+/-0.181) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.599 (+/-0.192) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      1.00       623

avg / total       0.98      0.99      0.99       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.002) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.497 (+/-0.006) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.214) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.004) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.003) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.681 (+/-0.293) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.589 (+/-0.226) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.007) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.003) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.681 (+/-0.293) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.639 (+/-0.233) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.586 (+/-0.229) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.007) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.003) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.681 (+/-0.293) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.233) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.677 (+/-0.288) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.587 (+/-0.232) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.586 (+/-0.227) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.007) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.003) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.681 (+/-0.293) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.639 (+/-0.233) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.677 (+/-0.287) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.628 (+/-0.227) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.585 (+/-0.230) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.586 (+/-0.227) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.007) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.003) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.681 (+/-0.294) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.681 (+/-0.293) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.681 (+/-0.292) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.677 (+/-0.287) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.693 (+/-0.303) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.566 (+/-0.245) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.585 (+/-0.229) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.586 (+/-0.227) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.007) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.003) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.642 (+/-0.236) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.681 (+/-0.255) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.681 (+/-0.293) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.693 (+/-0.298) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.677 (+/-0.297) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.690 (+/-0.311) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.590 (+/-0.261) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.585 (+/-0.230) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.586 (+/-0.227) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.007) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.003) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.682 (+/-0.293) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.658 (+/-0.243) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.680 (+/-0.290) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.692 (+/-0.297) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.693 (+/-0.314) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.688 (+/-0.312) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.589 (+/-0.260) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.585 (+/-0.230) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.586 (+/-0.227) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.007) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.003) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.658 (+/-0.216) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.657 (+/-0.215) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.698 (+/-0.305) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.681 (+/-0.293) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.681 (+/-0.293) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.681 (+/-0.293) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.656 (+/-0.214) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.656 (+/-0.213) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.639 (+/-0.233) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 0.6309573444801931, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6977543490807157
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.618 (+/-0.214) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.630 (+/-0.192) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.327) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.618 (+/-0.172) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.623 (+/-0.183) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.631 (+/-0.395) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.618 (+/-0.172) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.167) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.581 (+/-0.180) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.549 (+/-0.155) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.625 (+/-0.179) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.610 (+/-0.163) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.582 (+/-0.180) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.568 (+/-0.175) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.550 (+/-0.157) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.618 (+/-0.172) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.614 (+/-0.167) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.582 (+/-0.180) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.579 (+/-0.178) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.555 (+/-0.199) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.547 (+/-0.154) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.747 (+/-0.502) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.622 (+/-0.174) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.614 (+/-0.169) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.582 (+/-0.180) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.570 (+/-0.152) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.571 (+/-0.191) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.561 (+/-0.195) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.547 (+/-0.154) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.597 (+/-0.177) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.610 (+/-0.166) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.583 (+/-0.182) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.567 (+/-0.144) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.569 (+/-0.148) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.547 (+/-0.171) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.561 (+/-0.195) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.547 (+/-0.154) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.798 (+/-0.437) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.530 (+/-0.146) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.586 (+/-0.155) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.561 (+/-0.146) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.571 (+/-0.147) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.584 (+/-0.176) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.553 (+/-0.167) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.561 (+/-0.195) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.547 (+/-0.154) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.775 (+/-0.457) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.550 (+/-0.298) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.578 (+/-0.149) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.558 (+/-0.144) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.592 (+/-0.189) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.586 (+/-0.177) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.552 (+/-0.167) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.561 (+/-0.195) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.547 (+/-0.154) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.764 (+/-0.421) for {'C': 0.1, 'kernel': 'linear'}
0.614 (+/-0.179) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.630 (+/-0.180) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.627 (+/-0.185) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.630 (+/-0.189) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.630 (+/-0.189) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.619 (+/-0.169) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.610 (+/-0.163) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.610 (+/-0.163) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.616 (+/-0.182) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.603 (+/-0.179) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      1.00       623

avg / total       0.98      0.99      0.99       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.640 (+/-0.234) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.306) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.006) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.497 (+/-0.006) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.681 (+/-0.293) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.214) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.611 (+/-0.234) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.494 (+/-0.009) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.003) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.681 (+/-0.293) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.681 (+/-0.292) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.652 (+/-0.207) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.583 (+/-0.232) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.007) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.003) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.306) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.680 (+/-0.292) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.676 (+/-0.286) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.609 (+/-0.241) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.585 (+/-0.230) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.007) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.003) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.681 (+/-0.293) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.681 (+/-0.292) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.676 (+/-0.286) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.675 (+/-0.290) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.555 (+/-0.218) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.586 (+/-0.227) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.007) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.003) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.617 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.681 (+/-0.293) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.681 (+/-0.293) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.676 (+/-0.286) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.674 (+/-0.290) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.596 (+/-0.236) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.585 (+/-0.230) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.586 (+/-0.227) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.007) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.003) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.679 (+/-0.293) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.697 (+/-0.303) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.692 (+/-0.298) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.672 (+/-0.294) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.684 (+/-0.313) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.564 (+/-0.249) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.585 (+/-0.229) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.586 (+/-0.227) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.007) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.003) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.658 (+/-0.216) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.581 (+/-0.278) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.694 (+/-0.298) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.687 (+/-0.287) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.667 (+/-0.305) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.682 (+/-0.294) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.590 (+/-0.261) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.585 (+/-0.230) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.586 (+/-0.227) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.007) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.003) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.682 (+/-0.293) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.537 (+/-0.183) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.692 (+/-0.292) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.686 (+/-0.286) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.683 (+/-0.315) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.682 (+/-0.295) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.589 (+/-0.260) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.585 (+/-0.230) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.586 (+/-0.227) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.007) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.003) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.658 (+/-0.216) for {'C': 0.1, 'kernel': 'linear'}
0.656 (+/-0.213) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.697 (+/-0.305) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.681 (+/-0.292) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.680 (+/-0.292) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.680 (+/-0.292) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.680 (+/-0.292) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.679 (+/-0.291) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 0.6309573444801931, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.697594607964383
循环迭代之前，delta is: [0.36904266]
执行tunemodel()函数前，使用的grid search parameter是：
 [{'C': array([1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03, 1.e+04, 1.e+05,
       1.e+06, 1.e+07, 1.e+08]), 'kernel': ['rbf'], 'gamma': array([1.e-08, 1.e-07, 1.e-06, 1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01,
       1.e+00, 1.e+01, 1.e+02])}, {'C': array([0.39810717, 0.43651583, 0.47863009, 0.52480746, 0.57543994,
       0.63095734, 0.69183097, 0.75857758, 0.83176377, 0.91201084,
       1.        ]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.327) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.689 (+/-0.436) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.635 (+/-0.198) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.689 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.689 (+/-0.436) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.630 (+/-0.189) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.635 (+/-0.326) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.564 (+/-0.195) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.689 (+/-0.436) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.630 (+/-0.189) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.603 (+/-0.195) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.609 (+/-0.322) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.550 (+/-0.157) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.689 (+/-0.436) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.630 (+/-0.189) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.599 (+/-0.192) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.577 (+/-0.153) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.580 (+/-0.225) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.547 (+/-0.154) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.689 (+/-0.436) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.643 (+/-0.202) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.603 (+/-0.195) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.573 (+/-0.152) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.621 (+/-0.312) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.561 (+/-0.195) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.547 (+/-0.154) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.639 (+/-0.285) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.634 (+/-0.190) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.620 (+/-0.184) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.577 (+/-0.155) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.594 (+/-0.178) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.547 (+/-0.171) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.561 (+/-0.195) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.547 (+/-0.154) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.797 (+/-0.492) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.624 (+/-0.212) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.623 (+/-0.184) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.580 (+/-0.155) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.595 (+/-0.177) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.592 (+/-0.168) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.553 (+/-0.167) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.561 (+/-0.195) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.547 (+/-0.154) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.779 (+/-0.452) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.648 (+/-0.320) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.614 (+/-0.182) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.577 (+/-0.154) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.618 (+/-0.201) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.592 (+/-0.172) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.552 (+/-0.167) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.561 (+/-0.195) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.547 (+/-0.154) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.648 (+/-0.209) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.648 (+/-0.209) for {'C': 0.43651583224016594, 'kernel': 'linear'}
0.639 (+/-0.202) for {'C': 0.47863009232263826, 'kernel': 'linear'}
0.635 (+/-0.198) for {'C': 0.5248074602497725, 'kernel': 'linear'}
0.635 (+/-0.198) for {'C': 0.5754399373371568, 'kernel': 'linear'}
0.643 (+/-0.202) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.646 (+/-0.204) for {'C': 0.6918309709189364, 'kernel': 'linear'}
0.660 (+/-0.219) for {'C': 0.7585775750291837, 'kernel': 'linear'}
0.643 (+/-0.202) for {'C': 0.8317637711026709, 'kernel': 'linear'}
0.643 (+/-0.202) for {'C': 0.9120108393559095, 'kernel': 'linear'}
0.643 (+/-0.202) for {'C': 0.9999999999999997, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      1.00       623

avg / total       0.98      0.99      0.99       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.002) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.497 (+/-0.006) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.214) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.004) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.003) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.681 (+/-0.293) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.589 (+/-0.226) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.007) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.003) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.681 (+/-0.293) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.639 (+/-0.233) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.586 (+/-0.229) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.007) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.003) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.681 (+/-0.293) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.233) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.677 (+/-0.288) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.587 (+/-0.232) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.586 (+/-0.227) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.007) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.003) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.681 (+/-0.293) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.639 (+/-0.233) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.677 (+/-0.287) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.628 (+/-0.227) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.585 (+/-0.230) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.586 (+/-0.227) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.007) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.003) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.681 (+/-0.294) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.681 (+/-0.293) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.681 (+/-0.292) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.677 (+/-0.287) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.693 (+/-0.303) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.566 (+/-0.245) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.585 (+/-0.229) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.586 (+/-0.227) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.007) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.003) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.642 (+/-0.236) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.681 (+/-0.255) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.681 (+/-0.293) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.693 (+/-0.298) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.677 (+/-0.297) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.690 (+/-0.311) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.590 (+/-0.261) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.585 (+/-0.230) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.586 (+/-0.227) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.007) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.003) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.682 (+/-0.293) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.658 (+/-0.243) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.680 (+/-0.290) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.692 (+/-0.297) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.693 (+/-0.314) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.688 (+/-0.312) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.589 (+/-0.260) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.585 (+/-0.230) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.586 (+/-0.227) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.007) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.003) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.657 (+/-0.215) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.682 (+/-0.294) for {'C': 0.43651583224016594, 'kernel': 'linear'}
0.681 (+/-0.294) for {'C': 0.47863009232263826, 'kernel': 'linear'}
0.681 (+/-0.293) for {'C': 0.5248074602497725, 'kernel': 'linear'}
0.681 (+/-0.293) for {'C': 0.5754399373371568, 'kernel': 'linear'}
0.698 (+/-0.305) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 0.6918309709189364, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 0.7585775750291837, 'kernel': 'linear'}
0.681 (+/-0.293) for {'C': 0.8317637711026709, 'kernel': 'linear'}
0.681 (+/-0.293) for {'C': 0.9120108393559095, 'kernel': 'linear'}
0.681 (+/-0.293) for {'C': 0.9999999999999997, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 0.7585775750291837, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6982356336054085
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.618 (+/-0.214) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.630 (+/-0.192) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.327) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.618 (+/-0.172) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.623 (+/-0.183) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.631 (+/-0.395) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.618 (+/-0.172) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.167) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.581 (+/-0.180) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.549 (+/-0.155) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.625 (+/-0.179) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.610 (+/-0.163) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.582 (+/-0.180) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.568 (+/-0.175) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.550 (+/-0.157) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.618 (+/-0.172) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.614 (+/-0.167) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.582 (+/-0.180) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.579 (+/-0.178) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.555 (+/-0.199) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.547 (+/-0.154) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.747 (+/-0.502) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.622 (+/-0.174) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.614 (+/-0.169) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.582 (+/-0.180) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.570 (+/-0.152) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.571 (+/-0.191) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.561 (+/-0.195) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.547 (+/-0.154) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.597 (+/-0.177) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.610 (+/-0.166) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.583 (+/-0.182) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.567 (+/-0.144) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.569 (+/-0.148) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.547 (+/-0.171) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.561 (+/-0.195) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.547 (+/-0.154) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.798 (+/-0.437) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.530 (+/-0.146) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.586 (+/-0.155) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.561 (+/-0.146) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.571 (+/-0.147) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.584 (+/-0.176) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.553 (+/-0.167) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.561 (+/-0.195) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.547 (+/-0.154) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.775 (+/-0.457) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.550 (+/-0.298) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.578 (+/-0.149) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.558 (+/-0.144) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.592 (+/-0.189) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.586 (+/-0.177) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.552 (+/-0.167) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.561 (+/-0.195) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.547 (+/-0.154) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.627 (+/-0.185) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.630 (+/-0.189) for {'C': 0.43651583224016594, 'kernel': 'linear'}
0.630 (+/-0.189) for {'C': 0.47863009232263826, 'kernel': 'linear'}
0.630 (+/-0.189) for {'C': 0.5248074602497725, 'kernel': 'linear'}
0.630 (+/-0.189) for {'C': 0.5754399373371568, 'kernel': 'linear'}
0.630 (+/-0.189) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.630 (+/-0.189) for {'C': 0.6918309709189364, 'kernel': 'linear'}
0.630 (+/-0.189) for {'C': 0.7585775750291837, 'kernel': 'linear'}
0.630 (+/-0.189) for {'C': 0.8317637711026709, 'kernel': 'linear'}
0.630 (+/-0.189) for {'C': 0.9120108393559095, 'kernel': 'linear'}
0.630 (+/-0.189) for {'C': 0.9999999999999997, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      1.00       623

avg / total       0.98      0.99      0.99       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.640 (+/-0.234) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.306) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.006) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.497 (+/-0.006) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.681 (+/-0.293) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.214) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.611 (+/-0.234) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.494 (+/-0.009) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.003) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.681 (+/-0.293) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.681 (+/-0.292) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.652 (+/-0.207) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.583 (+/-0.232) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.007) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.003) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.306) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.680 (+/-0.292) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.676 (+/-0.286) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.609 (+/-0.241) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.585 (+/-0.230) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.007) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.003) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.681 (+/-0.293) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.681 (+/-0.292) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.676 (+/-0.286) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.675 (+/-0.290) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.555 (+/-0.218) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.586 (+/-0.227) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.007) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.003) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.617 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.681 (+/-0.293) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.681 (+/-0.293) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.676 (+/-0.286) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.674 (+/-0.290) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.596 (+/-0.236) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.585 (+/-0.230) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.586 (+/-0.227) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.007) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.003) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.679 (+/-0.293) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.697 (+/-0.303) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.692 (+/-0.298) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.672 (+/-0.294) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.684 (+/-0.313) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.564 (+/-0.249) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.585 (+/-0.229) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.586 (+/-0.227) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.007) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.003) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.658 (+/-0.216) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.581 (+/-0.278) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.694 (+/-0.298) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.687 (+/-0.287) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.667 (+/-0.305) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.682 (+/-0.294) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.590 (+/-0.261) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.585 (+/-0.230) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.586 (+/-0.227) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.007) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.003) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.682 (+/-0.293) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.537 (+/-0.183) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.692 (+/-0.292) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.686 (+/-0.286) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.683 (+/-0.315) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.682 (+/-0.295) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.589 (+/-0.260) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.585 (+/-0.230) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.586 (+/-0.227) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.007) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.003) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.697 (+/-0.305) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 0.43651583224016594, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 0.47863009232263826, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 0.5248074602497725, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 0.5754399373371568, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 0.6918309709189364, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 0.7585775750291837, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 0.8317637711026709, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 0.9120108393559095, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 0.9999999999999997, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 0.43651583224016594, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.697594607964383
第0轮loop中，tunemodel函数得到的最优参数是： ([{'C': array([1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03, 1.e+04, 1.e+05,
       1.e+06, 1.e+07, 1.e+08]), 'kernel': ['rbf'], 'gamma': array([1.e-08, 1.e-07, 1.e-06, 1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01,
       1.e+00, 1.e+01, 1.e+02])}, {'C': array([0.69183097, 0.70469307, 0.71779429, 0.73113908, 0.74473197,
       0.75857758, 0.77268059, 0.78704579, 0.80167806, 0.81658237,
       0.83176377]), 'kernel': ['linear']}], {'C': 0.7585775750291837, 'kernel': 'linear'}, 0.6982356336054085)
这是第0次迭代微调C和gamma。
第0次迭代，得到delta： [0.12762023]
执行tunemodel()函数前，使用的grid search parameter是：
 [{'C': array([1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03, 1.e+04, 1.e+05,
       1.e+06, 1.e+07, 1.e+08]), 'kernel': ['rbf'], 'gamma': array([1.e-08, 1.e-07, 1.e-06, 1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01,
       1.e+00, 1.e+01, 1.e+02])}, {'C': array([0.69183097, 0.70469307, 0.71779429, 0.73113908, 0.74473197,
       0.75857758, 0.77268059, 0.78704579, 0.80167806, 0.81658237,
       0.83176377]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.327) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.689 (+/-0.436) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.635 (+/-0.198) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.689 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.689 (+/-0.436) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.630 (+/-0.189) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.635 (+/-0.326) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.564 (+/-0.195) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.689 (+/-0.436) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.630 (+/-0.189) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.603 (+/-0.195) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.609 (+/-0.322) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.550 (+/-0.157) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.689 (+/-0.436) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.630 (+/-0.189) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.599 (+/-0.192) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.577 (+/-0.153) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.580 (+/-0.225) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.547 (+/-0.154) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.689 (+/-0.436) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.643 (+/-0.202) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.603 (+/-0.195) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.573 (+/-0.152) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.621 (+/-0.312) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.561 (+/-0.195) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.547 (+/-0.154) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.639 (+/-0.285) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.634 (+/-0.190) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.620 (+/-0.184) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.577 (+/-0.155) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.594 (+/-0.178) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.547 (+/-0.171) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.561 (+/-0.195) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.547 (+/-0.154) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.797 (+/-0.492) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.624 (+/-0.212) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.623 (+/-0.184) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.580 (+/-0.155) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.595 (+/-0.177) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.592 (+/-0.168) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.553 (+/-0.167) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.561 (+/-0.195) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.547 (+/-0.154) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.779 (+/-0.452) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.648 (+/-0.320) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.614 (+/-0.182) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.577 (+/-0.154) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.618 (+/-0.201) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.592 (+/-0.172) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.552 (+/-0.167) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.561 (+/-0.195) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.547 (+/-0.154) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.646 (+/-0.204) for {'C': 0.6918309709189364, 'kernel': 'linear'}
0.646 (+/-0.204) for {'C': 0.7046930689671468, 'kernel': 'linear'}
0.646 (+/-0.204) for {'C': 0.7177942912713616, 'kernel': 'linear'}
0.643 (+/-0.202) for {'C': 0.7311390834834173, 'kernel': 'linear'}
0.651 (+/-0.211) for {'C': 0.7447319739059889, 'kernel': 'linear'}
0.660 (+/-0.219) for {'C': 0.7585775750291837, 'kernel': 'linear'}
0.643 (+/-0.202) for {'C': 0.7726805850957021, 'kernel': 'linear'}
0.643 (+/-0.202) for {'C': 0.7870457896950985, 'kernel': 'linear'}
0.643 (+/-0.202) for {'C': 0.801678063387679, 'kernel': 'linear'}
0.643 (+/-0.202) for {'C': 0.8165823713585922, 'kernel': 'linear'}
0.643 (+/-0.202) for {'C': 0.8317637711026709, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      1.00       623

avg / total       0.98      0.99      0.99       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.002) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.497 (+/-0.006) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.214) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.004) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.003) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.681 (+/-0.293) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.589 (+/-0.226) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.007) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.003) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.681 (+/-0.293) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.639 (+/-0.233) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.586 (+/-0.229) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.007) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.003) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.681 (+/-0.293) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.233) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.677 (+/-0.288) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.587 (+/-0.232) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.586 (+/-0.227) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.007) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.003) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.681 (+/-0.293) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.639 (+/-0.233) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.677 (+/-0.287) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.628 (+/-0.227) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.585 (+/-0.230) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.586 (+/-0.227) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.007) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.003) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.681 (+/-0.294) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.681 (+/-0.293) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.681 (+/-0.292) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.677 (+/-0.287) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.693 (+/-0.303) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.566 (+/-0.245) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.585 (+/-0.229) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.586 (+/-0.227) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.007) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.003) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.642 (+/-0.236) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.681 (+/-0.255) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.681 (+/-0.293) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.693 (+/-0.298) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.677 (+/-0.297) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.690 (+/-0.311) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.590 (+/-0.261) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.585 (+/-0.230) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.586 (+/-0.227) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.007) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.003) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.682 (+/-0.293) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.658 (+/-0.243) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.680 (+/-0.290) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.692 (+/-0.297) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.693 (+/-0.314) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.688 (+/-0.312) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.589 (+/-0.260) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.585 (+/-0.230) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.586 (+/-0.227) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.007) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.003) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.698 (+/-0.306) for {'C': 0.6918309709189364, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 0.7046930689671468, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 0.7177942912713616, 'kernel': 'linear'}
0.681 (+/-0.293) for {'C': 0.7311390834834173, 'kernel': 'linear'}
0.682 (+/-0.294) for {'C': 0.7447319739059889, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 0.7585775750291837, 'kernel': 'linear'}
0.681 (+/-0.293) for {'C': 0.7726805850957021, 'kernel': 'linear'}
0.681 (+/-0.293) for {'C': 0.7870457896950985, 'kernel': 'linear'}
0.681 (+/-0.293) for {'C': 0.801678063387679, 'kernel': 'linear'}
0.681 (+/-0.293) for {'C': 0.8165823713585922, 'kernel': 'linear'}
0.681 (+/-0.293) for {'C': 0.8317637711026709, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 0.7585775750291837, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6982356336054085
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.618 (+/-0.214) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.630 (+/-0.192) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.327) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.618 (+/-0.172) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.623 (+/-0.183) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.631 (+/-0.395) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.618 (+/-0.172) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.167) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.581 (+/-0.180) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.549 (+/-0.155) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.625 (+/-0.179) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.610 (+/-0.163) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.582 (+/-0.180) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.568 (+/-0.175) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.550 (+/-0.157) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.618 (+/-0.172) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.614 (+/-0.167) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.582 (+/-0.180) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.579 (+/-0.178) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.555 (+/-0.199) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.547 (+/-0.154) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.747 (+/-0.502) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.622 (+/-0.174) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.614 (+/-0.169) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.582 (+/-0.180) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.570 (+/-0.152) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.571 (+/-0.191) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.561 (+/-0.195) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.547 (+/-0.154) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.597 (+/-0.177) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.610 (+/-0.166) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.583 (+/-0.182) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.567 (+/-0.144) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.569 (+/-0.148) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.547 (+/-0.171) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.561 (+/-0.195) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.547 (+/-0.154) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.798 (+/-0.437) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.530 (+/-0.146) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.586 (+/-0.155) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.561 (+/-0.146) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.571 (+/-0.147) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.584 (+/-0.176) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.553 (+/-0.167) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.561 (+/-0.195) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.547 (+/-0.154) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.775 (+/-0.457) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.550 (+/-0.298) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.578 (+/-0.149) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.558 (+/-0.144) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.592 (+/-0.189) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.586 (+/-0.177) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.552 (+/-0.167) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.561 (+/-0.195) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.547 (+/-0.154) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.630 (+/-0.189) for {'C': 0.6918309709189364, 'kernel': 'linear'}
0.630 (+/-0.189) for {'C': 0.7046930689671468, 'kernel': 'linear'}
0.630 (+/-0.189) for {'C': 0.7177942912713616, 'kernel': 'linear'}
0.630 (+/-0.189) for {'C': 0.7311390834834173, 'kernel': 'linear'}
0.630 (+/-0.189) for {'C': 0.7447319739059889, 'kernel': 'linear'}
0.630 (+/-0.189) for {'C': 0.7585775750291837, 'kernel': 'linear'}
0.630 (+/-0.189) for {'C': 0.7726805850957021, 'kernel': 'linear'}
0.630 (+/-0.189) for {'C': 0.7870457896950985, 'kernel': 'linear'}
0.630 (+/-0.189) for {'C': 0.801678063387679, 'kernel': 'linear'}
0.630 (+/-0.189) for {'C': 0.8165823713585922, 'kernel': 'linear'}
0.630 (+/-0.189) for {'C': 0.8317637711026709, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [1]
检查交叉验证集中测试集标签是否有预测不到的值： {-1}
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      1.00       623

avg / total       0.98      0.99      0.99       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.640 (+/-0.234) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.306) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.006) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.497 (+/-0.006) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.681 (+/-0.293) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.214) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.611 (+/-0.234) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.494 (+/-0.009) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.003) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.681 (+/-0.293) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.681 (+/-0.292) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.652 (+/-0.207) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.583 (+/-0.232) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.007) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.003) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.306) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.680 (+/-0.292) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.676 (+/-0.286) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.609 (+/-0.241) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.585 (+/-0.230) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.007) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.003) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.681 (+/-0.293) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.681 (+/-0.292) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.676 (+/-0.286) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.675 (+/-0.290) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.555 (+/-0.218) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.586 (+/-0.227) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.007) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.003) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.617 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.681 (+/-0.293) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.681 (+/-0.293) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.676 (+/-0.286) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.674 (+/-0.290) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.596 (+/-0.236) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.585 (+/-0.230) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.586 (+/-0.227) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.007) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.003) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.679 (+/-0.293) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.697 (+/-0.303) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.692 (+/-0.298) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.672 (+/-0.294) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.684 (+/-0.313) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.564 (+/-0.249) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.585 (+/-0.229) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.586 (+/-0.227) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.007) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.003) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.658 (+/-0.216) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.581 (+/-0.278) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.694 (+/-0.298) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.687 (+/-0.287) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.667 (+/-0.305) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.682 (+/-0.294) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.590 (+/-0.261) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.585 (+/-0.230) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.586 (+/-0.227) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.007) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.003) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.682 (+/-0.293) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.537 (+/-0.183) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.692 (+/-0.292) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.686 (+/-0.286) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.683 (+/-0.315) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.682 (+/-0.295) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.589 (+/-0.260) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.585 (+/-0.230) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.586 (+/-0.227) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.007) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.003) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.698 (+/-0.306) for {'C': 0.6918309709189364, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 0.7046930689671468, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 0.7177942912713616, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 0.7311390834834173, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 0.7447319739059889, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 0.7585775750291837, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 0.7726805850957021, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 0.7870457896950985, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 0.801678063387679, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 0.8165823713585922, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 0.8317637711026709, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 0.6918309709189364, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.697594607964383
第1轮loop中，tunemodel函数得到的最优参数是： ([{'C': array([1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03, 1.e+04, 1.e+05,
       1.e+06, 1.e+07, 1.e+08]), 'kernel': ['rbf'], 'gamma': array([1.e-08, 1.e-07, 1.e-06, 1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01,
       1.e+00, 1.e+01, 1.e+02])}, {'C': array([0.74473197, 0.74748073, 0.75023963, 0.75300871, 0.75578801,
       0.75857758, 0.76137743, 0.76418762, 0.76700819, 0.76983916,
       0.77268059]), 'kernel': ['linear']}], {'C': 0.7585775750291837, 'kernel': 'linear'}, 0.6982356336054085)
这是第1次迭代微调C和gamma。
第1次迭代，得到delta： [0.]
SVC模型clf_op_iter的参数是： {'kernel': 'linear', 'decision_function_shape': 'ovr', 'class_weight': {-1: 30}, 'tol': 0.001, 'gamma': 'auto', 'random_state': 0, 'cache_size': 200, 'verbose': False, 'degree': 3, 'C': 0.7585775750291837, 'probability': False, 'shrinking': True, 'coef0': 0.0, 'max_iter': -1}

训练模型SVC对训练样本的预测准确率： 0.8851522842639594

测试集中，预测为舞弊样本的有： (array([  10,   12,   22,   24,   38,   56,   65,   67,  112,  114,  115,
        117,  121,  136,  143,  144,  145,  146,  180,  181,  182,  183,
        208,  214,  216,  236,  243,  247,  255,  281,  286,  287,  288,
        300,  330,  349,  364,  366,  370,  403,  404,  433,  442,  449,
        457,  460,  468,  471,  472,  473,  474,  475,  476,  477,  481,
        482,  483,  484,  485,  487,  488,  489,  490,  509,  516,  535,
        543,  544,  545,  549,  562,  563,  565,  566,  567,  587,  590,
        611,  629,  630,  635,  641,  661,  667,  687,  695,  696,  697,
        718,  720,  721,  722,  723,  736,  742,  743,  750,  752,  775,
        776,  777,  778,  787,  790,  820,  845,  860,  862,  874,  879,
        880,  882,  883,  884,  885,  897,  898,  902,  929,  930,  931,
        960,  961,  963,  964,  966,  967,  998, 1013, 1014, 1015, 1016,
       1017, 1037, 1049, 1050, 1051, 1067, 1089, 1112, 1113, 1114, 1122,
       1124, 1129, 1141, 1142, 1148, 1154, 1156, 1158, 1160, 1164, 1180,
       1183, 1194, 1200, 1222, 1230, 1241, 1242, 1243, 1246, 1247, 1248,
       1249, 1250, 1251, 1252, 1254, 1255, 1256], dtype=int64),) 
测试集中，实际为舞弊样本的有： (array([1246, 1247, 1248, 1249, 1250, 1251, 1252, 1253, 1254, 1255, 1256],
      dtype=int64),) 
预测的舞弊样本数目： 172

训练模型SVC对测试样本的预测准确率： 0.8781725888324873
以上是第45次特征筛选。
第45次特征筛选，AUC值是： 0.8895374288632715
X_train_iter_svc.shape is: (1257, 7)
X_test_iter_svc.shape is: (1257, 7)
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.581 (+/-0.180) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.526 (+/-0.120) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.630 (+/-0.189) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.25      0.17      0.20         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.99       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.614 (+/-0.236) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.498 (+/-0.004) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.548 (+/-0.198) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.697 (+/-0.308) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.499 (+/-0.004) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.25      0.17      0.20         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.99       629

本轮grid search结果，得到最好的参数选择是： {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6967933259131008
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.625 (+/-0.179) for {'C': 1.0, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.578 (+/-0.145) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.25      0.17      0.20         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.99       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.697 (+/-0.308) for {'C': 1.0, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.693 (+/-0.302) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.25      0.17      0.20         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.99       629

本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6966325542089208
RBF的粗grid search最佳超参： {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001} RBF的粗grid search最高分值： 0.6967933259131008
linear的粗grid search最佳超参： {'C': 1.0, 'kernel': 'linear'} linear的粗grid search最高分值： 0.6966325542089208
粗grid search得到的parameter是：
 [{'C': array([1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03, 1.e+04, 1.e+05,
       1.e+06, 1.e+07, 1.e+08]), 'kernel': ['rbf'], 'gamma': array([1.e-08, 1.e-07, 1.e-06, 1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01,
       1.e+00, 1.e+01, 1.e+02])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.689 (+/-0.436) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.639 (+/-0.326) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.581 (+/-0.180) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.639 (+/-0.326) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.630 (+/-0.189) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.585 (+/-0.186) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.505 (+/-0.051) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.326) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.630 (+/-0.189) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.284) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.546 (+/-0.170) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.505 (+/-0.051) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.639 (+/-0.326) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.630 (+/-0.189) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.655 (+/-0.280) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.286) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.526 (+/-0.120) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.505 (+/-0.051) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.639 (+/-0.326) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.630 (+/-0.189) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.651 (+/-0.279) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.282) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.561 (+/-0.173) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.509 (+/-0.076) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.505 (+/-0.051) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.630 (+/-0.189) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.655 (+/-0.280) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.611 (+/-0.283) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.601 (+/-0.180) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.560 (+/-0.173) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.509 (+/-0.076) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.505 (+/-0.051) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.666 (+/-0.275) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.630 (+/-0.189) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.647 (+/-0.279) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.607 (+/-0.283) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.602 (+/-0.182) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.618 (+/-0.238) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.547 (+/-0.171) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.509 (+/-0.076) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.505 (+/-0.051) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.722 (+/-0.473) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.589 (+/-0.194) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.646 (+/-0.287) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.634 (+/-0.372) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.633 (+/-0.295) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.634 (+/-0.212) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.606 (+/-0.249) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.547 (+/-0.171) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.509 (+/-0.076) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.505 (+/-0.051) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.721 (+/-0.328) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.572 (+/-0.190) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.627 (+/-0.280) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.607 (+/-0.297) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.665 (+/-0.356) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.634 (+/-0.212) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.606 (+/-0.249) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.547 (+/-0.171) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.509 (+/-0.076) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.505 (+/-0.051) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.630 (+/-0.189) for {'C': 1.0, 'kernel': 'linear'}
0.655 (+/-0.280) for {'C': 10.0, 'kernel': 'linear'}
0.645 (+/-0.280) for {'C': 100.0, 'kernel': 'linear'}
0.582 (+/-0.153) for {'C': 1000.0, 'kernel': 'linear'}
0.619 (+/-0.185) for {'C': 10000.0, 'kernel': 'linear'}
0.641 (+/-0.286) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.003) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.002) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.236) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.002) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.308) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.236) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.523 (+/-0.146) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.497 (+/-0.008) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.308) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.217) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.563 (+/-0.206) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.523 (+/-0.146) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.498 (+/-0.007) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.615 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.308) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.308) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.695 (+/-0.303) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.548 (+/-0.198) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.521 (+/-0.148) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.497 (+/-0.006) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.308) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.307) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.694 (+/-0.303) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.580 (+/-0.209) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.522 (+/-0.149) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.521 (+/-0.148) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.497 (+/-0.006) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.697 (+/-0.308) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.308) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.694 (+/-0.301) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.695 (+/-0.302) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.589 (+/-0.226) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.519 (+/-0.151) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.521 (+/-0.148) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.497 (+/-0.006) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.680 (+/-0.193) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.697 (+/-0.308) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.697 (+/-0.307) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.693 (+/-0.300) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.694 (+/-0.304) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.647 (+/-0.255) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.562 (+/-0.208) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.520 (+/-0.151) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.521 (+/-0.148) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.497 (+/-0.006) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.616 (+/-0.239) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.662 (+/-0.228) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.697 (+/-0.303) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.691 (+/-0.293) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.696 (+/-0.305) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.696 (+/-0.306) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.620 (+/-0.265) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.558 (+/-0.214) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.519 (+/-0.151) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.521 (+/-0.148) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.497 (+/-0.006) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.706 (+/-0.269) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.633 (+/-0.241) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.696 (+/-0.301) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.691 (+/-0.292) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.304) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.696 (+/-0.305) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.271) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.557 (+/-0.215) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.519 (+/-0.151) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.521 (+/-0.148) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.497 (+/-0.006) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.616 (+/-0.239) for {'C': 0.1, 'kernel': 'linear'}
0.697 (+/-0.308) for {'C': 1.0, 'kernel': 'linear'}
0.697 (+/-0.308) for {'C': 10.0, 'kernel': 'linear'}
0.697 (+/-0.307) for {'C': 100.0, 'kernel': 'linear'}
0.693 (+/-0.302) for {'C': 1000.0, 'kernel': 'linear'}
0.696 (+/-0.307) for {'C': 10000.0, 'kernel': 'linear'}
0.696 (+/-0.307) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.7059284565916398
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.606 (+/-0.146) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.625 (+/-0.179) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.656 (+/-0.291) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.625 (+/-0.179) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.621 (+/-0.177) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.580 (+/-0.172) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.505 (+/-0.051) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.625 (+/-0.179) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.625 (+/-0.179) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.626 (+/-0.287) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.546 (+/-0.170) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.505 (+/-0.051) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.625 (+/-0.179) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.625 (+/-0.179) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.618 (+/-0.282) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.606 (+/-0.294) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.518 (+/-0.087) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.505 (+/-0.051) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.625 (+/-0.179) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.625 (+/-0.179) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.282) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.603 (+/-0.283) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.565 (+/-0.181) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.509 (+/-0.076) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.505 (+/-0.051) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.625 (+/-0.179) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.625 (+/-0.179) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.282) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.602 (+/-0.283) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.604 (+/-0.182) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.547 (+/-0.171) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.509 (+/-0.076) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.505 (+/-0.051) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.600 (+/-0.112) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.616 (+/-0.170) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.603 (+/-0.300) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.613 (+/-0.294) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.592 (+/-0.160) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.606 (+/-0.249) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.547 (+/-0.171) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.509 (+/-0.076) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.505 (+/-0.051) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.689 (+/-0.299) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.529 (+/-0.145) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.602 (+/-0.281) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.595 (+/-0.303) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.585 (+/-0.152) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.610 (+/-0.205) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.606 (+/-0.249) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.547 (+/-0.171) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.509 (+/-0.076) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.505 (+/-0.051) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.666 (+/-0.295) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.527 (+/-0.147) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.596 (+/-0.282) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.594 (+/-0.303) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.653 (+/-0.361) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.608 (+/-0.208) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.606 (+/-0.249) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.547 (+/-0.171) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.509 (+/-0.076) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.505 (+/-0.051) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.727 (+/-0.397) for {'C': 0.1, 'kernel': 'linear'}
0.625 (+/-0.179) for {'C': 1.0, 'kernel': 'linear'}
0.633 (+/-0.278) for {'C': 10.0, 'kernel': 'linear'}
0.598 (+/-0.284) for {'C': 100.0, 'kernel': 'linear'}
0.578 (+/-0.145) for {'C': 1000.0, 'kernel': 'linear'}
0.608 (+/-0.180) for {'C': 10000.0, 'kernel': 'linear'}
0.634 (+/-0.287) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.655 (+/-0.215) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.002) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.307) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.656 (+/-0.216) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.005) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.497 (+/-0.010) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.307) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.696 (+/-0.308) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.627 (+/-0.223) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.523 (+/-0.146) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.497 (+/-0.008) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.307) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.308) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.695 (+/-0.306) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.563 (+/-0.206) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.515 (+/-0.152) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.498 (+/-0.007) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.307) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.308) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.694 (+/-0.304) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.670 (+/-0.322) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.546 (+/-0.197) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.521 (+/-0.148) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.497 (+/-0.006) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.697 (+/-0.307) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.308) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.693 (+/-0.303) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.693 (+/-0.300) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.581 (+/-0.209) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.513 (+/-0.156) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.521 (+/-0.148) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.497 (+/-0.006) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.697 (+/-0.307) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.697 (+/-0.308) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.694 (+/-0.303) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.692 (+/-0.299) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.695 (+/-0.303) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.552 (+/-0.224) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.519 (+/-0.151) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.521 (+/-0.148) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.497 (+/-0.006) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.760 (+/-0.153) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.696 (+/-0.306) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.690 (+/-0.293) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.693 (+/-0.300) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.693 (+/-0.305) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.618 (+/-0.269) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.554 (+/-0.219) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.520 (+/-0.151) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.521 (+/-0.148) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.497 (+/-0.006) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.657 (+/-0.216) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.576 (+/-0.168) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.692 (+/-0.293) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.687 (+/-0.287) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.694 (+/-0.303) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.690 (+/-0.313) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.607 (+/-0.290) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.558 (+/-0.214) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.519 (+/-0.151) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.521 (+/-0.148) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.497 (+/-0.006) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.681 (+/-0.293) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.526 (+/-0.114) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.690 (+/-0.287) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.687 (+/-0.289) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.693 (+/-0.308) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.687 (+/-0.315) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.607 (+/-0.290) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.557 (+/-0.215) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.519 (+/-0.151) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.521 (+/-0.148) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.497 (+/-0.006) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.657 (+/-0.216) for {'C': 0.1, 'kernel': 'linear'}
0.697 (+/-0.308) for {'C': 1.0, 'kernel': 'linear'}
0.696 (+/-0.306) for {'C': 10.0, 'kernel': 'linear'}
0.691 (+/-0.298) for {'C': 100.0, 'kernel': 'linear'}
0.693 (+/-0.302) for {'C': 1000.0, 'kernel': 'linear'}
0.695 (+/-0.304) for {'C': 10000.0, 'kernel': 'linear'}
0.695 (+/-0.305) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.7602533184928684
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.496 (+/-0.001) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.496 (+/-0.001) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.496 (+/-0.001) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.496 (+/-0.001) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.496 (+/-0.001) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.496 (+/-0.001) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.496 (+/-0.001) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.547 (+/-0.301) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.697 (+/-0.437) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.639 (+/-0.326) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.496 (+/-0.001) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.496 (+/-0.001) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.496 (+/-0.001) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.496 (+/-0.001) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.496 (+/-0.001) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.496 (+/-0.001) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.496 (+/-0.001) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.547 (+/-0.301) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.689 (+/-0.436) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.689 (+/-0.374) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.660 (+/-0.290) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.496 (+/-0.001) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.496 (+/-0.001) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.496 (+/-0.001) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.496 (+/-0.001) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.496 (+/-0.001) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.496 (+/-0.001) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.597 (+/-0.402) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.689 (+/-0.436) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.689 (+/-0.374) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.643 (+/-0.209) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.618 (+/-0.172) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.496 (+/-0.001) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.496 (+/-0.001) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.496 (+/-0.001) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.496 (+/-0.001) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.496 (+/-0.001) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.597 (+/-0.402) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.689 (+/-0.436) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.689 (+/-0.374) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.627 (+/-0.185) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.625 (+/-0.179) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.644 (+/-0.164) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.496 (+/-0.001) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.496 (+/-0.001) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.496 (+/-0.001) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.496 (+/-0.001) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.747 (+/-0.502) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.702 (+/-0.420) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.689 (+/-0.374) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.677 (+/-0.299) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.656 (+/-0.138) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.660 (+/-0.136) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.630 (+/-0.189) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.656 (+/-0.312) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.666 (+/-0.275) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.693 (+/-0.354) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.635 (+/-0.153) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.644 (+/-0.120) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.669 (+/-0.142) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.630 (+/-0.189) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.604 (+/-0.171) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.672 (+/-0.287) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.630 (+/-0.189) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.710 (+/-0.209) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.618 (+/-0.172) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.677 (+/-0.150) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.625 (+/-0.179) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.647 (+/-0.460) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.747 (+/-0.502) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.544 (+/-0.101) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.649 (+/-0.290) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.612 (+/-0.165) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.708 (+/-0.220) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.630 (+/-0.189) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.677 (+/-0.150) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.630 (+/-0.192) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.496 (+/-0.001) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.647 (+/-0.460) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.747 (+/-0.502) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.702 (+/-0.429) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.511 (+/-0.032) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.623 (+/-0.295) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.624 (+/-0.166) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.724 (+/-0.211) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.655 (+/-0.285) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.673 (+/-0.153) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.630 (+/-0.192) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.647 (+/-0.460) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.722 (+/-0.473) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.739 (+/-0.451) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.691 (+/-0.370) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.503 (+/-0.010) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.595 (+/-0.196) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.619 (+/-0.169) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.708 (+/-0.232) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.655 (+/-0.285) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.698 (+/-0.247) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.646 (+/-0.287) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.722 (+/-0.473) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.714 (+/-0.418) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.689 (+/-0.299) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.732 (+/-0.391) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.453 (+/-0.300) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.589 (+/-0.194) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.609 (+/-0.185) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.714 (+/-0.225) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.659 (+/-0.285) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.694 (+/-0.242) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.646 (+/-0.287) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.630 (+/-0.189) for {'C': 1.0, 'kernel': 'linear'}
0.655 (+/-0.280) for {'C': 10.0, 'kernel': 'linear'}
0.645 (+/-0.280) for {'C': 100.0, 'kernel': 'linear'}
0.582 (+/-0.153) for {'C': 1000.0, 'kernel': 'linear'}
0.619 (+/-0.185) for {'C': 10000.0, 'kernel': 'linear'}
0.641 (+/-0.286) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.500 (+/-0.000) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.500 (+/-0.000) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.500 (+/-0.000) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.500 (+/-0.000) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.500 (+/-0.000) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.500 (+/-0.000) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.500 (+/-0.000) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.525 (+/-0.150) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.616 (+/-0.238) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.615 (+/-0.237) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.500 (+/-0.000) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.500 (+/-0.000) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.500 (+/-0.000) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.500 (+/-0.000) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.500 (+/-0.000) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.500 (+/-0.000) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.500 (+/-0.000) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.525 (+/-0.150) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.616 (+/-0.238) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.640 (+/-0.236) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.656 (+/-0.215) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.500 (+/-0.000) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.500 (+/-0.000) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.500 (+/-0.000) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.500 (+/-0.000) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.500 (+/-0.000) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.500 (+/-0.000) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.550 (+/-0.200) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.616 (+/-0.238) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.640 (+/-0.236) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.656 (+/-0.216) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.680 (+/-0.294) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.500 (+/-0.000) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.500 (+/-0.000) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.500 (+/-0.000) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.500 (+/-0.000) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.500 (+/-0.000) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.550 (+/-0.200) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.616 (+/-0.238) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.640 (+/-0.236) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.656 (+/-0.215) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.697 (+/-0.307) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.722 (+/-0.277) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.500 (+/-0.000) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.500 (+/-0.000) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.500 (+/-0.000) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.500 (+/-0.000) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.617 (+/-0.238) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.641 (+/-0.235) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.640 (+/-0.236) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.656 (+/-0.216) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.730 (+/-0.231) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.747 (+/-0.233) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.697 (+/-0.308) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.500 (+/-0.002) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.640 (+/-0.235) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.680 (+/-0.193) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.656 (+/-0.216) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.697 (+/-0.211) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.730 (+/-0.230) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.747 (+/-0.234) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.697 (+/-0.308) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.500 (+/-0.002) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.677 (+/-0.283) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.721 (+/-0.278) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.681 (+/-0.294) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.771 (+/-0.163) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.680 (+/-0.294) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.747 (+/-0.234) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.697 (+/-0.308) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.575 (+/-0.229) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.616 (+/-0.239) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.658 (+/-0.229) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.720 (+/-0.276) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.696 (+/-0.306) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.771 (+/-0.165) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.697 (+/-0.307) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.747 (+/-0.234) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.697 (+/-0.307) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.500 (+/-0.000) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.575 (+/-0.229) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.616 (+/-0.239) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.659 (+/-0.215) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.597 (+/-0.244) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.694 (+/-0.303) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.720 (+/-0.274) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.772 (+/-0.166) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.681 (+/-0.294) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.747 (+/-0.234) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.697 (+/-0.307) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.575 (+/-0.229) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.616 (+/-0.239) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.641 (+/-0.237) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.685 (+/-0.198) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.572 (+/-0.213) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.679 (+/-0.253) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.720 (+/-0.272) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.771 (+/-0.164) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.681 (+/-0.295) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.747 (+/-0.234) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.697 (+/-0.305) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.616 (+/-0.239) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.640 (+/-0.237) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.657 (+/-0.216) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.666 (+/-0.193) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.539 (+/-0.179) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.662 (+/-0.228) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.694 (+/-0.301) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.771 (+/-0.163) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.681 (+/-0.295) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.747 (+/-0.234) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.697 (+/-0.303) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.616 (+/-0.239) for {'C': 0.1, 'kernel': 'linear'}
0.697 (+/-0.308) for {'C': 1.0, 'kernel': 'linear'}
0.697 (+/-0.308) for {'C': 10.0, 'kernel': 'linear'}
0.697 (+/-0.307) for {'C': 100.0, 'kernel': 'linear'}
0.693 (+/-0.302) for {'C': 1000.0, 'kernel': 'linear'}
0.696 (+/-0.307) for {'C': 10000.0, 'kernel': 'linear'}
0.696 (+/-0.307) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.7719535823233572
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.496 (+/-0.001) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.496 (+/-0.001) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.496 (+/-0.001) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.496 (+/-0.001) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.496 (+/-0.001) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.597 (+/-0.402) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.697 (+/-0.437) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.639 (+/-0.326) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.627 (+/-0.185) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.625 (+/-0.179) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.496 (+/-0.001) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.496 (+/-0.001) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.496 (+/-0.001) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.496 (+/-0.001) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.496 (+/-0.001) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.647 (+/-0.460) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.664 (+/-0.388) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.639 (+/-0.326) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.627 (+/-0.185) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.625 (+/-0.179) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.652 (+/-0.123) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.496 (+/-0.001) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.496 (+/-0.001) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.496 (+/-0.001) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.496 (+/-0.001) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.496 (+/-0.001) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.652 (+/-0.313) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.643 (+/-0.306) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.660 (+/-0.290) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.633 (+/-0.120) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.655 (+/-0.126) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.660 (+/-0.136) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.496 (+/-0.001) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.496 (+/-0.001) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.496 (+/-0.001) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.496 (+/-0.001) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.764 (+/-0.421) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.600 (+/-0.167) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.627 (+/-0.291) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.620 (+/-0.172) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.706 (+/-0.214) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.715 (+/-0.210) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.630 (+/-0.189) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.496 (+/-0.001) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.496 (+/-0.001) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.496 (+/-0.001) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.496 (+/-0.001) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.631 (+/-0.196) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.565 (+/-0.085) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.603 (+/-0.155) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.699 (+/-0.211) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.689 (+/-0.248) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.715 (+/-0.210) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.621 (+/-0.177) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.747 (+/-0.502) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.548 (+/-0.097) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.600 (+/-0.112) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.604 (+/-0.143) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.708 (+/-0.211) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.626 (+/-0.188) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.723 (+/-0.208) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.616 (+/-0.170) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.747 (+/-0.502) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.722 (+/-0.473) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.527 (+/-0.071) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.613 (+/-0.206) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.581 (+/-0.166) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.704 (+/-0.224) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.635 (+/-0.190) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.656 (+/-0.138) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.602 (+/-0.159) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.747 (+/-0.502) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.697 (+/-0.437) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.689 (+/-0.436) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.520 (+/-0.060) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.598 (+/-0.184) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.567 (+/-0.126) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.680 (+/-0.235) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.635 (+/-0.190) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.656 (+/-0.138) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.595 (+/-0.159) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.747 (+/-0.502) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.697 (+/-0.437) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.714 (+/-0.352) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.667 (+/-0.311) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.560 (+/-0.294) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.558 (+/-0.153) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.575 (+/-0.159) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.639 (+/-0.136) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.635 (+/-0.190) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.649 (+/-0.142) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.586 (+/-0.154) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.697 (+/-0.437) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.689 (+/-0.299) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.681 (+/-0.297) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.725 (+/-0.321) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.502 (+/-0.445) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.534 (+/-0.146) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.573 (+/-0.170) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.632 (+/-0.144) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.626 (+/-0.176) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.667 (+/-0.253) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.605 (+/-0.279) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.689 (+/-0.299) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.673 (+/-0.293) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.673 (+/-0.293) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.720 (+/-0.326) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.499 (+/-0.446) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.529 (+/-0.145) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.571 (+/-0.164) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.628 (+/-0.140) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.626 (+/-0.176) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.660 (+/-0.258) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.602 (+/-0.281) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.727 (+/-0.397) for {'C': 0.1, 'kernel': 'linear'}
0.625 (+/-0.179) for {'C': 1.0, 'kernel': 'linear'}
0.633 (+/-0.278) for {'C': 10.0, 'kernel': 'linear'}
0.598 (+/-0.284) for {'C': 100.0, 'kernel': 'linear'}
0.578 (+/-0.145) for {'C': 1000.0, 'kernel': 'linear'}
0.608 (+/-0.180) for {'C': 10000.0, 'kernel': 'linear'}
0.634 (+/-0.287) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.500 (+/-0.000) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.500 (+/-0.000) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.500 (+/-0.000) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.500 (+/-0.000) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.500 (+/-0.000) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.550 (+/-0.200) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.616 (+/-0.238) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.615 (+/-0.237) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.656 (+/-0.215) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.697 (+/-0.307) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.500 (+/-0.000) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.500 (+/-0.000) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.500 (+/-0.000) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.500 (+/-0.000) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.500 (+/-0.000) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.575 (+/-0.230) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.616 (+/-0.238) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.615 (+/-0.237) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.656 (+/-0.215) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.697 (+/-0.307) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.746 (+/-0.233) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.500 (+/-0.000) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.500 (+/-0.000) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.500 (+/-0.000) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.500 (+/-0.000) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.500 (+/-0.000) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.640 (+/-0.235) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.640 (+/-0.235) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.656 (+/-0.215) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.705 (+/-0.151) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.747 (+/-0.233) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.747 (+/-0.233) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.500 (+/-0.000) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.500 (+/-0.000) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.500 (+/-0.000) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.500 (+/-0.002) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.657 (+/-0.217) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.678 (+/-0.190) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.679 (+/-0.291) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.697 (+/-0.306) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.772 (+/-0.166) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.772 (+/-0.167) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.697 (+/-0.308) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.500 (+/-0.000) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.500 (+/-0.000) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.500 (+/-0.000) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.500 (+/-0.002) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.697 (+/-0.305) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.676 (+/-0.188) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.701 (+/-0.255) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.771 (+/-0.164) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.747 (+/-0.234) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.772 (+/-0.167) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.696 (+/-0.308) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.617 (+/-0.238) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.679 (+/-0.275) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.760 (+/-0.153) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.737 (+/-0.205) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.771 (+/-0.164) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.697 (+/-0.307) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.772 (+/-0.167) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.696 (+/-0.306) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.617 (+/-0.238) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.617 (+/-0.238) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.612 (+/-0.271) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.725 (+/-0.244) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.677 (+/-0.253) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.771 (+/-0.164) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.697 (+/-0.308) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.747 (+/-0.234) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.696 (+/-0.304) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.617 (+/-0.238) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.616 (+/-0.238) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.616 (+/-0.238) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.581 (+/-0.184) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.682 (+/-0.303) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.659 (+/-0.233) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.770 (+/-0.161) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.697 (+/-0.308) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.747 (+/-0.234) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.695 (+/-0.301) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.617 (+/-0.238) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.616 (+/-0.238) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.657 (+/-0.217) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.670 (+/-0.183) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.572 (+/-0.193) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.670 (+/-0.306) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.649 (+/-0.243) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.768 (+/-0.160) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.697 (+/-0.308) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.746 (+/-0.234) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.694 (+/-0.299) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.616 (+/-0.238) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.657 (+/-0.216) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.657 (+/-0.216) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.726 (+/-0.073) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.545 (+/-0.216) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.611 (+/-0.222) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.645 (+/-0.248) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.767 (+/-0.161) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.697 (+/-0.307) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.746 (+/-0.233) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.693 (+/-0.296) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.657 (+/-0.216) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.656 (+/-0.216) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.656 (+/-0.216) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.697 (+/-0.143) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.514 (+/-0.275) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.576 (+/-0.168) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.644 (+/-0.248) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.767 (+/-0.160) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.697 (+/-0.307) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.746 (+/-0.233) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.692 (+/-0.293) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.657 (+/-0.216) for {'C': 0.1, 'kernel': 'linear'}
0.697 (+/-0.308) for {'C': 1.0, 'kernel': 'linear'}
0.696 (+/-0.306) for {'C': 10.0, 'kernel': 'linear'}
0.691 (+/-0.298) for {'C': 100.0, 'kernel': 'linear'}
0.693 (+/-0.302) for {'C': 1000.0, 'kernel': 'linear'}
0.695 (+/-0.304) for {'C': 10000.0, 'kernel': 'linear'}
0.695 (+/-0.305) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.7721138387336136
循环迭代之前，delta is: [1.16415322e-10 5.30957344e-07]
执行tunemodel()函数前，使用的grid search parameter是：
 [{'C': array([ 630957.34448019,  691830.97091894,  758577.57502918,
        831763.77110267,  912010.83935591, 1000000.        ,
       1096478.19614319, 1202264.43461741, 1318256.73855641,
       1445439.77074593, 1584893.19246111]), 'kernel': ['rbf'], 'gamma': array([3.98107171e-07, 4.36515832e-07, 4.78630092e-07, 5.24807460e-07,
       5.75439937e-07, 6.30957344e-07, 6.91830971e-07, 7.58577575e-07,
       8.31763771e-07, 9.12010839e-07, 1.00000000e-06])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.656 (+/-0.138) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.655 (+/-0.126) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.625 (+/-0.179) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.667 (+/-0.136) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.652 (+/-0.123) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.660 (+/-0.136) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.642 (+/-0.158) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.655 (+/-0.126) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.630 (+/-0.189) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.636 (+/-0.169) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.630 (+/-0.189) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.647 (+/-0.124) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.655 (+/-0.126) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.627 (+/-0.185) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.667 (+/-0.136) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.652 (+/-0.123) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.665 (+/-0.145) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.655 (+/-0.126) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.638 (+/-0.157) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.630 (+/-0.189) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.636 (+/-0.169) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.630 (+/-0.189) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.647 (+/-0.124) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.655 (+/-0.126) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.627 (+/-0.185) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.663 (+/-0.178) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.652 (+/-0.123) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.669 (+/-0.142) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.625 (+/-0.179) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.638 (+/-0.157) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.630 (+/-0.189) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.636 (+/-0.169) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.630 (+/-0.189) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.644 (+/-0.120) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.651 (+/-0.127) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.627 (+/-0.185) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.668 (+/-0.185) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.652 (+/-0.123) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.669 (+/-0.142) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.630 (+/-0.189) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.638 (+/-0.157) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.630 (+/-0.189) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.636 (+/-0.169) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.630 (+/-0.189) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.640 (+/-0.164) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.651 (+/-0.127) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.657 (+/-0.134) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.663 (+/-0.178) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.649 (+/-0.126) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.669 (+/-0.142) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.627 (+/-0.185) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.655 (+/-0.126) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.630 (+/-0.189) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.636 (+/-0.169) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.630 (+/-0.189) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.644 (+/-0.120) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.647 (+/-0.124) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.640 (+/-0.164) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.663 (+/-0.178) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.652 (+/-0.123) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.669 (+/-0.142) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.627 (+/-0.185) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.625 (+/-0.179) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.630 (+/-0.189) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.636 (+/-0.169) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.630 (+/-0.189) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.619 (+/-0.169) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.647 (+/-0.124) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.627 (+/-0.185) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.673 (+/-0.190) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.647 (+/-0.124) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.669 (+/-0.142) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.635 (+/-0.198) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.636 (+/-0.159) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.630 (+/-0.189) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.630 (+/-0.189) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.630 (+/-0.189) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.627 (+/-0.185) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.651 (+/-0.127) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.648 (+/-0.164) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.673 (+/-0.190) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.698 (+/-0.213) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.669 (+/-0.142) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.635 (+/-0.198) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.630 (+/-0.189) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.630 (+/-0.189) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.630 (+/-0.189) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.630 (+/-0.189) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.627 (+/-0.185) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.651 (+/-0.127) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.651 (+/-0.211) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.656 (+/-0.218) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.698 (+/-0.213) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.669 (+/-0.142) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.635 (+/-0.198) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.641 (+/-0.169) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.630 (+/-0.189) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.639 (+/-0.202) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.625 (+/-0.179) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.623 (+/-0.184) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.697 (+/-0.216) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.660 (+/-0.177) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.668 (+/-0.185) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.698 (+/-0.213) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.677 (+/-0.150) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.644 (+/-0.209) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.641 (+/-0.169) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.630 (+/-0.189) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.649 (+/-0.181) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.625 (+/-0.179) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.618 (+/-0.172) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.702 (+/-0.219) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.651 (+/-0.167) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.660 (+/-0.219) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.698 (+/-0.213) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.677 (+/-0.150) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.644 (+/-0.209) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.630 (+/-0.189) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.630 (+/-0.189) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.657 (+/-0.191) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.625 (+/-0.179) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.630 (+/-0.189) for {'C': 1.0, 'kernel': 'linear'}
0.655 (+/-0.280) for {'C': 10.0, 'kernel': 'linear'}
0.645 (+/-0.280) for {'C': 100.0, 'kernel': 'linear'}
0.582 (+/-0.153) for {'C': 1000.0, 'kernel': 'linear'}
0.619 (+/-0.185) for {'C': 10000.0, 'kernel': 'linear'}
0.641 (+/-0.286) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.730 (+/-0.231) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.747 (+/-0.233) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.697 (+/-0.307) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.747 (+/-0.233) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.746 (+/-0.233) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.747 (+/-0.233) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.722 (+/-0.278) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.747 (+/-0.233) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.697 (+/-0.308) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.721 (+/-0.277) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.697 (+/-0.308) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.730 (+/-0.231) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.747 (+/-0.233) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.697 (+/-0.307) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.747 (+/-0.233) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.746 (+/-0.233) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.747 (+/-0.234) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.747 (+/-0.233) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.722 (+/-0.277) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.697 (+/-0.308) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.721 (+/-0.277) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.697 (+/-0.308) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.730 (+/-0.231) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.747 (+/-0.233) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.697 (+/-0.307) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.722 (+/-0.278) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.746 (+/-0.233) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.747 (+/-0.234) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.697 (+/-0.308) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.722 (+/-0.277) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.697 (+/-0.308) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.721 (+/-0.277) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.697 (+/-0.308) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.730 (+/-0.230) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.746 (+/-0.233) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.697 (+/-0.307) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.722 (+/-0.279) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.746 (+/-0.233) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.747 (+/-0.234) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.697 (+/-0.308) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.722 (+/-0.277) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.697 (+/-0.308) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.721 (+/-0.277) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.697 (+/-0.308) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.721 (+/-0.277) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.746 (+/-0.233) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.747 (+/-0.233) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.722 (+/-0.278) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.746 (+/-0.233) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.747 (+/-0.234) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.697 (+/-0.307) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.747 (+/-0.233) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.697 (+/-0.308) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.721 (+/-0.277) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.697 (+/-0.308) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.730 (+/-0.230) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.746 (+/-0.233) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.722 (+/-0.277) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.722 (+/-0.278) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.746 (+/-0.233) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.747 (+/-0.234) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.697 (+/-0.307) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.697 (+/-0.308) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.697 (+/-0.308) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.721 (+/-0.277) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.697 (+/-0.308) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.680 (+/-0.294) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.746 (+/-0.233) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.697 (+/-0.307) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.722 (+/-0.279) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.746 (+/-0.233) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.747 (+/-0.234) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.697 (+/-0.307) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.721 (+/-0.277) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.697 (+/-0.308) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.697 (+/-0.308) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.697 (+/-0.308) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.697 (+/-0.306) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.746 (+/-0.233) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.722 (+/-0.277) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.722 (+/-0.279) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.771 (+/-0.165) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.747 (+/-0.234) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.697 (+/-0.307) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.697 (+/-0.308) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.697 (+/-0.308) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.697 (+/-0.308) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.697 (+/-0.308) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.697 (+/-0.306) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.746 (+/-0.233) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.697 (+/-0.308) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.697 (+/-0.309) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.771 (+/-0.165) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.747 (+/-0.234) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.697 (+/-0.307) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.722 (+/-0.277) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.697 (+/-0.308) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.697 (+/-0.308) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.697 (+/-0.308) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.697 (+/-0.306) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.771 (+/-0.166) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.722 (+/-0.278) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.722 (+/-0.278) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.771 (+/-0.165) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.747 (+/-0.234) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.697 (+/-0.307) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.722 (+/-0.277) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.697 (+/-0.308) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.722 (+/-0.277) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.697 (+/-0.308) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.680 (+/-0.294) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.771 (+/-0.166) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.705 (+/-0.270) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.697 (+/-0.308) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.771 (+/-0.165) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.747 (+/-0.234) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.697 (+/-0.307) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.697 (+/-0.308) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.697 (+/-0.308) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.722 (+/-0.278) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.697 (+/-0.308) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.616 (+/-0.239) for {'C': 0.1, 'kernel': 'linear'}
0.697 (+/-0.308) for {'C': 1.0, 'kernel': 'linear'}
0.697 (+/-0.308) for {'C': 10.0, 'kernel': 'linear'}
0.697 (+/-0.307) for {'C': 100.0, 'kernel': 'linear'}
0.693 (+/-0.302) for {'C': 1000.0, 'kernel': 'linear'}
0.696 (+/-0.307) for {'C': 10000.0, 'kernel': 'linear'}
0.696 (+/-0.307) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.7714728130925881
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.689 (+/-0.248) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.697 (+/-0.216) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.661 (+/-0.132) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.651 (+/-0.211) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.707 (+/-0.211) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.715 (+/-0.210) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.639 (+/-0.202) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.631 (+/-0.155) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.627 (+/-0.185) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.630 (+/-0.189) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.621 (+/-0.177) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.623 (+/-0.184) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.697 (+/-0.216) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.656 (+/-0.178) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.651 (+/-0.211) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.703 (+/-0.215) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.723 (+/-0.208) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.639 (+/-0.202) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.631 (+/-0.156) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.625 (+/-0.184) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.636 (+/-0.203) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.621 (+/-0.177) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.619 (+/-0.181) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.702 (+/-0.219) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.664 (+/-0.187) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.660 (+/-0.219) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.698 (+/-0.213) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.718 (+/-0.208) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.647 (+/-0.212) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.631 (+/-0.156) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.625 (+/-0.184) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.631 (+/-0.199) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.615 (+/-0.167) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.623 (+/-0.184) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.706 (+/-0.214) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.660 (+/-0.177) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.660 (+/-0.219) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.698 (+/-0.213) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.718 (+/-0.208) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.634 (+/-0.193) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.609 (+/-0.170) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.625 (+/-0.184) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.626 (+/-0.197) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.616 (+/-0.170) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.623 (+/-0.184) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.706 (+/-0.214) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.668 (+/-0.185) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.660 (+/-0.219) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.698 (+/-0.213) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.718 (+/-0.208) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.634 (+/-0.193) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.609 (+/-0.170) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.622 (+/-0.184) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.631 (+/-0.199) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.616 (+/-0.170) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.626 (+/-0.188) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.703 (+/-0.218) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.668 (+/-0.185) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.660 (+/-0.219) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.693 (+/-0.217) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.723 (+/-0.208) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.630 (+/-0.189) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.608 (+/-0.171) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.622 (+/-0.184) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.631 (+/-0.199) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.616 (+/-0.170) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.626 (+/-0.188) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.706 (+/-0.214) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.668 (+/-0.185) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.660 (+/-0.219) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.693 (+/-0.217) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.668 (+/-0.146) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.630 (+/-0.189) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.608 (+/-0.171) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.630 (+/-0.198) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.627 (+/-0.199) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.612 (+/-0.169) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.630 (+/-0.189) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.714 (+/-0.213) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.668 (+/-0.185) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.660 (+/-0.219) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.693 (+/-0.217) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.660 (+/-0.136) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.626 (+/-0.188) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.607 (+/-0.172) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.624 (+/-0.197) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.625 (+/-0.200) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.617 (+/-0.181) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.635 (+/-0.190) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.711 (+/-0.215) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.668 (+/-0.185) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.660 (+/-0.219) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.697 (+/-0.216) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.660 (+/-0.136) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.626 (+/-0.188) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.604 (+/-0.166) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.623 (+/-0.198) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.624 (+/-0.201) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.617 (+/-0.181) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.635 (+/-0.190) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.711 (+/-0.215) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.668 (+/-0.185) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.660 (+/-0.219) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.698 (+/-0.213) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.656 (+/-0.138) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.626 (+/-0.188) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.601 (+/-0.166) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.617 (+/-0.188) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.615 (+/-0.186) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.607 (+/-0.163) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.635 (+/-0.190) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.714 (+/-0.213) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.651 (+/-0.211) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.660 (+/-0.219) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.693 (+/-0.217) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.656 (+/-0.138) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.626 (+/-0.188) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.601 (+/-0.166) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.617 (+/-0.188) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.615 (+/-0.186) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.602 (+/-0.159) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.727 (+/-0.397) for {'C': 0.1, 'kernel': 'linear'}
0.625 (+/-0.179) for {'C': 1.0, 'kernel': 'linear'}
0.633 (+/-0.278) for {'C': 10.0, 'kernel': 'linear'}
0.598 (+/-0.284) for {'C': 100.0, 'kernel': 'linear'}
0.578 (+/-0.145) for {'C': 1000.0, 'kernel': 'linear'}
0.608 (+/-0.180) for {'C': 10000.0, 'kernel': 'linear'}
0.634 (+/-0.287) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.747 (+/-0.234) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.771 (+/-0.166) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.747 (+/-0.233) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.697 (+/-0.308) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.772 (+/-0.165) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.772 (+/-0.167) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.697 (+/-0.308) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.721 (+/-0.274) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.697 (+/-0.307) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.696 (+/-0.309) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.696 (+/-0.308) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.697 (+/-0.306) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.771 (+/-0.166) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.722 (+/-0.279) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.697 (+/-0.308) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.771 (+/-0.165) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.772 (+/-0.167) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.697 (+/-0.308) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.720 (+/-0.274) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.696 (+/-0.307) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.696 (+/-0.309) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.696 (+/-0.308) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.697 (+/-0.306) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.771 (+/-0.166) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.722 (+/-0.278) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.697 (+/-0.308) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.771 (+/-0.165) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.772 (+/-0.167) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.697 (+/-0.308) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.720 (+/-0.274) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.696 (+/-0.307) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.696 (+/-0.308) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.696 (+/-0.307) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.697 (+/-0.306) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.772 (+/-0.166) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.722 (+/-0.278) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.697 (+/-0.308) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.771 (+/-0.165) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.772 (+/-0.167) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.697 (+/-0.307) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.695 (+/-0.303) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.696 (+/-0.307) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.696 (+/-0.307) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.696 (+/-0.306) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.697 (+/-0.306) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.772 (+/-0.166) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.722 (+/-0.278) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.698 (+/-0.308) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.771 (+/-0.165) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.772 (+/-0.167) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.697 (+/-0.307) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.695 (+/-0.303) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.696 (+/-0.305) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.696 (+/-0.308) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.696 (+/-0.306) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.697 (+/-0.307) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.771 (+/-0.166) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.722 (+/-0.278) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.698 (+/-0.308) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.771 (+/-0.165) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.772 (+/-0.167) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.697 (+/-0.307) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.695 (+/-0.302) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.696 (+/-0.305) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.696 (+/-0.308) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.696 (+/-0.306) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.697 (+/-0.307) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.772 (+/-0.166) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.722 (+/-0.278) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.698 (+/-0.308) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.771 (+/-0.165) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.747 (+/-0.234) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.697 (+/-0.307) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.695 (+/-0.302) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.696 (+/-0.306) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.696 (+/-0.306) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.696 (+/-0.305) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.697 (+/-0.308) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.772 (+/-0.166) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.722 (+/-0.278) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.698 (+/-0.308) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.771 (+/-0.165) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.747 (+/-0.234) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.697 (+/-0.307) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.695 (+/-0.301) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.696 (+/-0.304) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.696 (+/-0.305) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.696 (+/-0.304) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.697 (+/-0.308) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.772 (+/-0.165) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.722 (+/-0.278) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.698 (+/-0.308) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.771 (+/-0.166) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.747 (+/-0.234) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.697 (+/-0.307) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.695 (+/-0.301) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.696 (+/-0.304) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.695 (+/-0.305) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.696 (+/-0.304) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.697 (+/-0.308) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.772 (+/-0.165) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.722 (+/-0.278) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.698 (+/-0.308) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.771 (+/-0.166) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.747 (+/-0.234) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.697 (+/-0.307) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.694 (+/-0.299) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.695 (+/-0.303) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.695 (+/-0.305) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.696 (+/-0.304) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.697 (+/-0.308) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.772 (+/-0.165) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.697 (+/-0.308) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.698 (+/-0.308) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.771 (+/-0.166) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.747 (+/-0.234) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.697 (+/-0.307) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.694 (+/-0.300) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.695 (+/-0.303) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.695 (+/-0.306) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.696 (+/-0.304) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.657 (+/-0.216) for {'C': 0.1, 'kernel': 'linear'}
0.697 (+/-0.308) for {'C': 1.0, 'kernel': 'linear'}
0.696 (+/-0.306) for {'C': 10.0, 'kernel': 'linear'}
0.691 (+/-0.298) for {'C': 100.0, 'kernel': 'linear'}
0.693 (+/-0.302) for {'C': 1000.0, 'kernel': 'linear'}
0.695 (+/-0.304) for {'C': 10000.0, 'kernel': 'linear'}
0.695 (+/-0.305) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.7721138387336136
第0轮loop中，tunemodel函数得到的最优参数是： ([{'C': array([630957.34448019, 642687.71731702, 654636.17406727, 666806.76921362,
       679203.63261718, 691830.97091894, 704693.06896715, 717794.29127136,
       731139.08348342, 744731.97390599, 758577.57502918]), 'kernel': ['rbf'], 'gamma': array([5.75439937e-07, 5.86138165e-07, 5.97035287e-07, 6.08135001e-07,
       6.19441075e-07, 6.30957344e-07, 6.42687717e-07, 6.54636174e-07,
       6.66806769e-07, 6.79203633e-07, 6.91830971e-07])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}], {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}, 0.7721138387336136)
这是第0次迭代微调C和gamma。
第0次迭代，得到delta： [308169.02908106      0.        ]
执行tunemodel()函数前，使用的grid search parameter是：
 [{'C': array([630957.34448019, 642687.71731702, 654636.17406727, 666806.76921362,
       679203.63261718, 691830.97091894, 704693.06896715, 717794.29127136,
       731139.08348342, 744731.97390599, 758577.57502918]), 'kernel': ['rbf'], 'gamma': array([5.75439937e-07, 5.86138165e-07, 5.97035287e-07, 6.08135001e-07,
       6.19441075e-07, 6.30957344e-07, 6.42687717e-07, 6.54636174e-07,
       6.66806769e-07, 6.79203633e-07, 6.91830971e-07])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.652 (+/-0.123) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.635 (+/-0.153) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 5.861381645140294e-07}
0.635 (+/-0.153) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 5.970352865838373e-07}
0.638 (+/-0.157) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.081350012787182e-07}
0.655 (+/-0.126) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.194410750767815e-07}
0.660 (+/-0.136) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.657 (+/-0.134) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.426877173170207e-07}
0.660 (+/-0.136) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.546361740672759e-07}
0.635 (+/-0.153) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.668067692136228e-07}
0.635 (+/-0.153) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.79203632617185e-07}
0.642 (+/-0.158) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.635 (+/-0.153) for {'C': 642687.7173170191, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.635 (+/-0.153) for {'C': 642687.7173170191, 'kernel': 'rbf', 'gamma': 5.861381645140294e-07}
0.635 (+/-0.153) for {'C': 642687.7173170191, 'kernel': 'rbf', 'gamma': 5.970352865838373e-07}
0.638 (+/-0.157) for {'C': 642687.7173170191, 'kernel': 'rbf', 'gamma': 6.081350012787182e-07}
0.655 (+/-0.126) for {'C': 642687.7173170191, 'kernel': 'rbf', 'gamma': 6.194410750767815e-07}
0.660 (+/-0.136) for {'C': 642687.7173170191, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.657 (+/-0.134) for {'C': 642687.7173170191, 'kernel': 'rbf', 'gamma': 6.426877173170207e-07}
0.660 (+/-0.136) for {'C': 642687.7173170191, 'kernel': 'rbf', 'gamma': 6.546361740672759e-07}
0.635 (+/-0.153) for {'C': 642687.7173170191, 'kernel': 'rbf', 'gamma': 6.668067692136228e-07}
0.635 (+/-0.153) for {'C': 642687.7173170191, 'kernel': 'rbf', 'gamma': 6.79203632617185e-07}
0.655 (+/-0.126) for {'C': 642687.7173170191, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.652 (+/-0.123) for {'C': 654636.1740672742, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.635 (+/-0.153) for {'C': 654636.1740672742, 'kernel': 'rbf', 'gamma': 5.861381645140294e-07}
0.635 (+/-0.153) for {'C': 654636.1740672742, 'kernel': 'rbf', 'gamma': 5.970352865838373e-07}
0.638 (+/-0.157) for {'C': 654636.1740672742, 'kernel': 'rbf', 'gamma': 6.081350012787182e-07}
0.655 (+/-0.126) for {'C': 654636.1740672742, 'kernel': 'rbf', 'gamma': 6.194410750767815e-07}
0.660 (+/-0.136) for {'C': 654636.1740672742, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.657 (+/-0.134) for {'C': 654636.1740672742, 'kernel': 'rbf', 'gamma': 6.426877173170207e-07}
0.660 (+/-0.136) for {'C': 654636.1740672742, 'kernel': 'rbf', 'gamma': 6.546361740672759e-07}
0.635 (+/-0.153) for {'C': 654636.1740672742, 'kernel': 'rbf', 'gamma': 6.668067692136228e-07}
0.635 (+/-0.153) for {'C': 654636.1740672742, 'kernel': 'rbf', 'gamma': 6.79203632617185e-07}
0.655 (+/-0.126) for {'C': 654636.1740672742, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.652 (+/-0.123) for {'C': 666806.7692136227, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.635 (+/-0.153) for {'C': 666806.7692136227, 'kernel': 'rbf', 'gamma': 5.861381645140294e-07}
0.635 (+/-0.153) for {'C': 666806.7692136227, 'kernel': 'rbf', 'gamma': 5.970352865838373e-07}
0.638 (+/-0.157) for {'C': 666806.7692136227, 'kernel': 'rbf', 'gamma': 6.081350012787182e-07}
0.655 (+/-0.126) for {'C': 666806.7692136227, 'kernel': 'rbf', 'gamma': 6.194410750767815e-07}
0.660 (+/-0.136) for {'C': 666806.7692136227, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.657 (+/-0.134) for {'C': 666806.7692136227, 'kernel': 'rbf', 'gamma': 6.426877173170207e-07}
0.660 (+/-0.136) for {'C': 666806.7692136227, 'kernel': 'rbf', 'gamma': 6.546361740672759e-07}
0.635 (+/-0.153) for {'C': 666806.7692136227, 'kernel': 'rbf', 'gamma': 6.668067692136228e-07}
0.635 (+/-0.153) for {'C': 666806.7692136227, 'kernel': 'rbf', 'gamma': 6.79203632617185e-07}
0.655 (+/-0.126) for {'C': 666806.7692136227, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.635 (+/-0.153) for {'C': 679203.6326171849, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.635 (+/-0.153) for {'C': 679203.6326171849, 'kernel': 'rbf', 'gamma': 5.861381645140294e-07}
0.635 (+/-0.153) for {'C': 679203.6326171849, 'kernel': 'rbf', 'gamma': 5.970352865838373e-07}
0.638 (+/-0.157) for {'C': 679203.6326171849, 'kernel': 'rbf', 'gamma': 6.081350012787182e-07}
0.660 (+/-0.136) for {'C': 679203.6326171849, 'kernel': 'rbf', 'gamma': 6.194410750767815e-07}
0.660 (+/-0.136) for {'C': 679203.6326171849, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.657 (+/-0.134) for {'C': 679203.6326171849, 'kernel': 'rbf', 'gamma': 6.426877173170207e-07}
0.660 (+/-0.136) for {'C': 679203.6326171849, 'kernel': 'rbf', 'gamma': 6.546361740672759e-07}
0.635 (+/-0.153) for {'C': 679203.6326171849, 'kernel': 'rbf', 'gamma': 6.668067692136228e-07}
0.635 (+/-0.153) for {'C': 679203.6326171849, 'kernel': 'rbf', 'gamma': 6.79203632617185e-07}
0.655 (+/-0.126) for {'C': 679203.6326171849, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.652 (+/-0.123) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.635 (+/-0.153) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 5.861381645140294e-07}
0.652 (+/-0.123) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 5.970352865838373e-07}
0.655 (+/-0.126) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 6.081350012787182e-07}
0.660 (+/-0.136) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 6.194410750767815e-07}
0.665 (+/-0.145) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.657 (+/-0.134) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 6.426877173170207e-07}
0.660 (+/-0.136) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 6.546361740672759e-07}
0.635 (+/-0.153) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 6.668067692136228e-07}
0.635 (+/-0.153) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 6.79203632617185e-07}
0.655 (+/-0.126) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.652 (+/-0.123) for {'C': 704693.0689671467, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.635 (+/-0.153) for {'C': 704693.0689671467, 'kernel': 'rbf', 'gamma': 5.861381645140294e-07}
0.652 (+/-0.123) for {'C': 704693.0689671467, 'kernel': 'rbf', 'gamma': 5.970352865838373e-07}
0.655 (+/-0.126) for {'C': 704693.0689671467, 'kernel': 'rbf', 'gamma': 6.081350012787182e-07}
0.660 (+/-0.136) for {'C': 704693.0689671467, 'kernel': 'rbf', 'gamma': 6.194410750767815e-07}
0.665 (+/-0.145) for {'C': 704693.0689671467, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.657 (+/-0.134) for {'C': 704693.0689671467, 'kernel': 'rbf', 'gamma': 6.426877173170207e-07}
0.660 (+/-0.136) for {'C': 704693.0689671467, 'kernel': 'rbf', 'gamma': 6.546361740672759e-07}
0.635 (+/-0.153) for {'C': 704693.0689671467, 'kernel': 'rbf', 'gamma': 6.668067692136228e-07}
0.635 (+/-0.153) for {'C': 704693.0689671467, 'kernel': 'rbf', 'gamma': 6.79203632617185e-07}
0.655 (+/-0.126) for {'C': 704693.0689671467, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.652 (+/-0.123) for {'C': 717794.2912713613, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.652 (+/-0.123) for {'C': 717794.2912713613, 'kernel': 'rbf', 'gamma': 5.861381645140294e-07}
0.652 (+/-0.123) for {'C': 717794.2912713613, 'kernel': 'rbf', 'gamma': 5.970352865838373e-07}
0.655 (+/-0.126) for {'C': 717794.2912713613, 'kernel': 'rbf', 'gamma': 6.081350012787182e-07}
0.660 (+/-0.136) for {'C': 717794.2912713613, 'kernel': 'rbf', 'gamma': 6.194410750767815e-07}
0.665 (+/-0.145) for {'C': 717794.2912713613, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.657 (+/-0.134) for {'C': 717794.2912713613, 'kernel': 'rbf', 'gamma': 6.426877173170207e-07}
0.660 (+/-0.136) for {'C': 717794.2912713613, 'kernel': 'rbf', 'gamma': 6.546361740672759e-07}
0.635 (+/-0.153) for {'C': 717794.2912713613, 'kernel': 'rbf', 'gamma': 6.668067692136228e-07}
0.622 (+/-0.174) for {'C': 717794.2912713613, 'kernel': 'rbf', 'gamma': 6.79203632617185e-07}
0.655 (+/-0.126) for {'C': 717794.2912713613, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.652 (+/-0.123) for {'C': 731139.0834834184, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.652 (+/-0.123) for {'C': 731139.0834834184, 'kernel': 'rbf', 'gamma': 5.861381645140294e-07}
0.652 (+/-0.123) for {'C': 731139.0834834184, 'kernel': 'rbf', 'gamma': 5.970352865838373e-07}
0.655 (+/-0.126) for {'C': 731139.0834834184, 'kernel': 'rbf', 'gamma': 6.081350012787182e-07}
0.660 (+/-0.136) for {'C': 731139.0834834184, 'kernel': 'rbf', 'gamma': 6.194410750767815e-07}
0.665 (+/-0.145) for {'C': 731139.0834834184, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.657 (+/-0.134) for {'C': 731139.0834834184, 'kernel': 'rbf', 'gamma': 6.426877173170207e-07}
0.660 (+/-0.136) for {'C': 731139.0834834184, 'kernel': 'rbf', 'gamma': 6.546361740672759e-07}
0.622 (+/-0.174) for {'C': 731139.0834834184, 'kernel': 'rbf', 'gamma': 6.668067692136228e-07}
0.622 (+/-0.174) for {'C': 731139.0834834184, 'kernel': 'rbf', 'gamma': 6.79203632617185e-07}
0.655 (+/-0.126) for {'C': 731139.0834834184, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.652 (+/-0.123) for {'C': 744731.9739059898, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.652 (+/-0.123) for {'C': 744731.9739059898, 'kernel': 'rbf', 'gamma': 5.861381645140294e-07}
0.652 (+/-0.123) for {'C': 744731.9739059898, 'kernel': 'rbf', 'gamma': 5.970352865838373e-07}
0.660 (+/-0.136) for {'C': 744731.9739059898, 'kernel': 'rbf', 'gamma': 6.081350012787182e-07}
0.660 (+/-0.136) for {'C': 744731.9739059898, 'kernel': 'rbf', 'gamma': 6.194410750767815e-07}
0.665 (+/-0.145) for {'C': 744731.9739059898, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.657 (+/-0.134) for {'C': 744731.9739059898, 'kernel': 'rbf', 'gamma': 6.426877173170207e-07}
0.660 (+/-0.136) for {'C': 744731.9739059898, 'kernel': 'rbf', 'gamma': 6.546361740672759e-07}
0.622 (+/-0.174) for {'C': 744731.9739059898, 'kernel': 'rbf', 'gamma': 6.668067692136228e-07}
0.622 (+/-0.174) for {'C': 744731.9739059898, 'kernel': 'rbf', 'gamma': 6.79203632617185e-07}
0.638 (+/-0.157) for {'C': 744731.9739059898, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.652 (+/-0.123) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.652 (+/-0.123) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 5.861381645140294e-07}
0.652 (+/-0.123) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 5.970352865838373e-07}
0.660 (+/-0.136) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 6.081350012787182e-07}
0.660 (+/-0.136) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 6.194410750767815e-07}
0.669 (+/-0.142) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.657 (+/-0.134) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 6.426877173170207e-07}
0.665 (+/-0.145) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 6.546361740672759e-07}
0.622 (+/-0.174) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 6.668067692136228e-07}
0.622 (+/-0.174) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 6.79203632617185e-07}
0.625 (+/-0.179) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.630 (+/-0.189) for {'C': 1.0, 'kernel': 'linear'}
0.655 (+/-0.280) for {'C': 10.0, 'kernel': 'linear'}
0.645 (+/-0.280) for {'C': 100.0, 'kernel': 'linear'}
0.582 (+/-0.153) for {'C': 1000.0, 'kernel': 'linear'}
0.619 (+/-0.185) for {'C': 10000.0, 'kernel': 'linear'}
0.641 (+/-0.286) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.746 (+/-0.233) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.721 (+/-0.277) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 5.861381645140294e-07}
0.721 (+/-0.277) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 5.970352865838373e-07}
0.722 (+/-0.277) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.081350012787182e-07}
0.747 (+/-0.233) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.194410750767815e-07}
0.747 (+/-0.233) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.747 (+/-0.233) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.426877173170207e-07}
0.747 (+/-0.233) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.546361740672759e-07}
0.721 (+/-0.277) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.668067692136228e-07}
0.721 (+/-0.277) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.79203632617185e-07}
0.722 (+/-0.278) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.721 (+/-0.277) for {'C': 642687.7173170191, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.721 (+/-0.277) for {'C': 642687.7173170191, 'kernel': 'rbf', 'gamma': 5.861381645140294e-07}
0.721 (+/-0.277) for {'C': 642687.7173170191, 'kernel': 'rbf', 'gamma': 5.970352865838373e-07}
0.722 (+/-0.277) for {'C': 642687.7173170191, 'kernel': 'rbf', 'gamma': 6.081350012787182e-07}
0.747 (+/-0.233) for {'C': 642687.7173170191, 'kernel': 'rbf', 'gamma': 6.194410750767815e-07}
0.747 (+/-0.233) for {'C': 642687.7173170191, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.747 (+/-0.233) for {'C': 642687.7173170191, 'kernel': 'rbf', 'gamma': 6.426877173170207e-07}
0.747 (+/-0.233) for {'C': 642687.7173170191, 'kernel': 'rbf', 'gamma': 6.546361740672759e-07}
0.721 (+/-0.277) for {'C': 642687.7173170191, 'kernel': 'rbf', 'gamma': 6.668067692136228e-07}
0.721 (+/-0.277) for {'C': 642687.7173170191, 'kernel': 'rbf', 'gamma': 6.79203632617185e-07}
0.747 (+/-0.233) for {'C': 642687.7173170191, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.746 (+/-0.233) for {'C': 654636.1740672742, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.721 (+/-0.277) for {'C': 654636.1740672742, 'kernel': 'rbf', 'gamma': 5.861381645140294e-07}
0.721 (+/-0.277) for {'C': 654636.1740672742, 'kernel': 'rbf', 'gamma': 5.970352865838373e-07}
0.722 (+/-0.277) for {'C': 654636.1740672742, 'kernel': 'rbf', 'gamma': 6.081350012787182e-07}
0.747 (+/-0.233) for {'C': 654636.1740672742, 'kernel': 'rbf', 'gamma': 6.194410750767815e-07}
0.747 (+/-0.233) for {'C': 654636.1740672742, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.747 (+/-0.233) for {'C': 654636.1740672742, 'kernel': 'rbf', 'gamma': 6.426877173170207e-07}
0.747 (+/-0.233) for {'C': 654636.1740672742, 'kernel': 'rbf', 'gamma': 6.546361740672759e-07}
0.721 (+/-0.277) for {'C': 654636.1740672742, 'kernel': 'rbf', 'gamma': 6.668067692136228e-07}
0.721 (+/-0.277) for {'C': 654636.1740672742, 'kernel': 'rbf', 'gamma': 6.79203632617185e-07}
0.747 (+/-0.233) for {'C': 654636.1740672742, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.746 (+/-0.233) for {'C': 666806.7692136227, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.721 (+/-0.277) for {'C': 666806.7692136227, 'kernel': 'rbf', 'gamma': 5.861381645140294e-07}
0.721 (+/-0.277) for {'C': 666806.7692136227, 'kernel': 'rbf', 'gamma': 5.970352865838373e-07}
0.722 (+/-0.277) for {'C': 666806.7692136227, 'kernel': 'rbf', 'gamma': 6.081350012787182e-07}
0.747 (+/-0.233) for {'C': 666806.7692136227, 'kernel': 'rbf', 'gamma': 6.194410750767815e-07}
0.747 (+/-0.233) for {'C': 666806.7692136227, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.747 (+/-0.233) for {'C': 666806.7692136227, 'kernel': 'rbf', 'gamma': 6.426877173170207e-07}
0.747 (+/-0.233) for {'C': 666806.7692136227, 'kernel': 'rbf', 'gamma': 6.546361740672759e-07}
0.721 (+/-0.277) for {'C': 666806.7692136227, 'kernel': 'rbf', 'gamma': 6.668067692136228e-07}
0.721 (+/-0.277) for {'C': 666806.7692136227, 'kernel': 'rbf', 'gamma': 6.79203632617185e-07}
0.747 (+/-0.233) for {'C': 666806.7692136227, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.721 (+/-0.277) for {'C': 679203.6326171849, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.721 (+/-0.277) for {'C': 679203.6326171849, 'kernel': 'rbf', 'gamma': 5.861381645140294e-07}
0.721 (+/-0.277) for {'C': 679203.6326171849, 'kernel': 'rbf', 'gamma': 5.970352865838373e-07}
0.722 (+/-0.277) for {'C': 679203.6326171849, 'kernel': 'rbf', 'gamma': 6.081350012787182e-07}
0.747 (+/-0.234) for {'C': 679203.6326171849, 'kernel': 'rbf', 'gamma': 6.194410750767815e-07}
0.747 (+/-0.233) for {'C': 679203.6326171849, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.747 (+/-0.233) for {'C': 679203.6326171849, 'kernel': 'rbf', 'gamma': 6.426877173170207e-07}
0.747 (+/-0.233) for {'C': 679203.6326171849, 'kernel': 'rbf', 'gamma': 6.546361740672759e-07}
0.721 (+/-0.277) for {'C': 679203.6326171849, 'kernel': 'rbf', 'gamma': 6.668067692136228e-07}
0.721 (+/-0.277) for {'C': 679203.6326171849, 'kernel': 'rbf', 'gamma': 6.79203632617185e-07}
0.747 (+/-0.233) for {'C': 679203.6326171849, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.746 (+/-0.233) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.721 (+/-0.277) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 5.861381645140294e-07}
0.746 (+/-0.233) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 5.970352865838373e-07}
0.747 (+/-0.233) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 6.081350012787182e-07}
0.747 (+/-0.234) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 6.194410750767815e-07}
0.747 (+/-0.234) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.747 (+/-0.233) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 6.426877173170207e-07}
0.747 (+/-0.233) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 6.546361740672759e-07}
0.721 (+/-0.277) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 6.668067692136228e-07}
0.721 (+/-0.277) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 6.79203632617185e-07}
0.747 (+/-0.233) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.746 (+/-0.233) for {'C': 704693.0689671467, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.721 (+/-0.277) for {'C': 704693.0689671467, 'kernel': 'rbf', 'gamma': 5.861381645140294e-07}
0.746 (+/-0.233) for {'C': 704693.0689671467, 'kernel': 'rbf', 'gamma': 5.970352865838373e-07}
0.747 (+/-0.233) for {'C': 704693.0689671467, 'kernel': 'rbf', 'gamma': 6.081350012787182e-07}
0.747 (+/-0.234) for {'C': 704693.0689671467, 'kernel': 'rbf', 'gamma': 6.194410750767815e-07}
0.747 (+/-0.234) for {'C': 704693.0689671467, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.747 (+/-0.233) for {'C': 704693.0689671467, 'kernel': 'rbf', 'gamma': 6.426877173170207e-07}
0.747 (+/-0.233) for {'C': 704693.0689671467, 'kernel': 'rbf', 'gamma': 6.546361740672759e-07}
0.721 (+/-0.277) for {'C': 704693.0689671467, 'kernel': 'rbf', 'gamma': 6.668067692136228e-07}
0.721 (+/-0.277) for {'C': 704693.0689671467, 'kernel': 'rbf', 'gamma': 6.79203632617185e-07}
0.747 (+/-0.233) for {'C': 704693.0689671467, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.746 (+/-0.233) for {'C': 717794.2912713613, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.746 (+/-0.233) for {'C': 717794.2912713613, 'kernel': 'rbf', 'gamma': 5.861381645140294e-07}
0.746 (+/-0.233) for {'C': 717794.2912713613, 'kernel': 'rbf', 'gamma': 5.970352865838373e-07}
0.747 (+/-0.233) for {'C': 717794.2912713613, 'kernel': 'rbf', 'gamma': 6.081350012787182e-07}
0.747 (+/-0.234) for {'C': 717794.2912713613, 'kernel': 'rbf', 'gamma': 6.194410750767815e-07}
0.747 (+/-0.234) for {'C': 717794.2912713613, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.747 (+/-0.233) for {'C': 717794.2912713613, 'kernel': 'rbf', 'gamma': 6.426877173170207e-07}
0.747 (+/-0.233) for {'C': 717794.2912713613, 'kernel': 'rbf', 'gamma': 6.546361740672759e-07}
0.721 (+/-0.277) for {'C': 717794.2912713613, 'kernel': 'rbf', 'gamma': 6.668067692136228e-07}
0.696 (+/-0.307) for {'C': 717794.2912713613, 'kernel': 'rbf', 'gamma': 6.79203632617185e-07}
0.747 (+/-0.233) for {'C': 717794.2912713613, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.746 (+/-0.233) for {'C': 731139.0834834184, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.746 (+/-0.233) for {'C': 731139.0834834184, 'kernel': 'rbf', 'gamma': 5.861381645140294e-07}
0.746 (+/-0.233) for {'C': 731139.0834834184, 'kernel': 'rbf', 'gamma': 5.970352865838373e-07}
0.747 (+/-0.233) for {'C': 731139.0834834184, 'kernel': 'rbf', 'gamma': 6.081350012787182e-07}
0.747 (+/-0.234) for {'C': 731139.0834834184, 'kernel': 'rbf', 'gamma': 6.194410750767815e-07}
0.747 (+/-0.234) for {'C': 731139.0834834184, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.747 (+/-0.233) for {'C': 731139.0834834184, 'kernel': 'rbf', 'gamma': 6.426877173170207e-07}
0.747 (+/-0.233) for {'C': 731139.0834834184, 'kernel': 'rbf', 'gamma': 6.546361740672759e-07}
0.696 (+/-0.307) for {'C': 731139.0834834184, 'kernel': 'rbf', 'gamma': 6.668067692136228e-07}
0.696 (+/-0.307) for {'C': 731139.0834834184, 'kernel': 'rbf', 'gamma': 6.79203632617185e-07}
0.747 (+/-0.233) for {'C': 731139.0834834184, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.746 (+/-0.233) for {'C': 744731.9739059898, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.746 (+/-0.233) for {'C': 744731.9739059898, 'kernel': 'rbf', 'gamma': 5.861381645140294e-07}
0.746 (+/-0.233) for {'C': 744731.9739059898, 'kernel': 'rbf', 'gamma': 5.970352865838373e-07}
0.747 (+/-0.234) for {'C': 744731.9739059898, 'kernel': 'rbf', 'gamma': 6.081350012787182e-07}
0.747 (+/-0.234) for {'C': 744731.9739059898, 'kernel': 'rbf', 'gamma': 6.194410750767815e-07}
0.747 (+/-0.234) for {'C': 744731.9739059898, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.747 (+/-0.233) for {'C': 744731.9739059898, 'kernel': 'rbf', 'gamma': 6.426877173170207e-07}
0.747 (+/-0.233) for {'C': 744731.9739059898, 'kernel': 'rbf', 'gamma': 6.546361740672759e-07}
0.696 (+/-0.307) for {'C': 744731.9739059898, 'kernel': 'rbf', 'gamma': 6.668067692136228e-07}
0.696 (+/-0.307) for {'C': 744731.9739059898, 'kernel': 'rbf', 'gamma': 6.79203632617185e-07}
0.722 (+/-0.277) for {'C': 744731.9739059898, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.746 (+/-0.233) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.746 (+/-0.233) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 5.861381645140294e-07}
0.746 (+/-0.233) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 5.970352865838373e-07}
0.747 (+/-0.234) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 6.081350012787182e-07}
0.747 (+/-0.234) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 6.194410750767815e-07}
0.747 (+/-0.234) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.747 (+/-0.233) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 6.426877173170207e-07}
0.747 (+/-0.234) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 6.546361740672759e-07}
0.696 (+/-0.307) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 6.668067692136228e-07}
0.696 (+/-0.307) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 6.79203632617185e-07}
0.697 (+/-0.308) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.616 (+/-0.239) for {'C': 0.1, 'kernel': 'linear'}
0.697 (+/-0.308) for {'C': 1.0, 'kernel': 'linear'}
0.697 (+/-0.308) for {'C': 10.0, 'kernel': 'linear'}
0.697 (+/-0.307) for {'C': 100.0, 'kernel': 'linear'}
0.693 (+/-0.302) for {'C': 1000.0, 'kernel': 'linear'}
0.696 (+/-0.307) for {'C': 10000.0, 'kernel': 'linear'}
0.696 (+/-0.307) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.7471138387336137
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.707 (+/-0.211) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.702 (+/-0.209) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 5.861381645140294e-07}
0.707 (+/-0.211) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 5.970352865838373e-07}
0.710 (+/-0.209) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.081350012787182e-07}
0.715 (+/-0.210) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.194410750767815e-07}
0.715 (+/-0.210) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.657 (+/-0.134) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.426877173170207e-07}
0.643 (+/-0.167) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.546361740672759e-07}
0.643 (+/-0.167) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.668067692136228e-07}
0.635 (+/-0.198) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.79203632617185e-07}
0.639 (+/-0.202) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.707 (+/-0.211) for {'C': 642687.7173170191, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.707 (+/-0.211) for {'C': 642687.7173170191, 'kernel': 'rbf', 'gamma': 5.861381645140294e-07}
0.707 (+/-0.211) for {'C': 642687.7173170191, 'kernel': 'rbf', 'gamma': 5.970352865838373e-07}
0.710 (+/-0.209) for {'C': 642687.7173170191, 'kernel': 'rbf', 'gamma': 6.081350012787182e-07}
0.715 (+/-0.210) for {'C': 642687.7173170191, 'kernel': 'rbf', 'gamma': 6.194410750767815e-07}
0.715 (+/-0.210) for {'C': 642687.7173170191, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.657 (+/-0.134) for {'C': 642687.7173170191, 'kernel': 'rbf', 'gamma': 6.426877173170207e-07}
0.643 (+/-0.167) for {'C': 642687.7173170191, 'kernel': 'rbf', 'gamma': 6.546361740672759e-07}
0.643 (+/-0.167) for {'C': 642687.7173170191, 'kernel': 'rbf', 'gamma': 6.668067692136228e-07}
0.635 (+/-0.198) for {'C': 642687.7173170191, 'kernel': 'rbf', 'gamma': 6.79203632617185e-07}
0.639 (+/-0.202) for {'C': 642687.7173170191, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.707 (+/-0.211) for {'C': 654636.1740672742, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.707 (+/-0.211) for {'C': 654636.1740672742, 'kernel': 'rbf', 'gamma': 5.861381645140294e-07}
0.707 (+/-0.211) for {'C': 654636.1740672742, 'kernel': 'rbf', 'gamma': 5.970352865838373e-07}
0.710 (+/-0.209) for {'C': 654636.1740672742, 'kernel': 'rbf', 'gamma': 6.081350012787182e-07}
0.715 (+/-0.210) for {'C': 654636.1740672742, 'kernel': 'rbf', 'gamma': 6.194410750767815e-07}
0.715 (+/-0.210) for {'C': 654636.1740672742, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.660 (+/-0.136) for {'C': 654636.1740672742, 'kernel': 'rbf', 'gamma': 6.426877173170207e-07}
0.643 (+/-0.167) for {'C': 654636.1740672742, 'kernel': 'rbf', 'gamma': 6.546361740672759e-07}
0.643 (+/-0.167) for {'C': 654636.1740672742, 'kernel': 'rbf', 'gamma': 6.668067692136228e-07}
0.635 (+/-0.198) for {'C': 654636.1740672742, 'kernel': 'rbf', 'gamma': 6.79203632617185e-07}
0.639 (+/-0.202) for {'C': 654636.1740672742, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.703 (+/-0.215) for {'C': 666806.7692136227, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.707 (+/-0.211) for {'C': 666806.7692136227, 'kernel': 'rbf', 'gamma': 5.861381645140294e-07}
0.710 (+/-0.209) for {'C': 666806.7692136227, 'kernel': 'rbf', 'gamma': 5.970352865838373e-07}
0.710 (+/-0.209) for {'C': 666806.7692136227, 'kernel': 'rbf', 'gamma': 6.081350012787182e-07}
0.715 (+/-0.210) for {'C': 666806.7692136227, 'kernel': 'rbf', 'gamma': 6.194410750767815e-07}
0.715 (+/-0.210) for {'C': 666806.7692136227, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.657 (+/-0.134) for {'C': 666806.7692136227, 'kernel': 'rbf', 'gamma': 6.426877173170207e-07}
0.643 (+/-0.167) for {'C': 666806.7692136227, 'kernel': 'rbf', 'gamma': 6.546361740672759e-07}
0.643 (+/-0.167) for {'C': 666806.7692136227, 'kernel': 'rbf', 'gamma': 6.668067692136228e-07}
0.635 (+/-0.198) for {'C': 666806.7692136227, 'kernel': 'rbf', 'gamma': 6.79203632617185e-07}
0.639 (+/-0.202) for {'C': 666806.7692136227, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.703 (+/-0.215) for {'C': 679203.6326171849, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.707 (+/-0.211) for {'C': 679203.6326171849, 'kernel': 'rbf', 'gamma': 5.861381645140294e-07}
0.710 (+/-0.209) for {'C': 679203.6326171849, 'kernel': 'rbf', 'gamma': 5.970352865838373e-07}
0.710 (+/-0.209) for {'C': 679203.6326171849, 'kernel': 'rbf', 'gamma': 6.081350012787182e-07}
0.715 (+/-0.210) for {'C': 679203.6326171849, 'kernel': 'rbf', 'gamma': 6.194410750767815e-07}
0.715 (+/-0.210) for {'C': 679203.6326171849, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.660 (+/-0.136) for {'C': 679203.6326171849, 'kernel': 'rbf', 'gamma': 6.426877173170207e-07}
0.643 (+/-0.167) for {'C': 679203.6326171849, 'kernel': 'rbf', 'gamma': 6.546361740672759e-07}
0.643 (+/-0.167) for {'C': 679203.6326171849, 'kernel': 'rbf', 'gamma': 6.668067692136228e-07}
0.635 (+/-0.198) for {'C': 679203.6326171849, 'kernel': 'rbf', 'gamma': 6.79203632617185e-07}
0.639 (+/-0.202) for {'C': 679203.6326171849, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.703 (+/-0.215) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.707 (+/-0.211) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 5.861381645140294e-07}
0.710 (+/-0.209) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 5.970352865838373e-07}
0.710 (+/-0.209) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 6.081350012787182e-07}
0.715 (+/-0.210) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 6.194410750767815e-07}
0.723 (+/-0.208) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.660 (+/-0.136) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 6.426877173170207e-07}
0.643 (+/-0.167) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 6.546361740672759e-07}
0.639 (+/-0.167) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 6.668067692136228e-07}
0.635 (+/-0.198) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 6.79203632617185e-07}
0.639 (+/-0.202) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.703 (+/-0.215) for {'C': 704693.0689671467, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.707 (+/-0.211) for {'C': 704693.0689671467, 'kernel': 'rbf', 'gamma': 5.861381645140294e-07}
0.707 (+/-0.211) for {'C': 704693.0689671467, 'kernel': 'rbf', 'gamma': 5.970352865838373e-07}
0.710 (+/-0.209) for {'C': 704693.0689671467, 'kernel': 'rbf', 'gamma': 6.081350012787182e-07}
0.715 (+/-0.210) for {'C': 704693.0689671467, 'kernel': 'rbf', 'gamma': 6.194410750767815e-07}
0.723 (+/-0.208) for {'C': 704693.0689671467, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.660 (+/-0.136) for {'C': 704693.0689671467, 'kernel': 'rbf', 'gamma': 6.426877173170207e-07}
0.643 (+/-0.167) for {'C': 704693.0689671467, 'kernel': 'rbf', 'gamma': 6.546361740672759e-07}
0.643 (+/-0.167) for {'C': 704693.0689671467, 'kernel': 'rbf', 'gamma': 6.668067692136228e-07}
0.635 (+/-0.198) for {'C': 704693.0689671467, 'kernel': 'rbf', 'gamma': 6.79203632617185e-07}
0.647 (+/-0.212) for {'C': 704693.0689671467, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.703 (+/-0.215) for {'C': 717794.2912713613, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.707 (+/-0.211) for {'C': 717794.2912713613, 'kernel': 'rbf', 'gamma': 5.861381645140294e-07}
0.707 (+/-0.211) for {'C': 717794.2912713613, 'kernel': 'rbf', 'gamma': 5.970352865838373e-07}
0.710 (+/-0.209) for {'C': 717794.2912713613, 'kernel': 'rbf', 'gamma': 6.081350012787182e-07}
0.715 (+/-0.210) for {'C': 717794.2912713613, 'kernel': 'rbf', 'gamma': 6.194410750767815e-07}
0.723 (+/-0.208) for {'C': 717794.2912713613, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.660 (+/-0.136) for {'C': 717794.2912713613, 'kernel': 'rbf', 'gamma': 6.426877173170207e-07}
0.643 (+/-0.167) for {'C': 717794.2912713613, 'kernel': 'rbf', 'gamma': 6.546361740672759e-07}
0.643 (+/-0.167) for {'C': 717794.2912713613, 'kernel': 'rbf', 'gamma': 6.668067692136228e-07}
0.635 (+/-0.198) for {'C': 717794.2912713613, 'kernel': 'rbf', 'gamma': 6.79203632617185e-07}
0.647 (+/-0.212) for {'C': 717794.2912713613, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.703 (+/-0.215) for {'C': 731139.0834834184, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.707 (+/-0.211) for {'C': 731139.0834834184, 'kernel': 'rbf', 'gamma': 5.861381645140294e-07}
0.710 (+/-0.209) for {'C': 731139.0834834184, 'kernel': 'rbf', 'gamma': 5.970352865838373e-07}
0.710 (+/-0.209) for {'C': 731139.0834834184, 'kernel': 'rbf', 'gamma': 6.081350012787182e-07}
0.715 (+/-0.210) for {'C': 731139.0834834184, 'kernel': 'rbf', 'gamma': 6.194410750767815e-07}
0.718 (+/-0.208) for {'C': 731139.0834834184, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.660 (+/-0.136) for {'C': 731139.0834834184, 'kernel': 'rbf', 'gamma': 6.426877173170207e-07}
0.643 (+/-0.167) for {'C': 731139.0834834184, 'kernel': 'rbf', 'gamma': 6.546361740672759e-07}
0.647 (+/-0.167) for {'C': 731139.0834834184, 'kernel': 'rbf', 'gamma': 6.668067692136228e-07}
0.635 (+/-0.198) for {'C': 731139.0834834184, 'kernel': 'rbf', 'gamma': 6.79203632617185e-07}
0.647 (+/-0.212) for {'C': 731139.0834834184, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.703 (+/-0.215) for {'C': 744731.9739059898, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.707 (+/-0.211) for {'C': 744731.9739059898, 'kernel': 'rbf', 'gamma': 5.861381645140294e-07}
0.710 (+/-0.209) for {'C': 744731.9739059898, 'kernel': 'rbf', 'gamma': 5.970352865838373e-07}
0.710 (+/-0.209) for {'C': 744731.9739059898, 'kernel': 'rbf', 'gamma': 6.081350012787182e-07}
0.715 (+/-0.210) for {'C': 744731.9739059898, 'kernel': 'rbf', 'gamma': 6.194410750767815e-07}
0.718 (+/-0.208) for {'C': 744731.9739059898, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.660 (+/-0.136) for {'C': 744731.9739059898, 'kernel': 'rbf', 'gamma': 6.426877173170207e-07}
0.643 (+/-0.167) for {'C': 744731.9739059898, 'kernel': 'rbf', 'gamma': 6.546361740672759e-07}
0.647 (+/-0.167) for {'C': 744731.9739059898, 'kernel': 'rbf', 'gamma': 6.668067692136228e-07}
0.630 (+/-0.189) for {'C': 744731.9739059898, 'kernel': 'rbf', 'gamma': 6.79203632617185e-07}
0.647 (+/-0.212) for {'C': 744731.9739059898, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.698 (+/-0.213) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.707 (+/-0.211) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 5.861381645140294e-07}
0.710 (+/-0.209) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 5.970352865838373e-07}
0.710 (+/-0.209) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 6.081350012787182e-07}
0.715 (+/-0.210) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 6.194410750767815e-07}
0.718 (+/-0.208) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.660 (+/-0.136) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 6.426877173170207e-07}
0.643 (+/-0.167) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 6.546361740672759e-07}
0.643 (+/-0.167) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 6.668067692136228e-07}
0.639 (+/-0.201) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 6.79203632617185e-07}
0.647 (+/-0.212) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.727 (+/-0.397) for {'C': 0.1, 'kernel': 'linear'}
0.625 (+/-0.179) for {'C': 1.0, 'kernel': 'linear'}
0.633 (+/-0.278) for {'C': 10.0, 'kernel': 'linear'}
0.598 (+/-0.284) for {'C': 100.0, 'kernel': 'linear'}
0.578 (+/-0.145) for {'C': 1000.0, 'kernel': 'linear'}
0.608 (+/-0.180) for {'C': 10000.0, 'kernel': 'linear'}
0.634 (+/-0.287) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.772 (+/-0.165) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.771 (+/-0.165) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 5.861381645140294e-07}
0.772 (+/-0.165) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 5.970352865838373e-07}
0.772 (+/-0.166) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.081350012787182e-07}
0.772 (+/-0.167) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.194410750767815e-07}
0.772 (+/-0.167) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.747 (+/-0.233) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.426877173170207e-07}
0.722 (+/-0.277) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.546361740672759e-07}
0.722 (+/-0.277) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.668067692136228e-07}
0.697 (+/-0.307) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.79203632617185e-07}
0.697 (+/-0.308) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.772 (+/-0.165) for {'C': 642687.7173170191, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.772 (+/-0.165) for {'C': 642687.7173170191, 'kernel': 'rbf', 'gamma': 5.861381645140294e-07}
0.772 (+/-0.165) for {'C': 642687.7173170191, 'kernel': 'rbf', 'gamma': 5.970352865838373e-07}
0.772 (+/-0.166) for {'C': 642687.7173170191, 'kernel': 'rbf', 'gamma': 6.081350012787182e-07}
0.772 (+/-0.167) for {'C': 642687.7173170191, 'kernel': 'rbf', 'gamma': 6.194410750767815e-07}
0.772 (+/-0.167) for {'C': 642687.7173170191, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.747 (+/-0.233) for {'C': 642687.7173170191, 'kernel': 'rbf', 'gamma': 6.426877173170207e-07}
0.722 (+/-0.277) for {'C': 642687.7173170191, 'kernel': 'rbf', 'gamma': 6.546361740672759e-07}
0.722 (+/-0.277) for {'C': 642687.7173170191, 'kernel': 'rbf', 'gamma': 6.668067692136228e-07}
0.697 (+/-0.307) for {'C': 642687.7173170191, 'kernel': 'rbf', 'gamma': 6.79203632617185e-07}
0.697 (+/-0.308) for {'C': 642687.7173170191, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.772 (+/-0.165) for {'C': 654636.1740672742, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.772 (+/-0.165) for {'C': 654636.1740672742, 'kernel': 'rbf', 'gamma': 5.861381645140294e-07}
0.772 (+/-0.165) for {'C': 654636.1740672742, 'kernel': 'rbf', 'gamma': 5.970352865838373e-07}
0.772 (+/-0.166) for {'C': 654636.1740672742, 'kernel': 'rbf', 'gamma': 6.081350012787182e-07}
0.772 (+/-0.167) for {'C': 654636.1740672742, 'kernel': 'rbf', 'gamma': 6.194410750767815e-07}
0.772 (+/-0.167) for {'C': 654636.1740672742, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.747 (+/-0.233) for {'C': 654636.1740672742, 'kernel': 'rbf', 'gamma': 6.426877173170207e-07}
0.722 (+/-0.277) for {'C': 654636.1740672742, 'kernel': 'rbf', 'gamma': 6.546361740672759e-07}
0.722 (+/-0.277) for {'C': 654636.1740672742, 'kernel': 'rbf', 'gamma': 6.668067692136228e-07}
0.697 (+/-0.307) for {'C': 654636.1740672742, 'kernel': 'rbf', 'gamma': 6.79203632617185e-07}
0.697 (+/-0.308) for {'C': 654636.1740672742, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.771 (+/-0.165) for {'C': 666806.7692136227, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.772 (+/-0.165) for {'C': 666806.7692136227, 'kernel': 'rbf', 'gamma': 5.861381645140294e-07}
0.772 (+/-0.166) for {'C': 666806.7692136227, 'kernel': 'rbf', 'gamma': 5.970352865838373e-07}
0.772 (+/-0.166) for {'C': 666806.7692136227, 'kernel': 'rbf', 'gamma': 6.081350012787182e-07}
0.772 (+/-0.167) for {'C': 666806.7692136227, 'kernel': 'rbf', 'gamma': 6.194410750767815e-07}
0.772 (+/-0.167) for {'C': 666806.7692136227, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.747 (+/-0.233) for {'C': 666806.7692136227, 'kernel': 'rbf', 'gamma': 6.426877173170207e-07}
0.722 (+/-0.277) for {'C': 666806.7692136227, 'kernel': 'rbf', 'gamma': 6.546361740672759e-07}
0.722 (+/-0.277) for {'C': 666806.7692136227, 'kernel': 'rbf', 'gamma': 6.668067692136228e-07}
0.697 (+/-0.307) for {'C': 666806.7692136227, 'kernel': 'rbf', 'gamma': 6.79203632617185e-07}
0.697 (+/-0.308) for {'C': 666806.7692136227, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.771 (+/-0.165) for {'C': 679203.6326171849, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.772 (+/-0.165) for {'C': 679203.6326171849, 'kernel': 'rbf', 'gamma': 5.861381645140294e-07}
0.772 (+/-0.166) for {'C': 679203.6326171849, 'kernel': 'rbf', 'gamma': 5.970352865838373e-07}
0.772 (+/-0.166) for {'C': 679203.6326171849, 'kernel': 'rbf', 'gamma': 6.081350012787182e-07}
0.772 (+/-0.167) for {'C': 679203.6326171849, 'kernel': 'rbf', 'gamma': 6.194410750767815e-07}
0.772 (+/-0.167) for {'C': 679203.6326171849, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.747 (+/-0.233) for {'C': 679203.6326171849, 'kernel': 'rbf', 'gamma': 6.426877173170207e-07}
0.722 (+/-0.277) for {'C': 679203.6326171849, 'kernel': 'rbf', 'gamma': 6.546361740672759e-07}
0.722 (+/-0.277) for {'C': 679203.6326171849, 'kernel': 'rbf', 'gamma': 6.668067692136228e-07}
0.697 (+/-0.307) for {'C': 679203.6326171849, 'kernel': 'rbf', 'gamma': 6.79203632617185e-07}
0.697 (+/-0.308) for {'C': 679203.6326171849, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.771 (+/-0.165) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.772 (+/-0.165) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 5.861381645140294e-07}
0.772 (+/-0.166) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 5.970352865838373e-07}
0.772 (+/-0.166) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 6.081350012787182e-07}
0.772 (+/-0.167) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 6.194410750767815e-07}
0.772 (+/-0.167) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.747 (+/-0.233) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 6.426877173170207e-07}
0.722 (+/-0.277) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 6.546361740672759e-07}
0.721 (+/-0.277) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 6.668067692136228e-07}
0.697 (+/-0.307) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 6.79203632617185e-07}
0.697 (+/-0.308) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.771 (+/-0.165) for {'C': 704693.0689671467, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.772 (+/-0.165) for {'C': 704693.0689671467, 'kernel': 'rbf', 'gamma': 5.861381645140294e-07}
0.772 (+/-0.165) for {'C': 704693.0689671467, 'kernel': 'rbf', 'gamma': 5.970352865838373e-07}
0.772 (+/-0.166) for {'C': 704693.0689671467, 'kernel': 'rbf', 'gamma': 6.081350012787182e-07}
0.772 (+/-0.167) for {'C': 704693.0689671467, 'kernel': 'rbf', 'gamma': 6.194410750767815e-07}
0.772 (+/-0.167) for {'C': 704693.0689671467, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.747 (+/-0.233) for {'C': 704693.0689671467, 'kernel': 'rbf', 'gamma': 6.426877173170207e-07}
0.722 (+/-0.277) for {'C': 704693.0689671467, 'kernel': 'rbf', 'gamma': 6.546361740672759e-07}
0.722 (+/-0.277) for {'C': 704693.0689671467, 'kernel': 'rbf', 'gamma': 6.668067692136228e-07}
0.697 (+/-0.307) for {'C': 704693.0689671467, 'kernel': 'rbf', 'gamma': 6.79203632617185e-07}
0.697 (+/-0.308) for {'C': 704693.0689671467, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.771 (+/-0.165) for {'C': 717794.2912713613, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.772 (+/-0.165) for {'C': 717794.2912713613, 'kernel': 'rbf', 'gamma': 5.861381645140294e-07}
0.772 (+/-0.165) for {'C': 717794.2912713613, 'kernel': 'rbf', 'gamma': 5.970352865838373e-07}
0.772 (+/-0.166) for {'C': 717794.2912713613, 'kernel': 'rbf', 'gamma': 6.081350012787182e-07}
0.772 (+/-0.167) for {'C': 717794.2912713613, 'kernel': 'rbf', 'gamma': 6.194410750767815e-07}
0.772 (+/-0.167) for {'C': 717794.2912713613, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.747 (+/-0.233) for {'C': 717794.2912713613, 'kernel': 'rbf', 'gamma': 6.426877173170207e-07}
0.722 (+/-0.277) for {'C': 717794.2912713613, 'kernel': 'rbf', 'gamma': 6.546361740672759e-07}
0.722 (+/-0.277) for {'C': 717794.2912713613, 'kernel': 'rbf', 'gamma': 6.668067692136228e-07}
0.697 (+/-0.307) for {'C': 717794.2912713613, 'kernel': 'rbf', 'gamma': 6.79203632617185e-07}
0.697 (+/-0.308) for {'C': 717794.2912713613, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.771 (+/-0.165) for {'C': 731139.0834834184, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.772 (+/-0.165) for {'C': 731139.0834834184, 'kernel': 'rbf', 'gamma': 5.861381645140294e-07}
0.772 (+/-0.166) for {'C': 731139.0834834184, 'kernel': 'rbf', 'gamma': 5.970352865838373e-07}
0.772 (+/-0.166) for {'C': 731139.0834834184, 'kernel': 'rbf', 'gamma': 6.081350012787182e-07}
0.772 (+/-0.167) for {'C': 731139.0834834184, 'kernel': 'rbf', 'gamma': 6.194410750767815e-07}
0.772 (+/-0.167) for {'C': 731139.0834834184, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.747 (+/-0.233) for {'C': 731139.0834834184, 'kernel': 'rbf', 'gamma': 6.426877173170207e-07}
0.722 (+/-0.277) for {'C': 731139.0834834184, 'kernel': 'rbf', 'gamma': 6.546361740672759e-07}
0.722 (+/-0.277) for {'C': 731139.0834834184, 'kernel': 'rbf', 'gamma': 6.668067692136228e-07}
0.697 (+/-0.307) for {'C': 731139.0834834184, 'kernel': 'rbf', 'gamma': 6.79203632617185e-07}
0.697 (+/-0.308) for {'C': 731139.0834834184, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.771 (+/-0.165) for {'C': 744731.9739059898, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.772 (+/-0.165) for {'C': 744731.9739059898, 'kernel': 'rbf', 'gamma': 5.861381645140294e-07}
0.772 (+/-0.166) for {'C': 744731.9739059898, 'kernel': 'rbf', 'gamma': 5.970352865838373e-07}
0.772 (+/-0.166) for {'C': 744731.9739059898, 'kernel': 'rbf', 'gamma': 6.081350012787182e-07}
0.772 (+/-0.167) for {'C': 744731.9739059898, 'kernel': 'rbf', 'gamma': 6.194410750767815e-07}
0.772 (+/-0.167) for {'C': 744731.9739059898, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.747 (+/-0.233) for {'C': 744731.9739059898, 'kernel': 'rbf', 'gamma': 6.426877173170207e-07}
0.722 (+/-0.277) for {'C': 744731.9739059898, 'kernel': 'rbf', 'gamma': 6.546361740672759e-07}
0.722 (+/-0.277) for {'C': 744731.9739059898, 'kernel': 'rbf', 'gamma': 6.668067692136228e-07}
0.697 (+/-0.307) for {'C': 744731.9739059898, 'kernel': 'rbf', 'gamma': 6.79203632617185e-07}
0.697 (+/-0.308) for {'C': 744731.9739059898, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.771 (+/-0.165) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.772 (+/-0.165) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 5.861381645140294e-07}
0.772 (+/-0.166) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 5.970352865838373e-07}
0.772 (+/-0.166) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 6.081350012787182e-07}
0.772 (+/-0.167) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 6.194410750767815e-07}
0.772 (+/-0.167) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.747 (+/-0.233) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 6.426877173170207e-07}
0.722 (+/-0.277) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 6.546361740672759e-07}
0.722 (+/-0.277) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 6.668067692136228e-07}
0.697 (+/-0.307) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 6.79203632617185e-07}
0.697 (+/-0.308) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.657 (+/-0.216) for {'C': 0.1, 'kernel': 'linear'}
0.697 (+/-0.308) for {'C': 1.0, 'kernel': 'linear'}
0.696 (+/-0.306) for {'C': 10.0, 'kernel': 'linear'}
0.691 (+/-0.298) for {'C': 100.0, 'kernel': 'linear'}
0.693 (+/-0.302) for {'C': 1000.0, 'kernel': 'linear'}
0.695 (+/-0.304) for {'C': 10000.0, 'kernel': 'linear'}
0.695 (+/-0.305) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.7721138387336136
第1轮loop中，tunemodel函数得到的最优参数是： ([{'C': array([679203.63261718, 681710.52630585, 684226.67276595, 686752.10614882,
       689286.86073184, 691830.97091894, 694384.47124098, 696947.39635632,
       699519.78105121, 702101.66024031, 704693.06896715]), 'kernel': ['rbf'], 'gamma': array([6.19441075e-07, 6.21727389e-07, 6.24022142e-07, 6.26325365e-07,
       6.28637088e-07, 6.30957344e-07, 6.33286164e-07, 6.35623580e-07,
       6.37969623e-07, 6.40324324e-07, 6.42687717e-07])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}], {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}, 0.7721138387336136)
这是第1次迭代微调C和gamma。
第1次迭代，得到delta： [0. 0.]
SVC模型clf_op_iter的参数是： {'kernel': 'rbf', 'decision_function_shape': 'ovr', 'class_weight': {-1: 15}, 'tol': 0.001, 'gamma': 6.309573444801931e-07, 'random_state': 0, 'cache_size': 200, 'verbose': False, 'degree': 3, 'C': 691830.9709189365, 'probability': False, 'shrinking': True, 'coef0': 0.0, 'max_iter': -1}

训练模型SVC对训练样本的预测准确率： 0.9142452161587526

测试集中，预测为舞弊样本的有： (array([ 112,  474,  475, 1230, 1247, 1248, 1252, 1255], dtype=int64),) 
测试集中，实际为舞弊样本的有： (array([1246, 1247, 1248, 1249, 1250, 1251, 1252, 1253, 1254, 1255, 1256],
      dtype=int64),) 
预测的舞弊样本数目： 8

训练模型SVC对测试样本的预测准确率： 0.9227498228206945
以上是第46次特征筛选。
第46次特征筛选，AUC值是： 0.6802130453815847
X_train_iter_svc.shape is: (1257, 6)
X_test_iter_svc.shape is: (1257, 6)
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.581 (+/-0.180) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.537 (+/-0.134) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.630 (+/-0.189) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.614 (+/-0.236) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.498 (+/-0.005) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.570 (+/-0.228) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.697 (+/-0.308) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.498 (+/-0.005) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6967933259131008
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.625 (+/-0.179) for {'C': 1.0, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.562 (+/-0.158) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.697 (+/-0.308) for {'C': 1.0, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.685 (+/-0.298) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.6966325542089208
RBF的粗grid search最佳超参： {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001} RBF的粗grid search最高分值： 0.6967933259131008
linear的粗grid search最佳超参： {'C': 1.0, 'kernel': 'linear'} linear的粗grid search最高分值： 0.6966325542089208
粗grid search得到的parameter是：
 [{'C': array([1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03, 1.e+04, 1.e+05,
       1.e+06, 1.e+07, 1.e+08]), 'kernel': ['rbf'], 'gamma': array([1.e-08, 1.e-07, 1.e-06, 1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01,
       1.e+00, 1.e+01, 1.e+02])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.689 (+/-0.436) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.639 (+/-0.326) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.581 (+/-0.180) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.639 (+/-0.326) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.630 (+/-0.189) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.585 (+/-0.186) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.505 (+/-0.051) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.326) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.630 (+/-0.189) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.652 (+/-0.284) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.538 (+/-0.171) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.639 (+/-0.326) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.630 (+/-0.189) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.650 (+/-0.281) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.592 (+/-0.299) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.537 (+/-0.134) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.639 (+/-0.326) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.630 (+/-0.189) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.650 (+/-0.281) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.669 (+/-0.295) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.539 (+/-0.135) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.546 (+/-0.171) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.630 (+/-0.189) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.655 (+/-0.280) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.644 (+/-0.282) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.564 (+/-0.177) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.530 (+/-0.117) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.537 (+/-0.135) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.666 (+/-0.275) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.630 (+/-0.189) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.650 (+/-0.281) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.637 (+/-0.288) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.605 (+/-0.196) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.528 (+/-0.108) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.538 (+/-0.113) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.521 (+/-0.106) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.722 (+/-0.473) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.589 (+/-0.194) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.633 (+/-0.281) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.615 (+/-0.293) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.568 (+/-0.155) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.558 (+/-0.124) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.502 (+/-0.035) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.528 (+/-0.108) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.521 (+/-0.106) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.721 (+/-0.328) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.572 (+/-0.190) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.629 (+/-0.279) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.609 (+/-0.296) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.621 (+/-0.380) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.563 (+/-0.159) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.507 (+/-0.061) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.528 (+/-0.108) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.521 (+/-0.106) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.630 (+/-0.189) for {'C': 1.0, 'kernel': 'linear'}
0.650 (+/-0.281) for {'C': 10.0, 'kernel': 'linear'}
0.648 (+/-0.282) for {'C': 100.0, 'kernel': 'linear'}
0.619 (+/-0.177) for {'C': 1000.0, 'kernel': 'linear'}
0.598 (+/-0.159) for {'C': 10000.0, 'kernel': 'linear'}
0.585 (+/-0.147) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.003) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.002) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.236) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.002) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.002) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.308) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.236) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.522 (+/-0.147) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.012) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.308) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.216) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.536 (+/-0.174) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.008) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.497 (+/-0.006) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.615 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.308) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.308) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.232) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.570 (+/-0.228) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.493 (+/-0.012) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.497 (+/-0.005) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.308) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.308) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.673 (+/-0.240) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.553 (+/-0.185) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.571 (+/-0.229) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.495 (+/-0.008) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.497 (+/-0.005) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.697 (+/-0.308) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.308) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.696 (+/-0.307) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.611 (+/-0.229) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.562 (+/-0.203) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.569 (+/-0.231) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.495 (+/-0.008) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.497 (+/-0.005) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.680 (+/-0.193) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.697 (+/-0.308) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.697 (+/-0.308) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.696 (+/-0.303) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.693 (+/-0.303) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.551 (+/-0.184) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.587 (+/-0.224) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.543 (+/-0.200) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.495 (+/-0.008) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.497 (+/-0.005) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.616 (+/-0.239) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.662 (+/-0.228) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.696 (+/-0.303) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.694 (+/-0.299) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.689 (+/-0.292) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.687 (+/-0.293) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.519 (+/-0.145) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.558 (+/-0.209) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.543 (+/-0.200) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.495 (+/-0.008) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.497 (+/-0.005) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.706 (+/-0.269) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.633 (+/-0.241) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.696 (+/-0.301) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.692 (+/-0.297) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.687 (+/-0.289) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.684 (+/-0.286) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.519 (+/-0.150) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.556 (+/-0.212) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.543 (+/-0.200) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.495 (+/-0.008) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.497 (+/-0.005) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.616 (+/-0.239) for {'C': 0.1, 'kernel': 'linear'}
0.697 (+/-0.308) for {'C': 1.0, 'kernel': 'linear'}
0.697 (+/-0.308) for {'C': 10.0, 'kernel': 'linear'}
0.697 (+/-0.307) for {'C': 100.0, 'kernel': 'linear'}
0.696 (+/-0.307) for {'C': 1000.0, 'kernel': 'linear'}
0.695 (+/-0.307) for {'C': 10000.0, 'kernel': 'linear'}
0.694 (+/-0.307) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.7059284565916398
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.606 (+/-0.146) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.513 (+/-0.101) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.625 (+/-0.179) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.631 (+/-0.197) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.625 (+/-0.179) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.621 (+/-0.177) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.573 (+/-0.200) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.505 (+/-0.051) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.625 (+/-0.179) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.625 (+/-0.179) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.292) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.538 (+/-0.171) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.625 (+/-0.179) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.625 (+/-0.179) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.623 (+/-0.288) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.586 (+/-0.294) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.530 (+/-0.113) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.625 (+/-0.179) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.621 (+/-0.177) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.619 (+/-0.286) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.604 (+/-0.306) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.539 (+/-0.135) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.522 (+/-0.105) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.625 (+/-0.179) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.621 (+/-0.177) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.615 (+/-0.285) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.594 (+/-0.295) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.559 (+/-0.163) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.523 (+/-0.102) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.526 (+/-0.106) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.600 (+/-0.112) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.612 (+/-0.164) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.604 (+/-0.299) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.588 (+/-0.307) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.564 (+/-0.187) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.519 (+/-0.068) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.530 (+/-0.107) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.521 (+/-0.106) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.689 (+/-0.299) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.529 (+/-0.146) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.598 (+/-0.281) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.590 (+/-0.307) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.555 (+/-0.160) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.536 (+/-0.095) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.502 (+/-0.035) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.528 (+/-0.108) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.521 (+/-0.106) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.666 (+/-0.295) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.526 (+/-0.147) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.592 (+/-0.284) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.587 (+/-0.308) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.588 (+/-0.308) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.536 (+/-0.096) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.507 (+/-0.061) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.528 (+/-0.108) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.521 (+/-0.106) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.727 (+/-0.397) for {'C': 0.1, 'kernel': 'linear'}
0.625 (+/-0.179) for {'C': 1.0, 'kernel': 'linear'}
0.632 (+/-0.282) for {'C': 10.0, 'kernel': 'linear'}
0.597 (+/-0.290) for {'C': 100.0, 'kernel': 'linear'}
0.562 (+/-0.158) for {'C': 1000.0, 'kernel': 'linear'}
0.558 (+/-0.134) for {'C': 10000.0, 'kernel': 'linear'}
0.544 (+/-0.100) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.655 (+/-0.215) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.523 (+/-0.149) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.498 (+/-0.003) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.307) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.655 (+/-0.216) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.495 (+/-0.009) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.497 (+/-0.010) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.307) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.696 (+/-0.308) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.612 (+/-0.231) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.522 (+/-0.147) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.012) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.307) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.308) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.670 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.535 (+/-0.175) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.488 (+/-0.024) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.497 (+/-0.006) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.307) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.308) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.694 (+/-0.306) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.641 (+/-0.244) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.570 (+/-0.227) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.493 (+/-0.012) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.497 (+/-0.005) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.697 (+/-0.307) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.696 (+/-0.308) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.694 (+/-0.305) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.662 (+/-0.226) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.551 (+/-0.187) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.560 (+/-0.225) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.495 (+/-0.008) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.497 (+/-0.005) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.697 (+/-0.307) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.696 (+/-0.308) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.693 (+/-0.304) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.685 (+/-0.299) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.243) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.561 (+/-0.200) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.566 (+/-0.229) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.495 (+/-0.008) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.497 (+/-0.005) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.760 (+/-0.153) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.696 (+/-0.306) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.690 (+/-0.296) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.682 (+/-0.282) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.682 (+/-0.281) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.550 (+/-0.183) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.575 (+/-0.224) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.543 (+/-0.200) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.495 (+/-0.008) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.497 (+/-0.005) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.657 (+/-0.216) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.574 (+/-0.167) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.692 (+/-0.291) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.681 (+/-0.279) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.679 (+/-0.276) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.677 (+/-0.284) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.508 (+/-0.155) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.547 (+/-0.226) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.543 (+/-0.200) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.495 (+/-0.008) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.497 (+/-0.005) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.681 (+/-0.293) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.525 (+/-0.114) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.688 (+/-0.284) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.679 (+/-0.275) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.679 (+/-0.277) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.675 (+/-0.289) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.506 (+/-0.162) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.548 (+/-0.224) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.543 (+/-0.200) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.495 (+/-0.008) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.497 (+/-0.005) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.657 (+/-0.216) for {'C': 0.1, 'kernel': 'linear'}
0.697 (+/-0.308) for {'C': 1.0, 'kernel': 'linear'}
0.696 (+/-0.306) for {'C': 10.0, 'kernel': 'linear'}
0.688 (+/-0.301) for {'C': 100.0, 'kernel': 'linear'}
0.685 (+/-0.298) for {'C': 1000.0, 'kernel': 'linear'}
0.685 (+/-0.298) for {'C': 10000.0, 'kernel': 'linear'}
0.684 (+/-0.298) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.7602533184928684
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.496 (+/-0.001) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.496 (+/-0.001) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.496 (+/-0.001) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.496 (+/-0.001) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.496 (+/-0.001) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.496 (+/-0.001) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.496 (+/-0.001) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.547 (+/-0.301) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.697 (+/-0.437) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.639 (+/-0.326) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.496 (+/-0.001) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.496 (+/-0.001) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.496 (+/-0.001) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.496 (+/-0.001) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.496 (+/-0.001) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.496 (+/-0.001) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.496 (+/-0.001) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.547 (+/-0.301) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.689 (+/-0.436) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.639 (+/-0.326) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.660 (+/-0.290) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.496 (+/-0.001) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.496 (+/-0.001) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.496 (+/-0.001) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.496 (+/-0.001) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.496 (+/-0.001) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.496 (+/-0.001) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.597 (+/-0.402) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.689 (+/-0.436) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.639 (+/-0.326) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.643 (+/-0.209) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.618 (+/-0.172) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.496 (+/-0.001) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.496 (+/-0.001) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.496 (+/-0.001) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.496 (+/-0.001) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.496 (+/-0.001) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.597 (+/-0.402) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.689 (+/-0.436) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.639 (+/-0.326) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.627 (+/-0.185) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.625 (+/-0.179) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.630 (+/-0.189) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.496 (+/-0.001) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.496 (+/-0.001) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.496 (+/-0.001) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.496 (+/-0.001) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.747 (+/-0.502) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.702 (+/-0.420) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.689 (+/-0.374) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.677 (+/-0.299) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.639 (+/-0.167) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.660 (+/-0.136) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.630 (+/-0.189) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.656 (+/-0.312) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.666 (+/-0.275) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.685 (+/-0.352) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.635 (+/-0.153) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.644 (+/-0.120) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.669 (+/-0.142) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.630 (+/-0.189) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.547 (+/-0.301) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.604 (+/-0.171) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.672 (+/-0.287) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.630 (+/-0.189) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.714 (+/-0.213) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.614 (+/-0.167) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.686 (+/-0.156) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.621 (+/-0.177) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.647 (+/-0.460) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.747 (+/-0.502) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.544 (+/-0.101) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.648 (+/-0.292) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.600 (+/-0.138) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.712 (+/-0.218) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.635 (+/-0.190) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.686 (+/-0.156) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.626 (+/-0.188) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.496 (+/-0.001) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.647 (+/-0.460) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.747 (+/-0.502) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.702 (+/-0.429) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.511 (+/-0.031) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.623 (+/-0.295) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.624 (+/-0.166) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.720 (+/-0.217) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.631 (+/-0.186) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.677 (+/-0.150) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.624 (+/-0.186) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.647 (+/-0.460) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.722 (+/-0.473) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.739 (+/-0.451) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.691 (+/-0.370) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.503 (+/-0.010) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.594 (+/-0.196) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.619 (+/-0.169) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.713 (+/-0.227) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.659 (+/-0.285) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.702 (+/-0.243) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.636 (+/-0.280) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.722 (+/-0.473) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.714 (+/-0.418) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.689 (+/-0.299) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.732 (+/-0.391) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.453 (+/-0.300) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.589 (+/-0.194) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.609 (+/-0.185) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.707 (+/-0.234) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.659 (+/-0.285) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.702 (+/-0.243) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.635 (+/-0.280) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.630 (+/-0.189) for {'C': 1.0, 'kernel': 'linear'}
0.650 (+/-0.281) for {'C': 10.0, 'kernel': 'linear'}
0.648 (+/-0.282) for {'C': 100.0, 'kernel': 'linear'}
0.619 (+/-0.177) for {'C': 1000.0, 'kernel': 'linear'}
0.598 (+/-0.159) for {'C': 10000.0, 'kernel': 'linear'}
0.585 (+/-0.147) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.500 (+/-0.000) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.500 (+/-0.000) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.500 (+/-0.000) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.500 (+/-0.000) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.500 (+/-0.000) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.500 (+/-0.000) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.500 (+/-0.000) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.525 (+/-0.150) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.616 (+/-0.238) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.615 (+/-0.237) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.500 (+/-0.000) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.500 (+/-0.000) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.500 (+/-0.000) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.500 (+/-0.000) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.500 (+/-0.000) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.500 (+/-0.000) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.500 (+/-0.000) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.525 (+/-0.150) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.616 (+/-0.238) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.615 (+/-0.237) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.656 (+/-0.215) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.500 (+/-0.000) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.500 (+/-0.000) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.500 (+/-0.000) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.500 (+/-0.000) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.500 (+/-0.000) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.500 (+/-0.000) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.550 (+/-0.200) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.616 (+/-0.238) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.615 (+/-0.237) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.656 (+/-0.216) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.680 (+/-0.294) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.500 (+/-0.000) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.500 (+/-0.000) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.500 (+/-0.000) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.500 (+/-0.000) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.500 (+/-0.000) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.550 (+/-0.200) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.616 (+/-0.238) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.615 (+/-0.237) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.656 (+/-0.215) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.697 (+/-0.307) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.697 (+/-0.308) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.500 (+/-0.000) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.500 (+/-0.000) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.500 (+/-0.000) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.500 (+/-0.000) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.617 (+/-0.238) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.641 (+/-0.235) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.640 (+/-0.236) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.656 (+/-0.216) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.705 (+/-0.270) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.747 (+/-0.234) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.697 (+/-0.308) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.500 (+/-0.002) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.640 (+/-0.235) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.680 (+/-0.193) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.656 (+/-0.215) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.697 (+/-0.211) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.730 (+/-0.230) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.747 (+/-0.234) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.697 (+/-0.308) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.525 (+/-0.150) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.677 (+/-0.283) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.721 (+/-0.278) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.681 (+/-0.294) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.771 (+/-0.163) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.680 (+/-0.293) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.747 (+/-0.234) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.696 (+/-0.308) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.575 (+/-0.229) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.616 (+/-0.239) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.658 (+/-0.229) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.719 (+/-0.276) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.679 (+/-0.293) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.772 (+/-0.166) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.697 (+/-0.307) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.747 (+/-0.234) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.697 (+/-0.307) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.500 (+/-0.000) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.575 (+/-0.229) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.616 (+/-0.239) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.659 (+/-0.215) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.597 (+/-0.244) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.694 (+/-0.303) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.720 (+/-0.274) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.772 (+/-0.166) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.681 (+/-0.294) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.722 (+/-0.166) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.697 (+/-0.306) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.575 (+/-0.229) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.616 (+/-0.239) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.641 (+/-0.237) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.685 (+/-0.198) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.571 (+/-0.210) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.678 (+/-0.251) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.720 (+/-0.272) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.771 (+/-0.166) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.681 (+/-0.295) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.747 (+/-0.234) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.696 (+/-0.305) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.616 (+/-0.239) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.640 (+/-0.237) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.657 (+/-0.216) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.666 (+/-0.193) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.539 (+/-0.178) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.662 (+/-0.228) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.694 (+/-0.301) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.771 (+/-0.161) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.681 (+/-0.295) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.747 (+/-0.234) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.696 (+/-0.303) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.616 (+/-0.239) for {'C': 0.1, 'kernel': 'linear'}
0.697 (+/-0.308) for {'C': 1.0, 'kernel': 'linear'}
0.697 (+/-0.308) for {'C': 10.0, 'kernel': 'linear'}
0.697 (+/-0.307) for {'C': 100.0, 'kernel': 'linear'}
0.696 (+/-0.307) for {'C': 1000.0, 'kernel': 'linear'}
0.695 (+/-0.307) for {'C': 10000.0, 'kernel': 'linear'}
0.694 (+/-0.307) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.7717933259131009
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.496 (+/-0.001) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.496 (+/-0.001) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.496 (+/-0.001) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.496 (+/-0.001) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.496 (+/-0.001) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.496 (+/-0.001) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.597 (+/-0.402) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.697 (+/-0.437) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.639 (+/-0.326) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.627 (+/-0.185) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.625 (+/-0.179) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.496 (+/-0.001) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.496 (+/-0.001) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.496 (+/-0.001) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.496 (+/-0.001) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.496 (+/-0.001) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.647 (+/-0.460) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.664 (+/-0.388) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.639 (+/-0.326) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.627 (+/-0.185) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.618 (+/-0.172) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.652 (+/-0.123) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.496 (+/-0.001) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.496 (+/-0.001) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.496 (+/-0.001) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.496 (+/-0.001) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.496 (+/-0.001) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.668 (+/-0.295) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.643 (+/-0.306) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.660 (+/-0.290) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.637 (+/-0.122) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.647 (+/-0.124) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.660 (+/-0.136) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.496 (+/-0.001) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.496 (+/-0.001) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.496 (+/-0.001) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.496 (+/-0.001) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.764 (+/-0.421) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.600 (+/-0.167) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.627 (+/-0.291) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.620 (+/-0.172) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.706 (+/-0.214) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.715 (+/-0.210) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.630 (+/-0.189) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.496 (+/-0.001) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.496 (+/-0.001) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.496 (+/-0.001) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.496 (+/-0.001) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.631 (+/-0.196) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.564 (+/-0.085) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.603 (+/-0.155) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.699 (+/-0.211) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.686 (+/-0.248) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.715 (+/-0.210) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.621 (+/-0.177) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.747 (+/-0.502) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.548 (+/-0.097) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.600 (+/-0.112) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.594 (+/-0.157) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.697 (+/-0.215) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.623 (+/-0.184) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.673 (+/-0.153) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.612 (+/-0.164) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.747 (+/-0.502) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.697 (+/-0.437) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.527 (+/-0.071) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.613 (+/-0.206) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.581 (+/-0.166) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.698 (+/-0.224) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.630 (+/-0.189) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.668 (+/-0.146) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.609 (+/-0.179) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.747 (+/-0.502) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.697 (+/-0.437) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.664 (+/-0.388) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.520 (+/-0.060) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.590 (+/-0.178) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.567 (+/-0.126) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.684 (+/-0.232) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.630 (+/-0.189) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.656 (+/-0.138) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.594 (+/-0.159) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.747 (+/-0.502) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.697 (+/-0.437) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.714 (+/-0.352) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.667 (+/-0.311) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.560 (+/-0.294) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.556 (+/-0.149) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.575 (+/-0.159) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.639 (+/-0.136) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.626 (+/-0.176) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.656 (+/-0.138) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.587 (+/-0.154) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.697 (+/-0.437) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.689 (+/-0.299) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.681 (+/-0.297) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.725 (+/-0.321) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.502 (+/-0.445) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.534 (+/-0.146) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.573 (+/-0.170) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.634 (+/-0.142) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.626 (+/-0.176) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.671 (+/-0.249) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.604 (+/-0.280) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.689 (+/-0.299) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.673 (+/-0.293) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.673 (+/-0.293) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.720 (+/-0.326) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.499 (+/-0.446) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.529 (+/-0.146) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.567 (+/-0.156) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.632 (+/-0.145) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.626 (+/-0.176) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.667 (+/-0.253) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.597 (+/-0.281) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.727 (+/-0.397) for {'C': 0.1, 'kernel': 'linear'}
0.625 (+/-0.179) for {'C': 1.0, 'kernel': 'linear'}
0.632 (+/-0.282) for {'C': 10.0, 'kernel': 'linear'}
0.597 (+/-0.290) for {'C': 100.0, 'kernel': 'linear'}
0.562 (+/-0.158) for {'C': 1000.0, 'kernel': 'linear'}
0.558 (+/-0.134) for {'C': 10000.0, 'kernel': 'linear'}
0.544 (+/-0.100) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.500 (+/-0.000) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.500 (+/-0.000) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.500 (+/-0.000) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.500 (+/-0.000) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.500 (+/-0.000) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.500 (+/-0.000) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.550 (+/-0.200) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.616 (+/-0.238) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.615 (+/-0.237) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.656 (+/-0.215) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.697 (+/-0.307) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.500 (+/-0.000) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.500 (+/-0.000) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.500 (+/-0.000) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.500 (+/-0.000) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.500 (+/-0.000) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.575 (+/-0.230) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.616 (+/-0.238) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.615 (+/-0.237) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.656 (+/-0.215) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.672 (+/-0.239) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.746 (+/-0.233) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.500 (+/-0.000) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.500 (+/-0.000) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.500 (+/-0.000) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.500 (+/-0.000) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.500 (+/-0.000) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.665 (+/-0.221) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.640 (+/-0.235) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.656 (+/-0.215) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.705 (+/-0.151) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.722 (+/-0.165) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.747 (+/-0.233) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.500 (+/-0.000) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.500 (+/-0.000) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.500 (+/-0.000) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.500 (+/-0.002) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.657 (+/-0.217) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.678 (+/-0.190) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.679 (+/-0.291) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.697 (+/-0.306) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.772 (+/-0.166) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.772 (+/-0.167) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.697 (+/-0.308) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.500 (+/-0.000) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.500 (+/-0.000) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.500 (+/-0.000) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.500 (+/-0.002) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.697 (+/-0.305) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.675 (+/-0.188) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.701 (+/-0.255) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.771 (+/-0.164) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.747 (+/-0.233) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.772 (+/-0.167) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.696 (+/-0.308) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.617 (+/-0.238) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.679 (+/-0.275) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.760 (+/-0.153) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.712 (+/-0.251) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.771 (+/-0.164) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.697 (+/-0.306) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.747 (+/-0.234) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.696 (+/-0.306) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.617 (+/-0.238) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.616 (+/-0.237) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.612 (+/-0.269) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.725 (+/-0.243) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.677 (+/-0.252) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.771 (+/-0.163) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.697 (+/-0.308) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.747 (+/-0.234) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.696 (+/-0.303) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.617 (+/-0.238) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.616 (+/-0.238) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.616 (+/-0.238) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.581 (+/-0.185) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.682 (+/-0.302) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.659 (+/-0.234) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.770 (+/-0.160) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.697 (+/-0.308) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.747 (+/-0.234) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.695 (+/-0.300) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.617 (+/-0.238) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.616 (+/-0.238) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.657 (+/-0.217) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.670 (+/-0.183) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.572 (+/-0.193) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.669 (+/-0.307) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.649 (+/-0.244) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.768 (+/-0.160) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.697 (+/-0.307) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.747 (+/-0.234) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.694 (+/-0.299) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.616 (+/-0.238) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.657 (+/-0.216) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.657 (+/-0.216) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.726 (+/-0.074) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.545 (+/-0.214) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.608 (+/-0.222) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.645 (+/-0.249) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.768 (+/-0.161) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.697 (+/-0.307) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.746 (+/-0.233) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.693 (+/-0.295) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.657 (+/-0.216) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.656 (+/-0.216) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.656 (+/-0.216) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.697 (+/-0.143) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.514 (+/-0.274) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.574 (+/-0.167) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.644 (+/-0.248) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.767 (+/-0.159) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.697 (+/-0.307) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.746 (+/-0.233) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.691 (+/-0.290) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.657 (+/-0.216) for {'C': 0.1, 'kernel': 'linear'}
0.697 (+/-0.308) for {'C': 1.0, 'kernel': 'linear'}
0.696 (+/-0.306) for {'C': 10.0, 'kernel': 'linear'}
0.688 (+/-0.301) for {'C': 100.0, 'kernel': 'linear'}
0.685 (+/-0.298) for {'C': 1000.0, 'kernel': 'linear'}
0.685 (+/-0.298) for {'C': 10000.0, 'kernel': 'linear'}
0.684 (+/-0.298) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.7719535823233572
循环迭代之前，delta is: [6.01892829e+05 5.30957344e-07]
执行tunemodel()函数前，使用的grid search parameter是：
 [{'C': array([251188.64315096, 275422.87033382, 301995.1720402 , 331131.12148259,
       363078.0547701 , 398107.1705535 , 436515.83224017, 478630.09232264,
       524807.46024977, 575439.93733716, 630957.34448019]), 'kernel': ['rbf'], 'gamma': array([3.98107171e-07, 4.36515832e-07, 4.78630092e-07, 5.24807460e-07,
       5.75439937e-07, 6.30957344e-07, 6.91830971e-07, 7.58577575e-07,
       8.31763771e-07, 9.12010839e-07, 1.00000000e-06])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.639 (+/-0.326) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.739 (+/-0.391) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.727 (+/-0.397) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.702 (+/-0.355) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.702 (+/-0.355) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.643 (+/-0.209) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.618 (+/-0.169) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.614 (+/-0.166) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.623 (+/-0.184) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.618 (+/-0.172) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.618 (+/-0.172) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.681 (+/-0.372) for {'C': 275422.87033381715, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.731 (+/-0.393) for {'C': 275422.87033381715, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.702 (+/-0.355) for {'C': 275422.87033381715, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.660 (+/-0.290) for {'C': 275422.87033381715, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.627 (+/-0.185) for {'C': 275422.87033381715, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.635 (+/-0.198) for {'C': 275422.87033381715, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.614 (+/-0.166) for {'C': 275422.87033381715, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.618 (+/-0.172) for {'C': 275422.87033381715, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.618 (+/-0.172) for {'C': 275422.87033381715, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.618 (+/-0.172) for {'C': 275422.87033381715, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.614 (+/-0.167) for {'C': 275422.87033381715, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.727 (+/-0.397) for {'C': 301995.17204020155, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.702 (+/-0.355) for {'C': 301995.17204020155, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.668 (+/-0.295) for {'C': 301995.17204020155, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.635 (+/-0.198) for {'C': 301995.17204020155, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.618 (+/-0.169) for {'C': 301995.17204020155, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.623 (+/-0.183) for {'C': 301995.17204020155, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.618 (+/-0.172) for {'C': 301995.17204020155, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.625 (+/-0.179) for {'C': 301995.17204020155, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.618 (+/-0.172) for {'C': 301995.17204020155, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.618 (+/-0.172) for {'C': 301995.17204020155, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.622 (+/-0.174) for {'C': 301995.17204020155, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.693 (+/-0.354) for {'C': 331131.12148259126, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.693 (+/-0.354) for {'C': 331131.12148259126, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.627 (+/-0.185) for {'C': 331131.12148259126, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.627 (+/-0.185) for {'C': 331131.12148259126, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.614 (+/-0.166) for {'C': 331131.12148259126, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.618 (+/-0.172) for {'C': 331131.12148259126, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.618 (+/-0.172) for {'C': 331131.12148259126, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.622 (+/-0.174) for {'C': 331131.12148259126, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.618 (+/-0.172) for {'C': 331131.12148259126, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.614 (+/-0.167) for {'C': 331131.12148259126, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.622 (+/-0.174) for {'C': 331131.12148259126, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.652 (+/-0.284) for {'C': 363078.0547701017, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.627 (+/-0.185) for {'C': 363078.0547701017, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.618 (+/-0.169) for {'C': 363078.0547701017, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.623 (+/-0.183) for {'C': 363078.0547701017, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.610 (+/-0.163) for {'C': 363078.0547701017, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.625 (+/-0.179) for {'C': 363078.0547701017, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.618 (+/-0.172) for {'C': 363078.0547701017, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.622 (+/-0.174) for {'C': 363078.0547701017, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.625 (+/-0.179) for {'C': 363078.0547701017, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.627 (+/-0.147) for {'C': 363078.0547701017, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.622 (+/-0.174) for {'C': 363078.0547701017, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.627 (+/-0.185) for {'C': 398107.17055349785, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.618 (+/-0.169) for {'C': 398107.17055349785, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.614 (+/-0.166) for {'C': 398107.17055349785, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.618 (+/-0.172) for {'C': 398107.17055349785, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.618 (+/-0.172) for {'C': 398107.17055349785, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.625 (+/-0.179) for {'C': 398107.17055349785, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.630 (+/-0.152) for {'C': 398107.17055349785, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.622 (+/-0.174) for {'C': 398107.17055349785, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.625 (+/-0.179) for {'C': 398107.17055349785, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.627 (+/-0.147) for {'C': 398107.17055349785, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.630 (+/-0.189) for {'C': 398107.17055349785, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.618 (+/-0.169) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.610 (+/-0.163) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.610 (+/-0.163) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.625 (+/-0.179) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.625 (+/-0.179) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.625 (+/-0.179) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.630 (+/-0.152) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.622 (+/-0.174) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.625 (+/-0.179) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.627 (+/-0.147) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.647 (+/-0.167) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.622 (+/-0.174) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.610 (+/-0.163) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.618 (+/-0.172) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.625 (+/-0.179) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.625 (+/-0.179) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.638 (+/-0.157) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.647 (+/-0.124) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.622 (+/-0.174) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.630 (+/-0.189) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.641 (+/-0.122) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.647 (+/-0.167) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.626 (+/-0.188) for {'C': 524807.460249773, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.625 (+/-0.179) for {'C': 524807.460249773, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.625 (+/-0.179) for {'C': 524807.460249773, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.625 (+/-0.179) for {'C': 524807.460249773, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.630 (+/-0.189) for {'C': 524807.460249773, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.655 (+/-0.126) for {'C': 524807.460249773, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.655 (+/-0.126) for {'C': 524807.460249773, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.622 (+/-0.174) for {'C': 524807.460249773, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.630 (+/-0.189) for {'C': 524807.460249773, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.636 (+/-0.159) for {'C': 524807.460249773, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.630 (+/-0.189) for {'C': 524807.460249773, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.643 (+/-0.168) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.642 (+/-0.158) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.625 (+/-0.179) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.625 (+/-0.179) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.643 (+/-0.167) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.655 (+/-0.126) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.642 (+/-0.158) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.622 (+/-0.174) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.630 (+/-0.189) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.636 (+/-0.169) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.630 (+/-0.189) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.639 (+/-0.167) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.655 (+/-0.126) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.630 (+/-0.189) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.663 (+/-0.138) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.643 (+/-0.167) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.660 (+/-0.136) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.642 (+/-0.158) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.642 (+/-0.158) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.630 (+/-0.189) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.636 (+/-0.169) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.630 (+/-0.189) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.630 (+/-0.189) for {'C': 1.0, 'kernel': 'linear'}
0.650 (+/-0.281) for {'C': 10.0, 'kernel': 'linear'}
0.648 (+/-0.282) for {'C': 100.0, 'kernel': 'linear'}
0.619 (+/-0.177) for {'C': 1000.0, 'kernel': 'linear'}
0.598 (+/-0.159) for {'C': 10000.0, 'kernel': 'linear'}
0.585 (+/-0.147) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.615 (+/-0.237) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.657 (+/-0.217) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.657 (+/-0.216) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.657 (+/-0.216) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.657 (+/-0.216) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.656 (+/-0.216) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.656 (+/-0.215) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.655 (+/-0.215) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.680 (+/-0.295) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.680 (+/-0.294) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.680 (+/-0.294) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.640 (+/-0.236) for {'C': 275422.87033381715, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.657 (+/-0.216) for {'C': 275422.87033381715, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.657 (+/-0.216) for {'C': 275422.87033381715, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.656 (+/-0.215) for {'C': 275422.87033381715, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.656 (+/-0.215) for {'C': 275422.87033381715, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.656 (+/-0.215) for {'C': 275422.87033381715, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.655 (+/-0.215) for {'C': 275422.87033381715, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.680 (+/-0.294) for {'C': 275422.87033381715, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.680 (+/-0.294) for {'C': 275422.87033381715, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.680 (+/-0.294) for {'C': 275422.87033381715, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.680 (+/-0.294) for {'C': 275422.87033381715, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.657 (+/-0.216) for {'C': 301995.17204020155, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.657 (+/-0.216) for {'C': 301995.17204020155, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.656 (+/-0.216) for {'C': 301995.17204020155, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.656 (+/-0.215) for {'C': 301995.17204020155, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.656 (+/-0.215) for {'C': 301995.17204020155, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.656 (+/-0.215) for {'C': 301995.17204020155, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.680 (+/-0.294) for {'C': 301995.17204020155, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.697 (+/-0.307) for {'C': 301995.17204020155, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.680 (+/-0.294) for {'C': 301995.17204020155, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.680 (+/-0.295) for {'C': 301995.17204020155, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.697 (+/-0.307) for {'C': 301995.17204020155, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.656 (+/-0.216) for {'C': 331131.12148259126, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.656 (+/-0.216) for {'C': 331131.12148259126, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.656 (+/-0.215) for {'C': 331131.12148259126, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.656 (+/-0.215) for {'C': 331131.12148259126, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.655 (+/-0.215) for {'C': 331131.12148259126, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.680 (+/-0.294) for {'C': 331131.12148259126, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.680 (+/-0.294) for {'C': 331131.12148259126, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.697 (+/-0.306) for {'C': 331131.12148259126, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.680 (+/-0.294) for {'C': 331131.12148259126, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.680 (+/-0.294) for {'C': 331131.12148259126, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.696 (+/-0.307) for {'C': 331131.12148259126, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.656 (+/-0.215) for {'C': 363078.0547701017, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.656 (+/-0.215) for {'C': 363078.0547701017, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.656 (+/-0.215) for {'C': 363078.0547701017, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.656 (+/-0.215) for {'C': 363078.0547701017, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.655 (+/-0.215) for {'C': 363078.0547701017, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.697 (+/-0.307) for {'C': 363078.0547701017, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.680 (+/-0.295) for {'C': 363078.0547701017, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.697 (+/-0.306) for {'C': 363078.0547701017, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.697 (+/-0.307) for {'C': 363078.0547701017, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.705 (+/-0.269) for {'C': 363078.0547701017, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.696 (+/-0.307) for {'C': 363078.0547701017, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.656 (+/-0.215) for {'C': 398107.17055349785, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.656 (+/-0.215) for {'C': 398107.17055349785, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.655 (+/-0.215) for {'C': 398107.17055349785, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.680 (+/-0.294) for {'C': 398107.17055349785, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.680 (+/-0.294) for {'C': 398107.17055349785, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.697 (+/-0.307) for {'C': 398107.17055349785, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.705 (+/-0.269) for {'C': 398107.17055349785, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.697 (+/-0.307) for {'C': 398107.17055349785, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.697 (+/-0.308) for {'C': 398107.17055349785, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.705 (+/-0.269) for {'C': 398107.17055349785, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.697 (+/-0.308) for {'C': 398107.17055349785, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.656 (+/-0.215) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.655 (+/-0.215) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.655 (+/-0.215) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.697 (+/-0.307) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.697 (+/-0.307) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.697 (+/-0.307) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.705 (+/-0.269) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.696 (+/-0.307) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.697 (+/-0.308) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.705 (+/-0.269) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.722 (+/-0.278) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.680 (+/-0.294) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.655 (+/-0.215) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.680 (+/-0.294) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.697 (+/-0.307) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.697 (+/-0.307) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.722 (+/-0.277) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.730 (+/-0.231) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.696 (+/-0.307) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.697 (+/-0.308) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.730 (+/-0.230) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.722 (+/-0.278) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.680 (+/-0.294) for {'C': 524807.460249773, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.697 (+/-0.307) for {'C': 524807.460249773, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.697 (+/-0.307) for {'C': 524807.460249773, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.697 (+/-0.307) for {'C': 524807.460249773, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.697 (+/-0.308) for {'C': 524807.460249773, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.747 (+/-0.233) for {'C': 524807.460249773, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.747 (+/-0.233) for {'C': 524807.460249773, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.696 (+/-0.307) for {'C': 524807.460249773, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.697 (+/-0.308) for {'C': 524807.460249773, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.721 (+/-0.277) for {'C': 524807.460249773, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.697 (+/-0.308) for {'C': 524807.460249773, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.705 (+/-0.269) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.722 (+/-0.277) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.697 (+/-0.307) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.697 (+/-0.308) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.722 (+/-0.277) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.747 (+/-0.233) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.722 (+/-0.278) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.696 (+/-0.307) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.697 (+/-0.308) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.721 (+/-0.277) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.697 (+/-0.308) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.705 (+/-0.270) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.747 (+/-0.233) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.697 (+/-0.307) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.747 (+/-0.233) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.722 (+/-0.277) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.747 (+/-0.234) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.722 (+/-0.278) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.722 (+/-0.278) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.697 (+/-0.308) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.721 (+/-0.277) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.697 (+/-0.308) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.616 (+/-0.239) for {'C': 0.1, 'kernel': 'linear'}
0.697 (+/-0.308) for {'C': 1.0, 'kernel': 'linear'}
0.697 (+/-0.308) for {'C': 10.0, 'kernel': 'linear'}
0.697 (+/-0.307) for {'C': 100.0, 'kernel': 'linear'}
0.696 (+/-0.307) for {'C': 1000.0, 'kernel': 'linear'}
0.695 (+/-0.307) for {'C': 10000.0, 'kernel': 'linear'}
0.694 (+/-0.307) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.7467928106191771
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.637 (+/-0.122) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.639 (+/-0.154) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.627 (+/-0.147) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.655 (+/-0.126) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.647 (+/-0.124) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.647 (+/-0.124) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.655 (+/-0.126) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.649 (+/-0.126) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.710 (+/-0.209) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.656 (+/-0.138) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.660 (+/-0.136) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.635 (+/-0.118) for {'C': 275422.87033381715, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.652 (+/-0.123) for {'C': 275422.87033381715, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.647 (+/-0.124) for {'C': 275422.87033381715, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.652 (+/-0.123) for {'C': 275422.87033381715, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.697 (+/-0.213) for {'C': 275422.87033381715, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.647 (+/-0.124) for {'C': 275422.87033381715, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.655 (+/-0.126) for {'C': 275422.87033381715, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.649 (+/-0.126) for {'C': 275422.87033381715, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.660 (+/-0.136) for {'C': 275422.87033381715, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.656 (+/-0.138) for {'C': 275422.87033381715, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.647 (+/-0.167) for {'C': 275422.87033381715, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.686 (+/-0.221) for {'C': 301995.17204020155, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.652 (+/-0.123) for {'C': 301995.17204020155, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.645 (+/-0.127) for {'C': 301995.17204020155, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.652 (+/-0.123) for {'C': 301995.17204020155, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.710 (+/-0.209) for {'C': 301995.17204020155, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.705 (+/-0.207) for {'C': 301995.17204020155, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.652 (+/-0.123) for {'C': 301995.17204020155, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.649 (+/-0.126) for {'C': 301995.17204020155, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.660 (+/-0.136) for {'C': 301995.17204020155, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.656 (+/-0.138) for {'C': 301995.17204020155, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.647 (+/-0.167) for {'C': 301995.17204020155, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.709 (+/-0.212) for {'C': 331131.12148259126, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.647 (+/-0.124) for {'C': 331131.12148259126, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.645 (+/-0.127) for {'C': 331131.12148259126, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.639 (+/-0.154) for {'C': 331131.12148259126, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.710 (+/-0.209) for {'C': 331131.12148259126, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.710 (+/-0.209) for {'C': 331131.12148259126, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.655 (+/-0.126) for {'C': 331131.12148259126, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.652 (+/-0.129) for {'C': 331131.12148259126, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.660 (+/-0.136) for {'C': 331131.12148259126, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.643 (+/-0.167) for {'C': 331131.12148259126, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.647 (+/-0.167) for {'C': 331131.12148259126, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.709 (+/-0.212) for {'C': 363078.0547701017, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.647 (+/-0.124) for {'C': 363078.0547701017, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.641 (+/-0.122) for {'C': 363078.0547701017, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.647 (+/-0.167) for {'C': 363078.0547701017, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.710 (+/-0.209) for {'C': 363078.0547701017, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.710 (+/-0.209) for {'C': 363078.0547701017, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.655 (+/-0.126) for {'C': 363078.0547701017, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.652 (+/-0.129) for {'C': 363078.0547701017, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.660 (+/-0.136) for {'C': 363078.0547701017, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.643 (+/-0.167) for {'C': 363078.0547701017, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.630 (+/-0.189) for {'C': 363078.0547701017, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.706 (+/-0.214) for {'C': 398107.17055349785, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.698 (+/-0.213) for {'C': 398107.17055349785, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.654 (+/-0.137) for {'C': 398107.17055349785, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.660 (+/-0.177) for {'C': 398107.17055349785, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.710 (+/-0.209) for {'C': 398107.17055349785, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.715 (+/-0.210) for {'C': 398107.17055349785, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.655 (+/-0.126) for {'C': 398107.17055349785, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.652 (+/-0.129) for {'C': 398107.17055349785, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.630 (+/-0.189) for {'C': 398107.17055349785, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.643 (+/-0.167) for {'C': 398107.17055349785, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.630 (+/-0.189) for {'C': 398107.17055349785, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.706 (+/-0.214) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.698 (+/-0.213) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.646 (+/-0.122) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.643 (+/-0.202) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.715 (+/-0.210) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.715 (+/-0.210) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.643 (+/-0.167) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.649 (+/-0.126) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.630 (+/-0.189) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.643 (+/-0.167) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.630 (+/-0.189) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.689 (+/-0.248) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.698 (+/-0.213) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.658 (+/-0.136) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.651 (+/-0.211) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.715 (+/-0.210) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.715 (+/-0.210) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.630 (+/-0.189) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.644 (+/-0.164) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.630 (+/-0.189) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.643 (+/-0.167) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.625 (+/-0.179) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.689 (+/-0.248) for {'C': 524807.460249773, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.701 (+/-0.212) for {'C': 524807.460249773, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.658 (+/-0.136) for {'C': 524807.460249773, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.651 (+/-0.211) for {'C': 524807.460249773, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.715 (+/-0.210) for {'C': 524807.460249773, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.715 (+/-0.210) for {'C': 524807.460249773, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.635 (+/-0.198) for {'C': 524807.460249773, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.640 (+/-0.164) for {'C': 524807.460249773, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.630 (+/-0.189) for {'C': 524807.460249773, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.626 (+/-0.188) for {'C': 524807.460249773, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.625 (+/-0.179) for {'C': 524807.460249773, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.686 (+/-0.248) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.701 (+/-0.212) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.658 (+/-0.136) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.651 (+/-0.211) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.715 (+/-0.210) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.715 (+/-0.210) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.639 (+/-0.202) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.640 (+/-0.164) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.630 (+/-0.189) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.626 (+/-0.188) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.625 (+/-0.179) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.686 (+/-0.248) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.697 (+/-0.216) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.661 (+/-0.132) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.651 (+/-0.211) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.715 (+/-0.210) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.715 (+/-0.210) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.639 (+/-0.202) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.632 (+/-0.154) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.627 (+/-0.185) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.636 (+/-0.203) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.621 (+/-0.177) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.727 (+/-0.397) for {'C': 0.1, 'kernel': 'linear'}
0.625 (+/-0.179) for {'C': 1.0, 'kernel': 'linear'}
0.632 (+/-0.282) for {'C': 10.0, 'kernel': 'linear'}
0.597 (+/-0.290) for {'C': 100.0, 'kernel': 'linear'}
0.562 (+/-0.158) for {'C': 1000.0, 'kernel': 'linear'}
0.558 (+/-0.134) for {'C': 10000.0, 'kernel': 'linear'}
0.544 (+/-0.100) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      1.00      0.99       623

avg / total       0.98      0.99      0.98       629

针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值：

0.705 (+/-0.151) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.722 (+/-0.277) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.705 (+/-0.269) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.747 (+/-0.233) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.730 (+/-0.231) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.722 (+/-0.165) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.747 (+/-0.233) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.746 (+/-0.233) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.772 (+/-0.166) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.747 (+/-0.233) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.747 (+/-0.233) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.705 (+/-0.151) for {'C': 275422.87033381715, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.746 (+/-0.233) for {'C': 275422.87033381715, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.730 (+/-0.231) for {'C': 275422.87033381715, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.746 (+/-0.233) for {'C': 275422.87033381715, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.755 (+/-0.173) for {'C': 275422.87033381715, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.722 (+/-0.165) for {'C': 275422.87033381715, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.747 (+/-0.233) for {'C': 275422.87033381715, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.746 (+/-0.233) for {'C': 275422.87033381715, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.747 (+/-0.233) for {'C': 275422.87033381715, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.747 (+/-0.233) for {'C': 275422.87033381715, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.722 (+/-0.278) for {'C': 275422.87033381715, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.730 (+/-0.066) for {'C': 301995.17204020155, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.746 (+/-0.233) for {'C': 301995.17204020155, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.730 (+/-0.231) for {'C': 301995.17204020155, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.746 (+/-0.233) for {'C': 301995.17204020155, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.772 (+/-0.166) for {'C': 301995.17204020155, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.772 (+/-0.166) for {'C': 301995.17204020155, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.746 (+/-0.233) for {'C': 301995.17204020155, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.746 (+/-0.233) for {'C': 301995.17204020155, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.747 (+/-0.233) for {'C': 301995.17204020155, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.747 (+/-0.233) for {'C': 301995.17204020155, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.722 (+/-0.278) for {'C': 301995.17204020155, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.772 (+/-0.166) for {'C': 331131.12148259126, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.746 (+/-0.233) for {'C': 331131.12148259126, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.730 (+/-0.231) for {'C': 331131.12148259126, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.721 (+/-0.277) for {'C': 331131.12148259126, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.772 (+/-0.166) for {'C': 331131.12148259126, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.772 (+/-0.167) for {'C': 331131.12148259126, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.747 (+/-0.233) for {'C': 331131.12148259126, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.746 (+/-0.233) for {'C': 331131.12148259126, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.747 (+/-0.233) for {'C': 331131.12148259126, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.722 (+/-0.278) for {'C': 331131.12148259126, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.722 (+/-0.278) for {'C': 331131.12148259126, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.772 (+/-0.166) for {'C': 363078.0547701017, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.746 (+/-0.233) for {'C': 363078.0547701017, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.730 (+/-0.230) for {'C': 363078.0547701017, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.722 (+/-0.277) for {'C': 363078.0547701017, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.772 (+/-0.166) for {'C': 363078.0547701017, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.772 (+/-0.167) for {'C': 363078.0547701017, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.747 (+/-0.233) for {'C': 363078.0547701017, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.746 (+/-0.233) for {'C': 363078.0547701017, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.747 (+/-0.233) for {'C': 363078.0547701017, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.721 (+/-0.279) for {'C': 363078.0547701017, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.697 (+/-0.308) for {'C': 363078.0547701017, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.772 (+/-0.166) for {'C': 398107.17055349785, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.771 (+/-0.165) for {'C': 398107.17055349785, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.746 (+/-0.233) for {'C': 398107.17055349785, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.722 (+/-0.277) for {'C': 398107.17055349785, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.772 (+/-0.166) for {'C': 398107.17055349785, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.772 (+/-0.167) for {'C': 398107.17055349785, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.747 (+/-0.233) for {'C': 398107.17055349785, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.746 (+/-0.233) for {'C': 398107.17055349785, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.697 (+/-0.308) for {'C': 398107.17055349785, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.721 (+/-0.279) for {'C': 398107.17055349785, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.697 (+/-0.308) for {'C': 398107.17055349785, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.772 (+/-0.166) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.771 (+/-0.165) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.730 (+/-0.230) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.697 (+/-0.307) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.772 (+/-0.167) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.772 (+/-0.167) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.722 (+/-0.277) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.746 (+/-0.233) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.697 (+/-0.308) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.721 (+/-0.279) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.697 (+/-0.308) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.747 (+/-0.235) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.771 (+/-0.165) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.747 (+/-0.233) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.697 (+/-0.308) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.772 (+/-0.167) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.772 (+/-0.167) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.697 (+/-0.308) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.722 (+/-0.277) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.697 (+/-0.308) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.721 (+/-0.279) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.697 (+/-0.308) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.747 (+/-0.235) for {'C': 524807.460249773, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.771 (+/-0.166) for {'C': 524807.460249773, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.747 (+/-0.233) for {'C': 524807.460249773, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.697 (+/-0.308) for {'C': 524807.460249773, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.772 (+/-0.167) for {'C': 524807.460249773, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.772 (+/-0.167) for {'C': 524807.460249773, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.697 (+/-0.307) for {'C': 524807.460249773, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.721 (+/-0.276) for {'C': 524807.460249773, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.697 (+/-0.308) for {'C': 524807.460249773, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.696 (+/-0.308) for {'C': 524807.460249773, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.697 (+/-0.308) for {'C': 524807.460249773, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.746 (+/-0.234) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.771 (+/-0.166) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.747 (+/-0.233) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.697 (+/-0.308) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.772 (+/-0.167) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.772 (+/-0.167) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.697 (+/-0.308) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.721 (+/-0.276) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.697 (+/-0.308) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.696 (+/-0.309) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.697 (+/-0.308) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.747 (+/-0.233) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.771 (+/-0.166) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.747 (+/-0.233) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.697 (+/-0.308) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.772 (+/-0.167) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.772 (+/-0.167) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.697 (+/-0.308) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.721 (+/-0.275) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.697 (+/-0.307) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.696 (+/-0.309) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.696 (+/-0.308) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.657 (+/-0.216) for {'C': 0.1, 'kernel': 'linear'}
0.697 (+/-0.308) for {'C': 1.0, 'kernel': 'linear'}
0.696 (+/-0.306) for {'C': 10.0, 'kernel': 'linear'}
0.688 (+/-0.301) for {'C': 100.0, 'kernel': 'linear'}
0.685 (+/-0.298) for {'C': 1000.0, 'kernel': 'linear'}
0.685 (+/-0.298) for {'C': 10000.0, 'kernel': 'linear'}
0.684 (+/-0.298) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证，分类结果如下报告：
交叉验证集真实标签有： [-1  1]
交叉验证集预测标签有： [-1  1]
检查交叉验证集中测试集标签是否有预测不到的值： set()
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.00      0.00      0.00         6
          1       0.99      0.99      0.99       623

avg / total       0.98      0.98      0.98       629

本轮grid search结果，得到最好的参数选择是： {'C': 398107.17055349785, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
本轮grid search结果，最好的参数对应的真正测试集上的score是： 0.7719535823233572
第0轮loop中，tunemodel函数得到的最优参数是： ([{'C': array([363078.0547701 , 369828.17978027, 376703.79898391, 383707.24549228,
       390840.8957924 , 398107.1705535 , 405508.53544838, 413047.50199016,
       420726.62838444, 428548.52039744, 436515.83224017]), 'kernel': ['rbf'], 'gamma': array([5.75439937e-07, 5.86138165e-07, 5.97035287e-07, 6.08135001e-07,
       6.19441075e-07, 6.30957344e-07, 6.42687717e-07, 6.54636174e-07,
       6.66806769e-07, 6.79203633e-07, 6.91830971e-07])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
       1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}], {'C': 398107.17055349785, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}, 0.7719535823233572)
这是第0次迭代微调C和gamma。
第0次迭代，得到delta： [9.31322575e-10 0.00000000e+00]
SVC模型clf_op_iter的参数是： {'kernel': 'rbf', 'decision_function_shape': 'ovr', 'class_weight': {-1: 15}, 'tol': 0.001, 'gamma': 6.309573444801931e-07, 'random_state': 0, 'cache_size': 200, 'verbose': False, 'degree': 3, 'C': 398107.17055349785, 'probability': False, 'shrinking': True, 'coef0': 0.0, 'max_iter': -1}

训练模型SVC对训练样本的预测准确率： 0.9128277817150957

测试集中，预测为舞弊样本的有： (array([ 112, 1230, 1247, 1248], dtype=int64),) 
测试集中，实际为舞弊样本的有： (array([1246, 1247, 1248, 1249, 1250, 1251, 1252, 1253, 1254, 1255, 1256],
      dtype=int64),) 
预测的舞弊样本数目： 4

训练模型SVC对测试样本的预测准确率： 0.9029057406094968
以上是第47次特征筛选。
第47次特征筛选，AUC值是： 0.5901065226907923
X_train_iter_svc.shape is: (1257, 5)
X_test_iter_svc.shape is: (1257, 5)
AUC值随特征数目变化： [0.8173792499635195, 0.8173792499635195, 0.8173792499635195, 0.6814168977090325, 0.6814168977090325, 0.8173792499635195, 0.8173792499635195, 0.8169779658543703, 0.8169779658543703, 0.8169779658543703, 0.726871443163578, 0.726871443163578, 0.726871443163578, 0.726871443163578, 0.726871443163578, 0.726871443163578, 0.8177805340726688, 0.726871443163578, 0.6814168977090325, 0.6814168977090325, 0.6814168977090325, 0.6814168977090325, 0.6818181818181819, 0.6818181818181819, 0.6818181818181819, 0.6818181818181819, 0.7264701590544287, 0.5909090909090908, 0.5454545454545454, 0.7264701590544287, 0.6997300452356633, 0.6467605428279586, 0.8153728294177732, 0.6997300452356633, 0.8177805340726688, 0.5454545454545454, 0.5454545454545454, 0.5454545454545454, 0.5067123887348606, 0.5352035604844594, 0.5267765941923246, 0.4970815701152779, 0.5829563694732233, 0.6616080548664818, 0.5905078067999416, 0.8895374288632715, 0.6802130453815847, 0.5901065226907923]
样本特征数目： [52, 51, 50, 49, 48, 47, 46, 45, 44, 43, 42, 41, 40, 39, 38, 37, 36, 35, 34, 33, 32, 31, 30, 29, 28, 27, 26, 25, 24, 23, 22, 21, 20, 19, 18, 17, 16, 15, 14, 13, 12, 11, 10, 9, 8, 7, 6, 5]

各轮特征筛选得到的最优超参是：
 [{'C': 0.6309573444801931, 'kernel': 'linear'}, {'C': 0.6309573444801931, 'kernel': 'linear'}, {'C': 0.6309573444801931, 'kernel': 'linear'}, {'C': 0.9999999999999997, 'kernel': 'linear'}, {'C': 0.9999999999999997, 'kernel': 'linear'}, {'C': 0.6309573444801931, 'kernel': 'linear'}, {'C': 0.6309573444801931, 'kernel': 'linear'}, {'C': 0.5306400191947748, 'kernel': 'linear'}, {'C': 0.5306400191947748, 'kernel': 'linear'}, {'C': 0.5365373995198518, 'kernel': 'linear'}, {'C': 0.9999999999999997, 'kernel': 'linear'}, {'C': 0.9999999999999997, 'kernel': 'linear'}, {'C': 0.9999999999999997, 'kernel': 'linear'}, {'C': 0.9999999999999997, 'kernel': 'linear'}, {'C': 0.9999999999999997, 'kernel': 'linear'}, {'C': 0.9999999999999997, 'kernel': 'linear'}, {'C': 27.039583641088424, 'kernel': 'rbf', 'gamma': 0.03981071705534969}, {'C': 0.9999999999999997, 'kernel': 'linear'}, {'C': 0.9999999999999997, 'kernel': 'linear'}, {'C': 0.9999999999999997, 'kernel': 'linear'}, {'C': 0.9999999999999997, 'kernel': 'linear'}, {'C': 0.9999999999999997, 'kernel': 'linear'}, {'C': 0.9999999999999997, 'kernel': 'linear'}, {'C': 0.9999999999999997, 'kernel': 'linear'}, {'C': 0.9999999999999997, 'kernel': 'linear'}, {'C': 0.9999999999999997, 'kernel': 'linear'}, {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.50264932573108e-07}, {'C': 22490546.058357812, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-05}, {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}, {'C': 478630.092322638, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}, {'C': 52.48074602497723, 'kernel': 'rbf', 'gamma': 0.01650440985652279}, {'C': 39.810717055349684, 'kernel': 'rbf', 'gamma': 0.021203131167398505}, {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}, {'C': 1242796.4384409143, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}, {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0015848931924611123}, {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-08}, {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-08}, {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-08}, {'C': 1803017.7408595693, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}, {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}, {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 4.01051366086576e-06}, {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}, {'C': 1202264.4346174113, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}, {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}, {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}, {'C': 0.7585775750291837, 'kernel': 'linear'}, {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}, {'C': 398107.17055349785, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}]
In [865]:
# 根据上一步试验，当{'C': 9.999999999999996, 'gamma': 0.09999999999999998, 'kernel': 'rbf'}, class_weight={-1:10}时，选24个特征时模型最好。
# 通过调节class_weight，我们发现当将class_weight调大时，最优分类超平面将远离负例样本，也就是说更多负例样本将被正确地分类。由此看，似乎
# class_weight就应该按照正负样本的比例设置权重，使其：权重_正例*正例样本数量 = 权重_负例*负例样本数量，或者使权重_负例*负例样本数量
# 稍微大一点也无妨。那么不同kernel之间，这个class_weight是否应有所不同呢？按照前面的grid_serach试验看，似乎应有所不同。因为kernel是
# 将样本从低维映射到高维，目的是在高维依然是线性可分，问题至此变成了2个不同维度的线性SVM，对不均衡样本，class_weight是否应有所不同？
# 以2维和3维简单分析，认为class_weight应有所不同，假设样本在2维是线性不可分的，在3维线性可分，在这两个维度空间中，正负样本的数目比例
# 是不变的，但几何间隔并不一样，甚至核函数映射后，在高维空间是否能够完全线性可分也未必，现实中总存在soft margin的情况，因此利用
# class_weight，使最优分类超平面距离负样本究竟多远，就不一样。也即，class_weight应不同。

class_weight={-1:10}
auc_svc = np.array(auc_values_svc)
best_param_op_idx = np.max(np.where(auc_svc==np.max(auc_svc))[0])
print("最佳超参的索引best_param_op_idx是：", best_param_op_idx)
param_op_svc = best_params_svc[best_param_op_idx]    
clf_op_svc = SVC(class_weight=class_weight, random_state=0)    # 或者'balanced' 

feature_count_op_svc = feature_count_svc[best_param_op_idx]
X_train_input = X_train[:,:feature_count_op_svc]
y_train_input = y_train
X_test_input = X_test[:,:feature_count_op_svc]
y_test_input = y_test
try:
    clf_op_svc = svc_op(X_train_input, y_train_input, class_weight, 
                         kernel = param_op_svc['kernel'], 
                         C=param_op_svc['C'], 
                         gamma=param_op_svc['gamma'])
except KeyError:
    clf_op_svc = svc_op(X_train_input, y_train_input, class_weight,
                        kernel = param_op_svc['kernel'],
                        C = param_op_svc['C'])

y_pred_op_svc = clf_op_svc.predict(X_test_input)
test_sample_weight = np.ones(X_test.shape[0], dtype=int)
test_sample_weight[test_set["label"]==-1]=class_weight[-1]  
auc_value_svc = auc(y_test, y_pred_op_svc)
print("对测试集预测结果：\n", y_pred_op_svc)
print("测试集中预测为舞弊样本的index是：\n", np.where(y_pred_op_svc==-1))
print("auc value is:", auc_value_svc)

# 检查支持向量有哪些
print("训练样本中，支持向量是：", clf_op_svc.support_vectors_)
print("训练样本中，支持向量下标是：", clf_op_svc.support_)
print("训练样本中，标签为-1的样本下标是：", np.where(y_train==-1))
print("测试样本中，标签为-1的样本下标是：", np.where(y_test==-1))
print("训练样本中，支持向量个数：", clf_op_svc.support_.size)
print("训练样本数目：", y_train_input.shape)

distance = clf_op_svc.decision_function(X_test_input)
print("根据desicion function，测试样本到最优分类超平面的距离是:\n", distance)
print("根据desicion function，测试样本到最优分类超平面的距离的shape是:\n", distance.shape)
# 测试集中实际舞弊样本的idx是：
Nidx = np.where(y_test==-1)
print("根据desicion function，测试集中真实舞弊样本到最优分类超平面的距离是:\n", distance[Nidx])

# 打印分类结果评估报告
print("得到的分类结果评估报告如下：\n", classification_report(y_test, y_pred_op_svc))

最佳超参的索引best_param_op_idx是： 45
对测试集预测结果：
 [ 1  1  1 ...  1 -1  1]
测试集中预测为舞弊样本的index是：
 (array([ 370, 1246, 1247, 1248, 1252, 1255], dtype=int64),)
auc value is: 0.726871443163578
训练样本中，支持向量是： [[3.00000000e-01 5.09536366e-02 3.18333487e-02 4.63639400e-01
  3.90075291e-02 9.18758298e-01 1.29804903e-01]
 [1.00000000e-01 4.73966837e-02 6.00575013e-01 4.48857231e-01
  1.92896149e-03 9.20066454e-01 9.50402118e-01]
 [1.00000000e-01 4.26945469e-02 1.00000000e+00 4.47333690e-01
  2.63705446e-03 9.19612610e-01 1.00000000e+00]
 [1.00000000e-01 5.24787432e-02 2.42621560e-02 4.67360952e-01
  5.40719382e-01 9.17890871e-01 4.09751391e-02]
 [1.00000000e-01 2.00099829e-01 1.08397168e-02 4.38234862e-01
  4.53281737e-02 9.05332085e-01 2.41172819e-02]
 [1.00000000e-01 1.69757042e-01 9.01655140e-02 4.40798917e-01
  7.75189627e-03 9.03510305e-01 7.06157430e-02]
 [3.00000000e-01 2.30162535e-02 1.15321383e-02 4.48583622e-01
  4.76058010e-02 7.79720746e-01 5.75888869e-02]
 [0.00000000e+00 2.11800484e-02 1.11203412e-02 4.69903748e-01
  3.52041659e-01 8.13106021e-01 2.36165693e-02]
 [1.00000000e-01 1.73176593e-02 6.06008215e-03 4.63031072e-01
  2.07417973e-01 8.81985359e-01 2.06227587e-02]
 [1.00000000e-01 3.51378378e-02 4.95555223e-01 4.51758984e-01
  7.08240943e-01 8.99691673e-01 1.01806111e-01]
 [1.00000000e-01 6.78342950e-02 1.32389211e-01 4.57329245e-01
  4.42289077e-03 9.46416845e-01 2.35613510e-01]
 [3.00000000e-01 5.16093136e-02 7.19590419e-02 4.55863938e-01
  6.70428455e-04 9.30792479e-01 2.87978392e-01]
 [3.00000000e-01 5.29447578e-02 8.02588859e-02 4.56653850e-01
  4.53050369e-04 9.34386629e-01 3.04407936e-01]
 [3.00000000e-01 4.68553739e-02 1.49729716e-01 4.54257514e-01
  5.48287798e-04 9.35097285e-01 2.82127455e-01]
 [2.00000000e-01 5.65174309e-02 1.22197277e-01 4.48612586e-01
  2.25233829e-03 9.27910815e-01 2.66856950e-01]
 [2.00000000e-01 5.62219472e-02 1.10015402e-01 4.50413022e-01
  1.99568149e-03 9.25394544e-01 2.68431701e-01]
 [1.00000000e-01 5.33864712e-02 5.51593445e-02 4.51955769e-01
  2.95718136e-01 9.30266350e-01 1.19058109e-01]
 [1.00000000e-01 4.52076003e-02 8.71596335e-02 4.48391254e-01
  3.91732530e-02 9.20656634e-01 2.04225057e-01]
 [1.00000000e-01 4.34001277e-02 6.79235956e-01 4.53290845e-01
  1.40916959e-01 9.28753232e-01 3.47548345e-02]
 [1.00000000e-01 4.86626707e-02 1.28564865e-01 4.32164114e-01
  2.30114075e-02 9.32996428e-01 2.50895121e-01]
 [1.00000000e-01 4.52092884e-02 2.61834832e-01 4.46119911e-01
  1.23335161e-02 9.26031084e-01 2.32891466e-01]
 [1.00000000e-01 4.43679582e-02 2.50944811e-01 4.47570119e-01
  1.78330741e-02 9.28018786e-01 1.98066367e-01]
 [1.00000000e-01 4.44600638e-02 2.17967853e-01 4.54066378e-01
  1.94687904e-02 9.23346756e-01 1.84989429e-01]
 [1.00000000e-01 1.17997947e-01 1.85536817e-03 4.61214716e-01
  4.65230536e-01 9.22535145e-01 6.50189532e-03]
 [1.00000000e-01 9.57486077e-02 9.99800766e-02 4.52742908e-01
  5.31306480e-02 9.23144540e-01 2.29283111e-01]
 [1.00000000e-01 4.71105548e-02 8.78283789e-02 4.47678577e-01
  7.15087815e-03 9.21749458e-01 3.10371958e-01]
 [1.00000000e-01 4.91626227e-02 8.36521419e-02 4.47634927e-01
  6.59990003e-03 9.32117715e-01 2.34287460e-01]
 [0.00000000e+00 6.15588710e-02 8.17456196e-02 4.49895281e-01
  1.68478778e-02 9.25242652e-01 2.18658906e-01]
 [1.00000000e-01 5.57015273e-02 1.07808933e-01 4.51824408e-01
  1.86665361e-02 9.26321446e-01 2.47927473e-01]
 [1.00000000e-01 5.40952874e-02 1.07975411e-01 4.51765352e-01
  9.24373410e-02 9.28070636e-01 1.39124307e-01]
 [1.00000000e-01 5.78056793e-02 4.93351288e-02 4.29982536e-01
  2.73513825e-01 9.18724138e-01 7.62532177e-02]
 [1.00000000e-01 4.78266157e-02 3.28024883e-01 4.47644376e-01
  3.28219388e-03 9.17332411e-01 4.68203096e-01]
 [1.00000000e-01 6.10288854e-02 1.66623820e-02 4.72198919e-01
  5.34327713e-01 9.16885582e-01 3.35211531e-02]
 [1.00000000e-01 5.22336908e-02 2.44531794e-01 4.49535091e-01
  1.61744900e-01 9.30516452e-01 2.11066741e-01]
 [1.00000000e-01 6.73208385e-02 1.55520401e-01 4.55663867e-01
  1.49859054e-01 9.50216567e-01 1.55669072e-01]
 [1.00000000e-01 6.76143879e-02 1.73620688e-01 4.60964627e-01
  1.84058438e-01 9.48014754e-01 1.25993977e-01]
 [1.00000000e-01 6.43693776e-02 2.58116492e-01 4.50688582e-01
  2.05221594e-01 9.43791383e-01 1.24665364e-01]
 [1.00000000e-01 6.01872386e-02 1.31135048e-01 4.54674500e-01
  2.71396003e-01 9.39971531e-01 1.02964616e-01]
 [3.00000000e-01 5.95128521e-02 2.88183556e-01 4.54675322e-01
  9.80009287e-02 9.24764103e-01 2.17611457e-01]
 [3.00000000e-01 5.97390895e-02 2.36391555e-01 4.51276061e-01
  1.29533665e-01 9.20193030e-01 1.38568067e-01]
 [3.00000000e-01 5.45614778e-02 4.04007221e-01 4.50937336e-01
  9.83216152e-02 9.28453719e-01 2.22011726e-01]
 [3.00000000e-01 5.37880223e-02 6.73946570e-01 4.50418157e-01
  6.64999384e-02 9.39765959e-01 1.68022225e-01]
 [3.00000000e-01 5.62890834e-02 4.40832753e-01 4.57625860e-01
  1.10909636e-01 9.38684420e-01 1.48406764e-01]
 [4.00000000e-01 5.48627291e-02 3.12156232e-03 4.82552302e-01
  6.39298192e-01 9.23598384e-01 1.58514132e-02]
 [2.00000000e-01 6.41912855e-02 6.03849337e-02 4.53138429e-01
  5.69648961e-03 9.15212643e-01 2.34119808e-01]
 [1.00000000e-01 1.48817896e-01 1.48088223e-01 4.49607910e-01
  4.37236651e-02 9.36783644e-01 1.63591415e-01]
 [1.00000000e-01 4.10036922e-02 2.94683480e-02 4.50520863e-01
  2.25725620e-01 9.18952280e-01 8.20664597e-02]
 [5.00000000e-01 5.06881514e-02 1.41004835e-01 4.46307246e-01
  1.45095568e-01 9.16047435e-01 2.07858294e-01]
 [3.00000000e-01 5.18027742e-02 1.29273821e-01 4.52813570e-01
  1.42642532e-01 9.20930526e-01 1.72229428e-01]
 [3.00000000e-01 4.55900200e-02 1.00410749e-01 4.49648170e-01
  1.44445048e-01 9.20510233e-01 1.67518095e-01]
 [1.00000000e-01 5.67087462e-02 1.07372655e-01 4.47220816e-01
  3.95821821e-02 9.25778237e-01 2.13215245e-01]
 [1.00000000e-01 5.49881392e-02 1.24211296e-01 4.60346029e-01
  4.43618097e-02 9.27764109e-01 2.30262004e-01]
 [1.00000000e-01 4.91625172e-02 1.71412519e-01 4.43372319e-01
  4.26066431e-02 9.23097264e-01 2.72414656e-01]
 [1.00000000e-01 4.47937754e-02 2.07525859e-01 4.52166933e-01
  7.16040189e-02 9.18224848e-01 2.26137987e-01]
 [3.00000000e-01 8.79819792e-02 6.22776797e-03 4.44263910e-01
  1.00000000e+00 9.15974540e-01 2.27038821e-02]
 [1.00000000e-01 4.11253041e-02 1.76034770e-02 4.49588601e-01
  3.29338025e-01 9.18854069e-01 2.55375159e-02]
 [1.00000000e-01 5.65530915e-02 4.97558566e-02 4.62829255e-01
  2.54548125e-01 9.29462668e-01 8.17928778e-02]
 [1.00000000e-01 5.45751583e-02 5.17950575e-02 4.68413176e-01
  2.30285718e-01 9.26592289e-01 8.29501015e-02]
 [1.00000000e-01 5.09700953e-02 1.76327844e-01 4.47726233e-01
  3.06298638e-02 9.24818089e-01 2.34906810e-01]
 [1.00000000e-01 5.16424421e-02 1.58764294e-01 4.48581055e-01
  6.68211629e-02 9.23485533e-01 2.03678641e-01]
 [1.00000000e-01 4.97174723e-02 1.53592677e-01 4.49729924e-01
  7.93327784e-02 9.23106719e-01 1.82722181e-01]
 [3.00000000e-01 8.43070369e-02 1.50552252e-02 4.28597853e-01
  4.76913532e-01 9.24942225e-01 3.43379478e-02]
 [3.00000000e-01 6.56127513e-02 1.19166512e-02 4.67230927e-01
  6.13385002e-01 9.15488366e-01 2.32358825e-02]
 [1.00000000e-01 6.54536501e-02 1.04695258e-01 4.56396161e-01
  6.24154902e-04 9.34822478e-01 2.26220211e-01]
 [1.00000000e-01 1.07084850e-01 6.16901904e-02 4.49530675e-01
  3.62617318e-01 9.38427913e-01 6.27275711e-02]
 [1.00000000e-01 1.08129382e-01 4.68654941e-02 4.49363161e-01
  4.10532506e-01 9.36298080e-01 6.96173078e-02]
 [2.00000000e-01 1.14937605e-01 3.37979281e-02 4.53959563e-01
  4.63928420e-01 9.30957790e-01 5.87996097e-02]
 [1.00000000e-01 4.37961223e-02 1.07299256e-01 4.52325716e-01
  7.76663924e-02 9.21368510e-01 1.26487108e-01]
 [1.00000000e-01 4.09228756e-02 1.36686184e-01 4.50376253e-01
  6.41432156e-02 9.19529954e-01 1.56953797e-01]
 [1.00000000e-01 4.02397322e-02 1.13394360e-01 4.50186247e-01
  7.96706830e-02 9.21122983e-01 1.12835776e-01]
 [1.00000000e-01 5.97125023e-02 1.84457229e-01 4.48958294e-01
  1.48647333e-01 9.42911146e-01 1.97001619e-01]
 [1.00000000e-01 7.09419734e-02 1.22598443e-01 4.55242875e-01
  1.19371239e-01 9.35929332e-01 1.61258387e-01]
 [1.00000000e-01 6.76003206e-02 1.32447790e-01 4.55864144e-01
  1.06649779e-01 9.31832233e-01 1.69807021e-01]
 [1.00000000e-01 6.05556613e-02 1.75231453e-02 4.50967224e-01
  3.46207963e-01 9.20028939e-01 2.47954703e-02]
 [1.00000000e-01 4.68022698e-02 1.32688874e-01 4.52496208e-01
  8.32493504e-02 9.25163962e-01 2.53004329e-01]
 [1.00000000e-01 7.12537048e-02 8.92032625e-02 4.50288542e-01
  1.04441562e-01 9.30292580e-01 1.53949521e-01]
 [1.00000000e-01 6.32397379e-02 9.10654591e-02 4.50323565e-01
  1.23480438e-01 9.30169359e-01 1.60863605e-01]
 [1.00000000e-01 1.82231294e-01 1.49640096e-02 4.58769902e-01
  4.85439704e-01 9.19676966e-01 2.67355313e-02]
 [1.00000000e-01 2.07035291e-01 1.04721260e-02 4.53205496e-01
  4.52572029e-01 9.19368913e-01 2.83792653e-02]
 [1.00000000e-01 2.43439537e-01 8.82049452e-03 4.60438156e-01
  3.68667316e-01 9.03385254e-01 2.38097425e-02]
 [3.00000000e-01 4.37228316e-02 1.89981468e-01 4.44482571e-01
  1.43443710e-01 9.21659787e-01 1.51496147e-01]
 [1.00000000e-01 8.73975883e-02 1.38339232e-02 4.54580216e-01
  4.17605365e-01 9.24428601e-01 3.28169092e-02]
 [1.00000000e-01 1.13571811e-01 8.57893272e-03 4.53166262e-01
  5.79541816e-01 9.20267146e-01 2.60480526e-02]
 [1.00000000e-01 4.96960196e-02 1.06115227e-01 4.47209724e-01
  2.81434674e-01 9.21611597e-01 9.04172220e-02]
 [1.00000000e-01 4.81344786e-02 7.65142904e-02 4.55348560e-01
  3.64104529e-01 9.20355901e-01 8.02272033e-02]
 [1.00000000e-01 6.56001611e-02 3.97854254e-01 4.53568562e-01
  2.20654900e-02 9.17541948e-01 3.83967416e-01]
 [1.00000000e-01 1.39799489e-01 2.67991512e-02 4.34473562e-01
  6.86381532e-01 9.29935727e-01 3.92805757e-02]
 [1.00000000e-01 6.48984137e-02 3.13375819e-02 4.57662834e-01
  6.56273053e-01 9.29790851e-01 4.23735890e-02]
 [1.00000000e-01 6.29743934e-02 2.52425044e-01 4.52033107e-01
  8.30755556e-02 9.27443551e-01 2.80885551e-01]
 [1.00000000e-01 1.15871463e-01 9.46019645e-02 4.55143353e-01
  1.23702659e-01 9.33021133e-01 1.89034961e-01]
 [1.00000000e-01 5.91524478e-02 1.80408139e-02 4.85040869e-01
  4.61299975e-01 9.21830589e-01 3.88597379e-02]
 [1.00000000e-01 2.21457582e-01 1.10805928e-02 4.65821697e-01
  4.32375238e-01 9.15950749e-01 3.70093759e-02]
 [1.00000000e-01 7.13498197e-02 2.26781549e-01 4.50482965e-01
  2.06641546e-01 9.35172926e-01 1.50910648e-01]
 [1.00000000e-01 7.38215855e-02 4.00299750e-02 4.54199485e-01
  3.60495192e-01 9.42735769e-01 5.72735522e-02]
 [1.00000000e-01 5.67583334e-02 1.49017182e-02 4.59647012e-01
  4.31766687e-01 9.30991646e-01 3.66042355e-02]
 [1.00000000e-01 1.05576729e-01 3.51246125e-01 4.56298898e-01
  5.15826362e-02 9.25896883e-01 2.04470768e-01]
 [1.00000000e-01 8.04610362e-02 8.65692220e-02 4.42415921e-01
  1.91560672e-01 9.27511566e-01 9.89963964e-02]
 [1.00000000e-01 8.09132648e-02 2.31892962e-01 4.64039132e-01
  1.92610436e-01 9.47292813e-01 1.10458219e-01]
 [1.00000000e-01 5.30850792e-02 5.33522082e-02 4.51999522e-01
  5.63384814e-01 9.21467026e-01 6.51879931e-02]
 [1.00000000e-01 8.62356994e-02 9.73123111e-02 4.55459174e-01
  2.49059867e-01 9.26502618e-01 7.74385258e-02]
 [1.00000000e-01 1.39029796e-01 3.28418640e-02 4.59421367e-01
  4.41562690e-01 9.26377872e-01 5.22072679e-02]
 [1.00000000e-01 6.26818639e-02 1.73876876e-01 4.45146052e-01
  1.44111448e-01 9.27706158e-01 1.52159172e-01]
 [1.00000000e-01 4.01735455e-02 6.47958444e-01 4.17567535e-01
  1.51626596e-02 9.32884797e-01 4.61219389e-02]]
训练样本中，支持向量下标是： [1246 1247 1248 1249 1250 1251 1252 1253 1254 1255   26   48   49   50
   51   52   67   68  112  142  144  145  146  192  209  244  245  254
  255  286  349  371  404  405  458  459  460  461  472  473  474  475
  476  563  604  618  644  694  696  697  720  721  722  723  737  743
  752  753  777  778  779  845  846  852  861  862  863  882  883  884
  903  904  905  930  957  959  960  963  964  965 1014 1016 1017 1050
 1051 1114 1122 1124 1142 1143 1159 1161 1165 1172 1173 1177 1184 1187
 1199 1205 1208 1212 1230]
训练样本中，标签为-1的样本下标是： (array([1246, 1247, 1248, 1249, 1250, 1251, 1252, 1253, 1254, 1255, 1256],
      dtype=int64),)
测试样本中，标签为-1的样本下标是： (array([1246, 1247, 1248, 1249, 1250, 1251, 1252, 1253, 1254, 1255, 1256],
      dtype=int64),)
训练样本中，支持向量个数： 103
训练样本数目： (1257,)
根据desicion function，测试样本到最优分类超平面的距离是:
 [ 1.05155175  1.00452876  1.02674393 ...  0.4619497  -1.51103659
  0.10535483]
根据desicion function，测试样本到最优分类超平面的距离的shape是:
 (1257,)
根据desicion function，测试集中真实舞弊样本到最优分类超平面的距离是:
 [-0.61381692 -0.90587131 -1.55419457  0.50349293  0.37889801  0.16567334
 -0.28684544  1.34096502  0.4619497  -1.51103659  0.10535483]
得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.83      0.45      0.59        11
          1       1.00      1.00      1.00      1246

avg / total       0.99      0.99      0.99      1257
In [893]:
# 由上面看到基于rbf和linear的核函数，SVC的分类准确率并不是非常高。SVC模型的最佳特征向量数目是49-52个。
# 接下来我们用逻辑回归训练模型分类财报数据是否舞弊。
from sklearn.linear_model import LogisticRegressionCV
from sklearn.linear_model.coordinate_descent import ConvergenceWarning

warnings.filterwarnings(action='ignore', category=ConvergenceWarning)    # 只拦截坐标下降法时聚合异常

# 使用之前整理的曼.惠特尼 U检验得到的样本。
X_train_lg_orgin = feature_test_sample.iloc[::2,:]
X_train_lg_orgin.reset_index(drop=True, inplace=True)
X_test_lg_orgin = feature_test_sample.iloc[1:feature_test_sample.shape[0]:2,:]
X_test_lg_orgin.reset_index(drop=True, inplace=True)

X_train_iter_lg = np.array(X_train_lg_orgin)
X_test_iter_lg = np.array(X_test_lg_orgin)
feature_selection_iteration_count = X_train_iter_lg.shape[1]-4    # 设置最多要删减多少个属性
print("feature_selection_iteration_count:", feature_selection_iteration_count)
reduce_count_per_iterate = 1    
auc_values_lg = []
class_weight_lg = {1:1, -1:120}
# 经过多次试验，发现class_weight=20最合适。而且class_weight的margin很窄，稍大（如190）就会导致将很多非舞弊样本分类为舞弊样本。而该值
# 稍小，如170，则又使舞弊样本被分类为非舞弊样本。所以逻辑回归中，对于不均衡样本，超参class_weight是需要细心调试的。

cv_iter = CV_set(10, 0.5, 0.5)
lr = LogisticRegressionCV(Cs=np.logspace(-5,5,11), cv=cv_iter, class_weight=class_weight_lg, random_state=0)
for i in range(feature_selection_iteration_count): 
    for train_idx, test_idx in cv_iter.split(X_train_iter_lg, y_train):
        X_train_cv, X_test_cv = X_train_iter_lg[train_idx], X_train_iter_lg[test_idx]
        y_train_cv, y_test_cv = y_train[train_idx], y_train[test_idx]
        lr.fit(X_train_cv, y_train_cv)
    score = lr.score(X_train_cv, y_train_cv)
    print("\n第%d次特征筛选：\n"%i)
    print("R值，即准确率是：", score)
    print("参数：",lr.coef_)
    print("截距：",lr.intercept_)
    #print("稀疏的特征个数比例是：%0.2f"%np.mean(lr.coef_.ravel()==0)*100)
    
    print("训练结果用决策函数计算测试集的分类score值是：\n",lr.decision_function(X_test_iter_lg))
    print("对测试集预测结果，用概率表示：\n", lr.predict_proba(X_test_iter_lg))
    y_pred_iter_lg = lr.predict(X_test_iter_lg)
    print("对测试集预测结果：\n", y_pred_iter_lg)
    print("测试集预测结果中，舞弊样本的索引号：", np.where(y_pred_iter_lg==-1))
    print("预测测试集中多少舞弊样本：", np.where(y_pred_iter_lg==-1)[0].size)
    
    # 计算训练模型clf_op的AUC值，评估模型质量。
    auc_value_iter_lg = auc(y_test, y_pred_iter_lg)
    auc_values_lg.append(auc_value_iter_lg)
    print("第%d次特征筛选，AUC值是："%i, auc_value_iter_lg)
    print("X_train_iter_lg.shape is:",X_train_iter_lg.shape)
    print("X_test_iter_lg.shape is:",X_test_iter_lg.shape)
    X_train_iter_lg = feature_reduce(X_train_iter_lg, n=reduce_count_per_iterate)
    X_test_iter_lg = feature_reduce(X_test_iter_lg, n=reduce_count_per_iterate)


# 计算特征数目
print("AUC值随特征数目变化：", auc_values_lg)
feature_count_lg = [X_train_lg_orgin.shape[1] - reduce_count_per_iterate*i for i in range(feature_selection_iteration_count)]
print("样本特征数目：", feature_count_lg)

auc_plot_by_feature_selection(feature_count_lg, auc_values_lg)

feature_selection_iteration_count: 25

第0次特征筛选：

R值，即准确率是： 0.9920382165605095
参数： [[-2.03213230e-16  6.76838587e-17  3.50660744e-18  5.28404801e-18
   2.38926333e-18  1.70635782e-17 -7.87191586e-17 -4.22540598e-17
  -1.18605367e-15  4.30985235e-17 -2.89865627e-14 -4.16543603e-17
  -1.27580511e-16  1.40824450e-17  1.15945078e-17 -1.70226810e-16
   3.85953638e-17  4.88761672e-10 -2.12277807e-10 -3.01744799e-17
  -5.05665447e-17 -3.45748875e-17 -1.91307534e-17 -1.20871215e-17
  -3.33256208e-16  6.59996573e-17  1.67449990e-14  1.61121093e-16
   2.79810618e-16]]
截距： [-7.34495773e-17]
训练结果用决策函数计算测试集的分类score值是：
 [0.63402895 0.51100628 0.46304664 ... 0.14683536 0.04383206 0.04146938]
对测试集预测结果，用概率表示：
 [[0.34659755 0.65340245]
 [0.37495766 0.62504234]
 [0.38626333 0.61373667]
 ...
 [0.46335697 0.53664303]
 [0.48904374 0.51095626]
 [0.48963414 0.51036586]]
对测试集预测结果：
 [1 1 1 ... 1 1 1]
测试集预测结果中，舞弊样本的索引号： (array([], dtype=int64),)
预测测试集中多少舞弊样本： 0
第0次特征筛选，AUC值是： 0.5
X_train_iter_lg.shape is: (1257, 29)
X_test_iter_lg.shape is: (1257, 29)

第1次特征筛选：

R值，即准确率是： 0.9920382165605095
参数： [[-2.03213230e-16  6.76838587e-17  3.50660744e-18  5.28404801e-18
   2.38926333e-18  1.70635782e-17 -7.87191586e-17 -4.22540598e-17
  -1.18605367e-15  4.30985235e-17 -2.89865627e-14 -4.16543603e-17
  -1.27580511e-16  1.40824450e-17  1.15945078e-17 -1.70226810e-16
   3.85953638e-17  4.88761672e-10 -2.12277807e-10 -3.01744799e-17
  -5.05665447e-17 -3.45748875e-17 -1.91307534e-17 -1.20871215e-17
  -3.33256208e-16  6.59996573e-17  1.67449990e-14  1.61121093e-16]]
截距： [-7.34495773e-17]
训练结果用决策函数计算测试集的分类score值是：
 [0.63402895 0.51100628 0.46304664 ... 0.14683536 0.04383206 0.04146938]
对测试集预测结果，用概率表示：
 [[0.34659755 0.65340245]
 [0.37495766 0.62504234]
 [0.38626333 0.61373667]
 ...
 [0.46335697 0.53664303]
 [0.48904374 0.51095626]
 [0.48963414 0.51036586]]
对测试集预测结果：
 [1 1 1 ... 1 1 1]
测试集预测结果中，舞弊样本的索引号： (array([], dtype=int64),)
预测测试集中多少舞弊样本： 0
第1次特征筛选，AUC值是： 0.5
X_train_iter_lg.shape is: (1257, 28)
X_test_iter_lg.shape is: (1257, 28)

第2次特征筛选：

R值，即准确率是： 0.9920382165605095
参数： [[-2.03213230e-16  6.76838587e-17  3.50660744e-18  5.28404801e-18
   2.38926333e-18  1.70635782e-17 -7.87191586e-17 -4.22540598e-17
  -1.18605367e-15  4.30985235e-17 -2.89865627e-14 -4.16543603e-17
  -1.27580511e-16  1.40824450e-17  1.15945078e-17 -1.70226810e-16
   3.85953638e-17  4.88761672e-10 -2.12277807e-10 -3.01744799e-17
  -5.05665447e-17 -3.45748875e-17 -1.91307534e-17 -1.20871215e-17
  -3.33256208e-16  6.59996573e-17  1.67449990e-14]]
截距： [-7.34495773e-17]
训练结果用决策函数计算测试集的分类score值是：
 [0.63402895 0.51100628 0.46304664 ... 0.14683536 0.04383206 0.04146938]
对测试集预测结果，用概率表示：
 [[0.34659755 0.65340245]
 [0.37495766 0.62504234]
 [0.38626333 0.61373667]
 ...
 [0.46335697 0.53664303]
 [0.48904374 0.51095626]
 [0.48963414 0.51036586]]
对测试集预测结果：
 [1 1 1 ... 1 1 1]
测试集预测结果中，舞弊样本的索引号： (array([], dtype=int64),)
预测测试集中多少舞弊样本： 0
第2次特征筛选，AUC值是： 0.5
X_train_iter_lg.shape is: (1257, 27)
X_test_iter_lg.shape is: (1257, 27)

第3次特征筛选：

R值，即准确率是： 0.9920382165605095
参数： [[-2.03213211e-16  6.76838487e-17  3.50660716e-18  5.28404733e-18
   2.38926291e-18  1.70635809e-17 -7.87191483e-17 -4.22540562e-17
  -1.18605343e-15  4.30985205e-17 -2.89865569e-14 -4.16543567e-17
  -1.27580509e-16  1.40824435e-17  1.15945074e-17 -1.70226801e-16
   3.85953608e-17  4.88761672e-10 -2.12277806e-10 -3.01744743e-17
  -5.05665392e-17 -3.45748797e-17 -1.91307506e-17 -1.20871510e-17
  -3.33256185e-16  6.59996338e-17]]
截距： [-7.34495686e-17]
训练结果用决策函数计算测试集的分类score值是：
 [0.63402895 0.51100628 0.46304664 ... 0.14683536 0.04383206 0.04146938]
对测试集预测结果，用概率表示：
 [[0.34659755 0.65340245]
 [0.37495766 0.62504234]
 [0.38626333 0.61373667]
 ...
 [0.46335697 0.53664303]
 [0.48904374 0.51095626]
 [0.48963414 0.51036586]]
对测试集预测结果：
 [1 1 1 ... 1 1 1]
测试集预测结果中，舞弊样本的索引号： (array([], dtype=int64),)
预测测试集中多少舞弊样本： 0
第3次特征筛选，AUC值是： 0.5
X_train_iter_lg.shape is: (1257, 26)
X_test_iter_lg.shape is: (1257, 26)

第4次特征筛选：

R值，即准确率是： 0.9920382165605095
参数： [[-2.03213211e-16  6.76838487e-17  3.50660716e-18  5.28404733e-18
   2.38926291e-18  1.70635809e-17 -7.87191483e-17 -4.22540562e-17
  -1.18605343e-15  4.30985205e-17 -2.89865569e-14 -4.16543567e-17
  -1.27580509e-16  1.40824435e-17  1.15945074e-17 -1.70226801e-16
   3.85953608e-17  4.88761672e-10 -2.12277806e-10 -3.01744743e-17
  -5.05665392e-17 -3.45748797e-17 -1.91307506e-17 -1.20871510e-17
  -3.33256185e-16]]
截距： [-7.34495686e-17]
训练结果用决策函数计算测试集的分类score值是：
 [0.63402895 0.51100628 0.46304664 ... 0.14683536 0.04383206 0.04146938]
对测试集预测结果，用概率表示：
 [[0.34659755 0.65340245]
 [0.37495766 0.62504234]
 [0.38626333 0.61373667]
 ...
 [0.46335697 0.53664303]
 [0.48904374 0.51095626]
 [0.48963414 0.51036586]]
对测试集预测结果：
 [1 1 1 ... 1 1 1]
测试集预测结果中，舞弊样本的索引号： (array([], dtype=int64),)
预测测试集中多少舞弊样本： 0
第4次特征筛选，AUC值是： 0.5
X_train_iter_lg.shape is: (1257, 25)
X_test_iter_lg.shape is: (1257, 25)

第5次特征筛选：

R值，即准确率是： 0.9920382165605095
参数： [[-2.03213211e-16  6.76838487e-17  3.50660716e-18  5.28404733e-18
   2.38926291e-18  1.70635809e-17 -7.87191483e-17 -4.22540562e-17
  -1.18605343e-15  4.30985205e-17 -2.89865569e-14 -4.16543567e-17
  -1.27580509e-16  1.40824435e-17  1.15945074e-17 -1.70226801e-16
   3.85953608e-17  4.88761672e-10 -2.12277806e-10 -3.01744743e-17
  -5.05665392e-17 -3.45748797e-17 -1.91307506e-17 -1.20871510e-17]]
截距： [-7.34495685e-17]
训练结果用决策函数计算测试集的分类score值是：
 [0.63402895 0.51100628 0.46304664 ... 0.14683536 0.04383206 0.04146938]
对测试集预测结果，用概率表示：
 [[0.34659755 0.65340245]
 [0.37495766 0.62504234]
 [0.38626333 0.61373667]
 ...
 [0.46335697 0.53664303]
 [0.48904374 0.51095626]
 [0.48963414 0.51036586]]
对测试集预测结果：
 [1 1 1 ... 1 1 1]
测试集预测结果中，舞弊样本的索引号： (array([], dtype=int64),)
预测测试集中多少舞弊样本： 0
第5次特征筛选，AUC值是： 0.5
X_train_iter_lg.shape is: (1257, 24)
X_test_iter_lg.shape is: (1257, 24)

第6次特征筛选：

R值，即准确率是： 0.9920382165605095
参数： [[-2.03213211e-16  6.76838487e-17  3.50660716e-18  5.28404733e-18
   2.38926291e-18  1.70635809e-17 -7.87191483e-17 -4.22540562e-17
  -1.18605343e-15  4.30985205e-17 -2.89865569e-14 -4.16543567e-17
  -1.27580509e-16  1.40824435e-17  1.15945074e-17 -1.70226801e-16
   3.85953608e-17  4.88761672e-10 -2.12277806e-10 -3.01744743e-17
  -5.05665392e-17 -3.45748797e-17 -1.91307506e-17]]
截距： [-7.34495685e-17]
训练结果用决策函数计算测试集的分类score值是：
 [0.63402895 0.51100628 0.46304664 ... 0.14683536 0.04383206 0.04146938]
对测试集预测结果，用概率表示：
 [[0.34659755 0.65340245]
 [0.37495766 0.62504234]
 [0.38626333 0.61373667]
 ...
 [0.46335697 0.53664303]
 [0.48904374 0.51095626]
 [0.48963414 0.51036586]]
对测试集预测结果：
 [1 1 1 ... 1 1 1]
测试集预测结果中，舞弊样本的索引号： (array([], dtype=int64),)
预测测试集中多少舞弊样本： 0
第6次特征筛选，AUC值是： 0.5
X_train_iter_lg.shape is: (1257, 23)
X_test_iter_lg.shape is: (1257, 23)

第7次特征筛选：

R值，即准确率是： 0.9920382165605095
参数： [[-2.03213211e-16  6.76838487e-17  3.50660716e-18  5.28404733e-18
   2.38926291e-18  1.70635809e-17 -7.87191483e-17 -4.22540562e-17
  -1.18605343e-15  4.30985205e-17 -2.89865569e-14 -4.16543567e-17
  -1.27580509e-16  1.40824435e-17  1.15945074e-17 -1.70226801e-16
   3.85953608e-17  4.88761672e-10 -2.12277806e-10 -3.01744743e-17
  -5.05665392e-17 -3.45748797e-17]]
截距： [-7.34495685e-17]
训练结果用决策函数计算测试集的分类score值是：
 [0.63402895 0.51100628 0.46304664 ... 0.14683536 0.04383206 0.04146938]
对测试集预测结果，用概率表示：
 [[0.34659755 0.65340245]
 [0.37495766 0.62504234]
 [0.38626333 0.61373667]
 ...
 [0.46335697 0.53664303]
 [0.48904374 0.51095626]
 [0.48963414 0.51036586]]
对测试集预测结果：
 [1 1 1 ... 1 1 1]
测试集预测结果中，舞弊样本的索引号： (array([], dtype=int64),)
预测测试集中多少舞弊样本： 0
第7次特征筛选，AUC值是： 0.5
X_train_iter_lg.shape is: (1257, 22)
X_test_iter_lg.shape is: (1257, 22)

第8次特征筛选：

R值，即准确率是： 0.9920382165605095
参数： [[-2.03213211e-16  6.76838487e-17  3.50660716e-18  5.28404733e-18
   2.38926291e-18  1.70635809e-17 -7.87191483e-17 -4.22540562e-17
  -1.18605343e-15  4.30985205e-17 -2.89865569e-14 -4.16543567e-17
  -1.27580509e-16  1.40824435e-17  1.15945074e-17 -1.70226801e-16
   3.85953608e-17  4.88761672e-10 -2.12277806e-10 -3.01744743e-17
  -5.05665392e-17]]
截距： [-7.34495685e-17]
训练结果用决策函数计算测试集的分类score值是：
 [0.63402895 0.51100628 0.46304664 ... 0.14683536 0.04383206 0.04146938]
对测试集预测结果，用概率表示：
 [[0.34659755 0.65340245]
 [0.37495766 0.62504234]
 [0.38626333 0.61373667]
 ...
 [0.46335697 0.53664303]
 [0.48904374 0.51095626]
 [0.48963414 0.51036586]]
对测试集预测结果：
 [1 1 1 ... 1 1 1]
测试集预测结果中，舞弊样本的索引号： (array([], dtype=int64),)
预测测试集中多少舞弊样本： 0
第8次特征筛选，AUC值是： 0.5
X_train_iter_lg.shape is: (1257, 21)
X_test_iter_lg.shape is: (1257, 21)

第9次特征筛选：

R值，即准确率是： 0.9920382165605095
参数： [[-2.03213211e-16  6.76838487e-17  3.50660716e-18  5.28404733e-18
   2.38926291e-18  1.70635809e-17 -7.87191483e-17 -4.22540562e-17
  -1.18605343e-15  4.30985205e-17 -2.89865569e-14 -4.16543567e-17
  -1.27580509e-16  1.40824435e-17  1.15945074e-17 -1.70226801e-16
   3.85953608e-17  4.88761672e-10 -2.12277806e-10 -3.01744743e-17]]
截距： [-7.34495685e-17]
训练结果用决策函数计算测试集的分类score值是：
 [0.63402895 0.51100628 0.46304664 ... 0.14683536 0.04383206 0.04146938]
对测试集预测结果，用概率表示：
 [[0.34659755 0.65340245]
 [0.37495766 0.62504234]
 [0.38626333 0.61373667]
 ...
 [0.46335697 0.53664303]
 [0.48904374 0.51095626]
 [0.48963414 0.51036586]]
对测试集预测结果：
 [1 1 1 ... 1 1 1]
测试集预测结果中，舞弊样本的索引号： (array([], dtype=int64),)
预测测试集中多少舞弊样本： 0
第9次特征筛选，AUC值是： 0.5
X_train_iter_lg.shape is: (1257, 20)
X_test_iter_lg.shape is: (1257, 20)

第10次特征筛选：

R值，即准确率是： 0.9920382165605095
参数： [[-2.03213211e-16  6.76838487e-17  3.50660716e-18  5.28404733e-18
   2.38926291e-18  1.70635809e-17 -7.87191483e-17 -4.22540562e-17
  -1.18605343e-15  4.30985205e-17 -2.89865569e-14 -4.16543567e-17
  -1.27580509e-16  1.40824435e-17  1.15945074e-17 -1.70226801e-16
   3.85953608e-17  4.88761672e-10 -2.12277806e-10]]
截距： [-7.34495685e-17]
训练结果用决策函数计算测试集的分类score值是：
 [0.63402895 0.51100628 0.46304664 ... 0.14683536 0.04383206 0.04146938]
对测试集预测结果，用概率表示：
 [[0.34659755 0.65340245]
 [0.37495766 0.62504234]
 [0.38626333 0.61373667]
 ...
 [0.46335697 0.53664303]
 [0.48904374 0.51095626]
 [0.48963414 0.51036586]]
对测试集预测结果：
 [1 1 1 ... 1 1 1]
测试集预测结果中，舞弊样本的索引号： (array([], dtype=int64),)
预测测试集中多少舞弊样本： 0
第10次特征筛选，AUC值是： 0.5
X_train_iter_lg.shape is: (1257, 19)
X_test_iter_lg.shape is: (1257, 19)

第11次特征筛选：

R值，即准确率是： 0.9920382165605095
参数： [[-1.63181291e-17  5.41474443e-18  4.40610503e-19  5.78716077e-19
   3.26803782e-19  1.08472473e-18 -7.29330105e-18 -5.29079810e-18
  -1.33945114e-16  4.15486942e-18 -3.29584448e-15 -5.56546362e-18
  -5.72889209e-18  1.40755425e-18  1.36714561e-18 -1.20572027e-17
   3.57334169e-18  3.18882119e-10]]
截距： [-6.7029432e-18]
训练结果用决策函数计算测试集的分类score值是：
 [0.53405393 0.53405393 0.53405393 ... 0.16935227 0.04104908 0.03177271]
对测试集预测结果，用概率表示：
 [[0.36957187 0.63042813]
 [0.36957187 0.63042813]
 [0.36957187 0.63042813]
 ...
 [0.45776283 0.54223717]
 [0.48973917 0.51026083]
 [0.49205749 0.50794251]]
对测试集预测结果：
 [1 1 1 ... 1 1 1]
测试集预测结果中，舞弊样本的索引号： (array([], dtype=int64),)
预测测试集中多少舞弊样本： 0
第11次特征筛选，AUC值是： 0.5
X_train_iter_lg.shape is: (1257, 18)
X_test_iter_lg.shape is: (1257, 18)

第12次特征筛选：

R值，即准确率是： 0.9952229299363057
参数： [[  2.90678275   6.07572766  -6.6267034    4.13107283  -0.66780832
   10.66195231 -17.88158946  -6.42733409   2.90437635  11.28403982
   -0.26464584  -3.00052277   1.2720182   13.79337845  27.44652778
   -2.67505526  36.16381365]]
截距： [-1.25913029]
训练结果用决策函数计算测试集的分类score值是：
 [  100.21992773    32.03247482    26.6574223  ...    52.80826386
 -1767.9021739      4.65940144]
对测试集预测结果，用概率表示：
 [[0.00000000e+00 1.00000000e+00]
 [1.22124533e-14 1.00000000e+00]
 [2.64743782e-12 1.00000000e+00]
 ...
 [0.00000000e+00 1.00000000e+00]
 [1.00000000e+00 0.00000000e+00]
 [9.38325093e-03 9.90616749e-01]]
对测试集预测结果：
 [ 1  1  1 ...  1 -1  1]
测试集预测结果中，舞弊样本的索引号： (array([  93,  117,  121,  127,  142,  144,  244,  255,  332,  369,  370,
        399,  454,  486,  521,  530,  613,  618,  641,  722,  724,  790,
        792, 1163, 1222, 1246, 1247, 1251, 1252, 1253, 1255], dtype=int64),)
预测测试集中多少舞弊样本： 31
第12次特征筛选，AUC值是： 0.7626951699985407
X_train_iter_lg.shape is: (1257, 17)
X_test_iter_lg.shape is: (1257, 17)

第13次特征筛选：

R值，即准确率是： 0.9856687898089171
参数： [[ 0.69109826  2.60091091 -2.27073927  1.56427676 -0.25656371  2.02379431
  -9.09107685  3.92446716  1.82598195  6.34243129 -0.28738847 -7.50652684
   0.34966168  4.42949548 16.02301754 -0.44771323]]
截距： [4.92756848]
训练结果用决策函数计算测试集的分类score值是：
 [  56.22856572   15.39224594   12.65468487 ...   11.82417606 -634.96112975
    5.25309847]
对测试集预测结果，用概率表示：
 [[0.00000000e+000 1.00000000e+000]
 [2.06648584e-007 9.99999793e-001]
 [3.19255807e-006 9.99996807e-001]
 ...
 [7.32524919e-006 9.99992675e-001]
 [1.00000000e+000 1.73734123e-276]
 [5.20406034e-003 9.94795940e-001]]
对测试集预测结果：
 [ 1  1  1 ...  1 -1  1]
测试集预测结果中，舞弊样本的索引号： (array([  93,  117,  121,  142,  144,  255,  262,  269,  279,  332,  370,
        399,  454,  520,  521,  540,  549,  615,  618,  641,  658,  722,
        787,  790,  792,  819, 1033, 1246, 1247, 1248, 1249, 1251, 1252,
       1253, 1255], dtype=int64),)
预测测试集中多少舞弊样本： 35
第13次特征筛选，AUC值是： 0.8528016926893331
X_train_iter_lg.shape is: (1257, 16)
X_test_iter_lg.shape is: (1257, 16)

第14次特征筛选：

R值，即准确率是： 0.9920382165605095
参数： [[  1.10966347   3.36182535  -1.26857313   3.67682606   0.9160366
    2.70024122 -11.06080946   5.37871744   2.31485101   7.60230724
   -0.35489103  -9.57055504   0.15280969   6.0489781   21.06954161]]
截距： [4.36023276]
训练结果用决策函数计算测试集的分类score值是：
 [  73.90184743   20.21317564   16.62460994 ...   14.30727592 -843.6865802
    4.87124114]
对测试集预测结果，用概率表示：
 [[0.00000000e+00 1.00000000e+00]
 [1.66544134e-09 9.99999998e-01]
 [6.02592350e-08 9.99999940e-01]
 ...
 [6.11545465e-07 9.99999388e-01]
 [1.00000000e+00 0.00000000e+00]
 [7.60555960e-03 9.92394440e-01]]
对测试集预测结果：
 [ 1  1  1 ...  1 -1  1]
测试集预测结果中，舞弊样本的索引号： (array([  93,  117,  121,  142,  144,  255,  262,  269,  332,  370,  399,
        454,  520,  521,  540,  549,  615,  618,  641,  658,  722,  792,
        819, 1246, 1247, 1248, 1249, 1251, 1252, 1253, 1255], dtype=int64),)
预测测试集中多少舞弊样本： 31
第14次特征筛选，AUC值是： 0.8544068291259302
X_train_iter_lg.shape is: (1257, 15)
X_test_iter_lg.shape is: (1257, 15)

第15次特征筛选：

R值，即准确率是： 0.9585987261146497
参数： [[ -0.94255943   8.20555273  -1.99850615  14.5408816   -6.90213072
    3.98868188 -19.5888829   11.98975571   3.57418258  18.60886652
   -0.197935   -20.41689196   0.08116072  28.17375381]]
截距： [6.53345548]
训练结果用决策函数计算测试集的分类score值是：
 [ 1.71530782e+01  2.17746121e+01  1.95315159e+01 ... -3.85021911e-01
 -3.23083296e+03 -1.47095243e+01]
对测试集预测结果，用概率表示：
 [[3.55232580e-08 9.99999964e-01]
 [3.49466900e-10 1.00000000e+00]
 [3.29283822e-09 9.99999997e-01]
 ...
 [5.95083754e-01 4.04916246e-01]
 [1.00000000e+00 0.00000000e+00]
 [9.99999591e-01 4.09010632e-07]]
对测试集预测结果：
 [ 1  1  1 ... -1 -1 -1]
测试集预测结果中，舞弊样本的索引号： (array([  10,   11,   14,   90,   93,  107,  114,  121,  142,  238,  254,
        276,  277,  283,  324,  330,  331,  332,  370,  399,  518,  520,
        521,  544,  545,  549,  568,  583,  635,  641,  675,  693,  694,
        695,  697,  792,  819,  906,  976, 1033, 1041, 1068, 1071, 1089,
       1222, 1234, 1246, 1247, 1251, 1252, 1253, 1254, 1255, 1256],
      dtype=int64),)
预测测试集中多少舞弊样本： 54
第15次特征筛选，AUC值是： 0.845177294615497
X_train_iter_lg.shape is: (1257, 14)
X_test_iter_lg.shape is: (1257, 14)

第16次特征筛选：

R值，即准确率是： 0.9777070063694268
参数： [[ 6.90296445e-04 -4.30248401e-04  1.32115705e-04  9.71223791e-05
   7.72296728e-05  1.00015708e-03 -9.03539454e-06 -1.07993055e-03
   4.42515211e-04  8.82194577e-04 -3.75018958e-02 -1.48433617e-03
   1.16165252e-03]]
截距： [1.11912012]
训练结果用决策函数计算测试集的分类score值是：
 [0.90505535 0.96957078 0.95774776 ... 1.00610027 0.87704741 0.98531182]
对测试集预测结果，用概率表示：
 [[0.28801273 0.71198727]
 [0.27496606 0.72503394]
 [0.27732936 0.72267064]
 ...
 [0.26774373 0.73225627]
 [0.29379    0.70621   ]
 [0.27183907 0.72816093]]
对测试集预测结果：
 [1 1 1 ... 1 1 1]
测试集预测结果中，舞弊样本的索引号： (array([  67,   90,   98,   99,  114,  143,  144,  270,  277,  331,  332,
        348,  370,  399,  469,  520,  521,  540,  549,  615,  641,  657,
        658,  792,  793,  794,  819,  906, 1032, 1033, 1152, 1246, 1247,
       1248], dtype=int64),)
预测测试集中多少舞弊样本： 34
第16次特征筛选，AUC值是： 0.6239238289800088
X_train_iter_lg.shape is: (1257, 13)
X_test_iter_lg.shape is: (1257, 13)

第17次特征筛选：

R值，即准确率是： 0.9617834394904459
参数： [[ -0.58295976   6.18979894   1.16050985  41.27631055   4.28454072
    2.22066831 -23.46605223  11.43346698   3.35590023  18.56128559
   -0.18461593 -18.24071324]]
截距： [8.24100795]
训练结果用决策函数计算测试集的分类score值是：
 [  15.18533607   19.40327627   17.22909151 ...   14.02310121 -495.16622786
   -6.018963  ]
对测试集预测结果，用概率表示：
 [[2.54151237e-007 9.99999746e-001]
 [3.74338227e-009 9.99999996e-001]
 [3.29230918e-008 9.99999967e-001]
 ...
 [8.12538920e-007 9.99999187e-001]
 [1.00000000e+000 8.95446440e-216]
 [9.97573711e-001 2.42628928e-003]]
对测试集预测结果：
 [ 1  1  1 ...  1 -1 -1]
测试集预测结果中，舞弊样本的索引号： (array([  10,   12,   14,   90,   91,  107,  114,  121,  142,  238,  242,
        276,  277,  279,  280,  324,  330,  331,  332,  370,  399,  518,
        519,  520,  521,  544,  545,  549,  635,  641,  674,  675,  694,
        695,  696,  697,  736,  792,  794,  819,  906,  976, 1068, 1071,
       1089, 1137, 1234, 1246, 1247, 1251, 1252, 1253, 1255, 1256],
      dtype=int64),)
预测测试集中多少舞弊样本： 54
第17次特征筛选，AUC值是： 0.7993214650518022
X_train_iter_lg.shape is: (1257, 12)
X_test_iter_lg.shape is: (1257, 12)

第18次特征筛选：

R值，即准确率是： 0.9665605095541401
参数： [[ -0.78079273  -2.78450005  -4.91453911  61.33340993  -5.89367459
   -8.31836124 -63.44756969  -2.75846651   4.86560041  43.11324444
   -0.21361642]]
截距： [41.24317585]
训练结果用决策函数计算测试集的分类score值是：
 [   24.03189053    30.9432141     27.13021845 ...    17.01761274
 -1262.79395149    -8.79976428]
对测试集预测结果，用概率表示：
 [[3.65665276e-11 1.00000000e+00]
 [3.64153152e-14 1.00000000e+00]
 [1.65001346e-12 1.00000000e+00]
 ...
 [4.06766026e-08 9.99999959e-01]
 [1.00000000e+00 0.00000000e+00]
 [9.99849254e-01 1.50745882e-04]]
对测试集预测结果：
 [ 1  1  1 ...  1 -1 -1]
测试集预测结果中，舞弊样本的索引号： (array([  11,   90,  107,  114,  121,  144,  146,  238,  276,  277,  279,
        280,  324,  331,  332,  347,  348,  349,  370,  404,  481,  518,
        519,  520,  521,  540,  544,  545,  546,  549,  583,  614,  615,
        641,  657,  666,  674,  675,  694,  695,  696,  697,  722,  723,
        736,  792,  793,  794,  819,  906,  976, 1033, 1050, 1089, 1160,
       1197, 1207, 1234, 1247, 1251, 1252, 1253, 1255, 1256], dtype=int64),)
预测测试集中多少舞弊样本： 64
第18次特征筛选，AUC值是： 0.7494527943966145
X_train_iter_lg.shape is: (1257, 11)
X_test_iter_lg.shape is: (1257, 11)

第19次特征筛选：

R值，即准确率是： 0.8805732484076433
参数： [[  1.1524155    0.6056264    0.31596518 160.47661429 -56.75168263
   -3.58384784 -20.22926331  -1.62450753   0.61208183  19.02000326]]
截距： [6.69458062]
训练结果用决策函数计算测试集的分类score值是：
 [  16.05360088   17.97075293   17.08168156 ...  -10.92759498 -483.64929177
  -26.47762726]
对测试集预测结果，用概率表示：
 [[1.06661989e-007 9.99999893e-001]
 [1.56819895e-008 9.99999984e-001]
 [3.81522310e-008 9.99999962e-001]
 ...
 [9.99982044e-001 1.79555207e-005]
 [1.00000000e+000 8.99044955e-211]
 [1.00000000e+000 3.16892957e-012]]
对测试集预测结果：
 [ 1  1  1 ... -1 -1 -1]
测试集预测结果中，舞弊样本的索引号： (array([  10,   21,   35,   36,   37,   38,   39,   40,   58,   61,   62,
         67,   73,   74,   75,   90,  107,  111,  112,  113,  114,  115,
        117,  121,  143,  144,  145,  146,  158,  163,  165,  181,  182,
        183,  185,  195,  196,  200,  201,  214,  215,  233,  236,  246,
        247,  262,  264,  269,  276,  281,  288,  300,  319,  324,  330,
        331,  332,  338,  347,  349,  358,  359,  366,  368,  369,  370,
        403,  404,  429,  430,  431,  432,  433,  442,  463,  468,  469,
        471,  477,  481,  503,  516,  543,  544,  545,  555,  565,  567,
        611,  613,  614,  615,  616,  624,  630,  635,  641,  661,  665,
        666,  667,  675,  687,  696,  697,  722,  723,  735,  736,  742,
        777,  778,  787,  788,  790,  794,  795,  797,  883,  884,  885,
        951,  954,  955,  965,  966,  967, 1012, 1014, 1017, 1050, 1051,
       1061, 1071, 1089, 1094, 1095, 1096, 1097, 1098, 1129, 1148, 1160,
       1222, 1230, 1234, 1241, 1242, 1243, 1246, 1247, 1248, 1249, 1251,
       1252, 1253, 1254, 1255, 1256], dtype=int64),)
预测测试集中多少舞弊样本： 159
第19次特征筛选，AUC值是： 0.8947541222822121
X_train_iter_lg.shape is: (1257, 10)
X_test_iter_lg.shape is: (1257, 10)

第20次特征筛选：

R值，即准确率是： 0.8742038216560509
参数： [[   0.60152679   -0.52057869    0.85646571  196.98385641 -111.05405471
     1.41935943  -19.0220167    -1.47478524    0.3022195 ]]
截距： [11.55344688]
训练结果用决策函数计算测试集的分类score值是：
 [  10.65900105    9.61027701   10.43659687 ...  -10.8983353  -442.72882198
  -23.33268934]
对测试集预测结果，用概率表示：
 [[2.34879148e-005 9.99976512e-001]
 [6.70317583e-005 9.99932968e-001]
 [2.93380217e-005 9.99970662e-001]
 ...
 [9.99981511e-001 1.84886452e-005]
 [1.00000000e+000 5.31270419e-193]
 [1.00000000e+000 7.35769483e-011]]
对测试集预测结果：
 [ 1  1  1 ... -1 -1 -1]
测试集预测结果中，舞弊样本的索引号： (array([  10,   21,   22,   30,   32,   34,   35,   36,   37,   38,   39,
         40,   57,   58,   61,   62,   65,   67,   73,   74,   75,   90,
        107,  108,  111,  113,  115,  117,  121,  123,  126,  143,  144,
        145,  158,  163,  165,  181,  182,  183,  185,  186,  195,  196,
        197,  200,  201,  214,  216,  233,  236,  243,  244,  245,  246,
        247,  264,  276,  281,  283,  288,  299,  300,  301,  319,  330,
        338,  339,  340,  349,  358,  366,  368,  369,  370,  429,  430,
        431,  432,  433,  442,  449,  457,  463,  469,  471,  477,  481,
        492,  493,  503,  516,  517,  529,  544,  555,  567,  583,  590,
        599,  600,  601,  611,  612,  613,  614,  615,  618,  624,  630,
        635,  641,  651,  657,  658,  661,  664,  665,  666,  667,  674,
        675,  687,  696,  697,  712,  718,  722,  723,  736,  742,  775,
        777,  778,  792,  793,  794,  795,  797,  883,  884,  885,  910,
        911,  947,  948,  949,  950,  951,  954,  955,  956,  965,  966,
        967, 1012, 1014, 1037, 1071, 1074, 1089, 1094, 1095, 1096, 1097,
       1098, 1148, 1188, 1222, 1230, 1231, 1234, 1239, 1241, 1246, 1247,
       1248, 1249, 1251, 1252, 1253, 1254, 1255, 1256], dtype=int64),)
预测测试集中多少舞弊样本： 184
第20次特征筛选，AUC值是： 0.8847220195534802
X_train_iter_lg.shape is: (1257, 9)
X_test_iter_lg.shape is: (1257, 9)

第21次特征筛选：

R值，即准确率是： 0.8821656050955414
参数： [[   0.77287046    0.41987355    0.69778946  264.8291387  -207.40292127
     2.45240204   -8.67664032   -2.20804846]]
截距： [7.02374223]
训练结果用决策函数计算测试集的分类score值是：
 [   9.64060381    9.06812659    9.53831334 ...   -7.93185357 -217.49845298
  -20.59143137]
对测试集预测结果，用概率表示：
 [[6.50295459e-05 9.99934970e-01]
 [1.15269020e-04 9.99884731e-01]
 [7.20330599e-05 9.99927967e-01]
 ...
 [9.99641009e-01 3.58991219e-04]
 [1.00000000e+00 3.48034301e-95]
 [9.99999999e-01 1.14091944e-09]]
对测试集预测结果：
 [ 1  1  1 ... -1 -1 -1]
测试集预测结果中，舞弊样本的索引号： (array([  10,   20,   21,   22,   34,   35,   36,   37,   38,   39,   40,
         57,   58,   59,   61,   62,   73,   74,   75,   90,  107,  108,
        109,  110,  111,  121,  126,  142,  144,  145,  146,  158,  162,
        163,  165,  181,  182,  183,  185,  186,  195,  196,  197,  200,
        229,  233,  236,  243,  244,  246,  247,  264,  276,  278,  281,
        283,  288,  300,  301,  309,  319,  320,  330,  338,  339,  349,
        358,  366,  368,  369,  370,  429,  430,  431,  432,  433,  442,
        449,  455,  457,  463,  469,  471,  477,  483,  484,  485,  492,
        493,  500,  503,  516,  518,  529,  544,  555,  563,  584,  599,
        602,  611,  612,  613,  615,  617,  624,  629,  630,  635,  651,
        653,  657,  658,  659,  660,  661,  665,  666,  667,  674,  675,
        696,  697,  712,  722,  723,  736,  742,  777,  792,  793,  795,
        797,  829,  849,  910,  911,  931,  947,  948,  949,  950,  951,
        954,  955,  956,  965,  966,  970,  971, 1071, 1074, 1094, 1095,
       1096, 1097, 1098, 1156, 1239, 1246, 1247, 1248, 1249, 1251, 1252,
       1253, 1254, 1255, 1256], dtype=int64),)
预测测试集中多少舞弊样本： 169
第21次特征筛选，AUC值是： 0.8907412811907194
X_train_iter_lg.shape is: (1257, 8)
X_test_iter_lg.shape is: (1257, 8)

第22次特征筛选：

R值，即准确率是： 0.8805732484076433
参数： [[   0.91268663   -0.70857842    1.27340634  216.87273121 -144.71342152
     2.29058762   -7.17032919]]
截距： [2.2626857]
训练结果用决策函数计算测试集的分类score值是：
 [   7.75050062    8.14741097    8.20684924 ...   -9.19938324 -201.72689448
  -21.11857446]
对测试集预测结果，用概率表示：
 [[4.30341684e-04 9.99569658e-01]
 [2.89400097e-04 9.99710600e-01]
 [2.72704441e-04 9.99727296e-01]
 ...
 [9.99898908e-01 1.01091517e-04]
 [1.00000000e+00 2.46106383e-88]
 [9.99999999e-01 6.73472158e-10]]
对测试集预测结果：
 [ 1  1  1 ... -1 -1 -1]
测试集预测结果中，舞弊样本的索引号： (array([  10,   12,   20,   21,   22,   34,   35,   36,   37,   38,   39,
         40,   57,   58,   59,   62,   67,   73,   74,   75,   90,   93,
        107,  108,  109,  110,  111,  115,  117,  121,  144,  145,  158,
        162,  163,  165,  177,  182,  183,  185,  186,  195,  196,  199,
        200,  201,  202,  216,  232,  233,  236,  243,  244,  246,  247,
        264,  270,  276,  277,  278,  281,  283,  288,  299,  300,  301,
        309,  330,  333,  338,  340,  349,  351,  358,  359,  366,  368,
        369,  370,  429,  430,  431,  432,  433,  434,  442,  454,  455,
        456,  457,  463,  469,  471,  477,  483,  484,  485,  492,  493,
        500,  501,  502,  503,  516,  544,  555,  558,  563,  584,  599,
        611,  613,  614,  615,  616,  617,  618,  624,  629,  630,  635,
        651,  657,  658,  661,  665,  666,  667,  675,  687,  712,  722,
        723,  736,  742,  775,  777,  792,  793,  795,  797,  815,  829,
        849,  910,  911,  947,  951,  953,  954,  955,  956,  966,  970,
        971, 1017, 1037, 1061, 1071, 1074, 1089, 1094, 1095, 1096, 1097,
       1098, 1148, 1156, 1222, 1234, 1239, 1246, 1247, 1248, 1249, 1251,
       1252, 1253, 1254, 1255, 1256], dtype=int64),)
预测测试集中多少舞弊样本： 181
第22次特征筛选，AUC值是： 0.8859258718809281
X_train_iter_lg.shape is: (1257, 7)
X_test_iter_lg.shape is: (1257, 7)

第23次特征筛选：

R值，即准确率是： 0.8710191082802548
参数： [[   0.98587444    0.32231535    1.41598978  221.50234783 -148.61203776
     2.22240724]]
截距： [-3.36423593]
训练结果用决策函数计算测试集的分类score值是：
 [  6.87684649   7.2300183    7.30282292 ...  -8.69396149 -63.62728446
 -19.25029245]
对测试集预测结果，用概率表示：
 [[1.03032875e-03 9.98969671e-01]
 [7.23983073e-04 9.99276017e-01]
 [6.73180993e-04 9.99326819e-01]
 ...
 [9.99832433e-01 1.67566701e-04]
 [1.00000000e+00 2.32820630e-28]
 [9.99999996e-01 4.36218633e-09]]
对测试集预测结果：
 [ 1  1  1 ... -1 -1 -1]
测试集预测结果中，舞弊样本的索引号： (array([  10,   11,   12,   20,   21,   22,   24,   34,   35,   36,   37,
         38,   39,   40,   57,   58,   59,   62,   67,   73,   74,   75,
         90,   93,  107,  108,  109,  110,  115,  117,  121,  144,  145,
        158,  162,  163,  165,  177,  181,  182,  183,  185,  186,  195,
        196,  199,  200,  201,  202,  214,  231,  232,  233,  236,  243,
        244,  246,  247,  264,  269,  276,  281,  283,  287,  288,  299,
        300,  301,  309,  330,  333,  334,  338,  340,  349,  351,  358,
        359,  366,  368,  369,  370,  411,  429,  430,  431,  432,  433,
        434,  442,  454,  455,  456,  457,  463,  469,  471,  477,  492,
        493,  500,  501,  502,  503,  509,  516,  532,  544,  555,  558,
        584,  599,  611,  613,  615,  616,  617,  623,  624,  629,  630,
        635,  641,  651,  653,  658,  659,  660,  661,  665,  666,  667,
        675,  712,  718,  722,  723,  736,  742,  756,  775,  777,  787,
        792,  793,  795,  797,  815,  816,  829,  849,  876,  910,  911,
        931,  947,  951,  953,  954,  955,  956,  966,  970,  971, 1017,
       1037, 1061, 1071, 1074, 1089, 1094, 1095, 1096, 1097, 1098, 1137,
       1148, 1154, 1156, 1220, 1222, 1239, 1244, 1246, 1247, 1248, 1249,
       1251, 1252, 1254, 1255, 1256], dtype=int64),)
预测测试集中多少舞弊样本： 192
第23次特征筛选，AUC值是： 0.8356559171165913
X_train_iter_lg.shape is: (1257, 6)
X_test_iter_lg.shape is: (1257, 6)

第24次特征筛选：

R值，即准确率是： 0.8550955414012739
参数： [[   1.07335221    0.50095129    1.33200667  236.08507457 -156.65038928]]
截距： [-2.72710102]
训练结果用决策函数计算测试集的分类score值是：
 [  6.89628971   6.031193     6.66133615 ...  -9.33872273 -68.43682313
 -20.05316857]
对测试集预测结果，用概率表示：
 [[1.01050938e-03 9.98989491e-01]
 [2.39686717e-03 9.97603133e-01]
 [1.27780084e-03 9.98722199e-01]
 ...
 [9.99912056e-01 8.79439679e-05]
 [1.00000000e+00 1.89786517e-30]
 [9.99999998e-01 1.95442741e-09]]
对测试集预测结果：
 [ 1  1  1 ... -1 -1 -1]
测试集预测结果中，舞弊样本的索引号： (array([  10,   11,   12,   20,   21,   22,   24,   34,   35,   36,   37,
         38,   39,   40,   57,   58,   59,   62,   65,   67,   75,   90,
        107,  108,  109,  110,  115,  117,  121,  127,  144,  145,  158,
        162,  163,  165,  177,  180,  181,  182,  183,  185,  186,  195,
        199,  200,  201,  202,  231,  232,  233,  236,  243,  244,  246,
        247,  264,  269,  276,  281,  283,  286,  287,  288,  300,  301,
        309,  330,  332,  333,  334,  338,  349,  351,  358,  359,  366,
        368,  369,  370,  429,  430,  431,  432,  433,  434,  442,  449,
        454,  455,  456,  457,  463,  469,  471,  477,  492,  493,  500,
        502,  503,  509,  516,  532,  544,  555,  558,  599,  611,  612,
        613,  615,  617,  623,  624,  629,  630,  635,  641,  651,  653,
        658,  659,  660,  661,  665,  666,  667,  675,  696,  697,  712,
        718,  722,  723,  736,  742,  775,  777,  778,  787,  790,  792,
        793,  795,  797,  815,  829,  874,  876,  910,  911,  931,  947,
        951,  953,  954,  955,  956,  966,  970,  971, 1017, 1037, 1061,
       1071, 1074, 1089, 1094, 1095, 1096, 1097, 1098, 1137, 1148, 1154,
       1156, 1160, 1220, 1222, 1239, 1241, 1244, 1246, 1247, 1248, 1249,
       1251, 1252, 1254, 1255, 1256], dtype=int64),)
预测测试集中多少舞弊样本： 192
第24次特征筛选，AUC值是： 0.8356559171165913
X_train_iter_lg.shape is: (1257, 5)
X_test_iter_lg.shape is: (1257, 5)
AUC值随特征数目变化： [0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.7626951699985407, 0.8528016926893331, 0.8544068291259302, 0.845177294615497, 0.6239238289800088, 0.7993214650518022, 0.7494527943966145, 0.8947541222822121, 0.8847220195534802, 0.8907412811907194, 0.8859258718809281, 0.8356559171165913, 0.8356559171165913]
样本特征数目： [29, 28, 27, 26, 25, 24, 23, 22, 21, 20, 19, 18, 17, 16, 15, 14, 13, 12, 11, 10, 9, 8, 7, 6, 5]
In [895]:
# 通过上面试验，发现特征选8个时，分类器性能最佳。逻辑回归的sklearn库有个小问题，即不能将模型保留下来，如果在交叉验证中得到一个最优模型，
# 则如果仅仅以特征筛选的样本重新训练模型，则新得到的逻辑回归模型并不是最优的，性能往往下降。所以特征筛选后，依然需要一次
# LogisticRegressionCV，交叉验证得到最优模型。
lr_op = LogisticRegressionCV(Cs=np.logspace(-5,5,11), cv=cv_iter, class_weight=class_weight_lg, random_state=0, solver='lbfgs')

auc_lg = np.array(auc_values_lg)
best_param_op_idx_lg = np.max(np.where(auc_lg==np.max(auc_lg))[0])
print("最佳超参的索引best_param_op_idx是：", best_param_op_idx_lg)
best_feature_count_lg = feature_count_lg[best_param_op_idx_lg]  

X_train_op = np.array(X_train_lg_orgin.iloc[:,:best_feature_count_lg])
y_train_op = y_train
X_test_op = np.array(X_test_lg_orgin.iloc[:,:best_feature_count_lg])
y_test_op = y_test
print("逻辑回归模型中，测试集中标签为+1的样本数是：",X_test_op)

for train_idx, test_idx in cv_iter.split(X_train_op, y_train_op):
    X_train_cv, X_test_cv = X_train_op[train_idx], X_train_op[test_idx]
    y_train_cv, y_test_cv = y_train_op[train_idx], y_train_op[test_idx]
    lr_op.fit(X_train_cv, y_train_cv)

print("训练的最优逻辑回归模型的参数：\n", lr_op.coef_)
print("训练的最优逻辑回归模型的截距：\n", lr_op.intercept_)
y_pred_lg = lr_op.predict(X_test_op)
y_pred_lg_prob = lr_op.predict_proba(X_test_op)
print("用训练好的逻辑回归模型，预测测试集的结果是：\n", y_pred_lg)
print("测试集中预测为舞弊样本的索引号是：", np.where(y_pred_lg==-1))
print("测试集中预测为舞弊样本的个数有：", np.where(y_pred_lg==-1)[0].size)

# 计算训练模型clf_op的AUC值，评估模型质量。
auc_value_op_lg = auc(y_test_op, y_pred_lg)
print("最优逻辑回归模型得到的AUC值：", auc_value_op_lg)

# 打印逻辑回归的分类效果报告
print("用最优逻辑回归模型，得到的分类结果评估报告如下：\n", classification_report(y_test_op, y_pred_lg))
# 通过逻辑回归的调试，发现逻辑回归并不比SVC性能差很多，但是计算更加简便，而且计算快速，综合表现很不错。

最佳超参的索引best_param_op_idx是： 19
逻辑回归模型中，测试集中标签为+1的样本数是： [[ 5.00000000e+00  1.50905500e+00  1.33241000e-01 ...  7.92489000e-01
   9.04582000e-01  4.31495000e-01]
 [ 5.00000000e+00  1.55607000e+00  1.53912000e-01 ...  1.38748100e+00
   8.35262000e-01  7.39164000e-01]
 [ 5.00000000e+00  1.63892600e+00  1.23446000e-01 ...  1.18496600e+00
   7.85876000e-01  5.75827000e-01]
 ...
 [ 4.00000000e+00  2.37979000e-01 -1.20878000e-01 ...  9.75833000e-01
   6.92581600e+00  1.99014000e-01]
 [ 3.00000000e+00 -9.50609000e-01 -1.83469000e-01 ...  2.02900000e-03
   5.52993977e+00  7.30000000e-04]
 [ 1.00000000e+00 -9.16360000e-02 -4.00649000e-01 ...  2.18884000e-01
   5.52993977e+00  1.24130000e-02]]
训练的最优逻辑回归模型的参数：
 [[  1.1524155    0.6056264    0.31596518 160.47661429 -56.75168263
   -3.58384784 -20.22926331  -1.62450753   0.61208183  19.02000326]]
训练的最优逻辑回归模型的截距：
 [6.69458062]
用训练好的逻辑回归模型，预测测试集的结果是：
 [ 1  1  1 ... -1 -1 -1]
测试集中预测为舞弊样本的索引号是： (array([  10,   21,   35,   36,   37,   38,   39,   40,   58,   61,   62,
         67,   73,   74,   75,   90,  107,  111,  112,  113,  114,  115,
        117,  121,  143,  144,  145,  146,  158,  163,  165,  181,  182,
        183,  185,  195,  196,  200,  201,  214,  215,  233,  236,  246,
        247,  262,  264,  269,  276,  281,  288,  300,  319,  324,  330,
        331,  332,  338,  347,  349,  358,  359,  366,  368,  369,  370,
        403,  404,  429,  430,  431,  432,  433,  442,  463,  468,  469,
        471,  477,  481,  503,  516,  543,  544,  545,  555,  565,  567,
        611,  613,  614,  615,  616,  624,  630,  635,  641,  661,  665,
        666,  667,  675,  687,  696,  697,  722,  723,  735,  736,  742,
        777,  778,  787,  788,  790,  794,  795,  797,  883,  884,  885,
        951,  954,  955,  965,  966,  967, 1012, 1014, 1017, 1050, 1051,
       1061, 1071, 1089, 1094, 1095, 1096, 1097, 1098, 1129, 1148, 1160,
       1222, 1230, 1234, 1241, 1242, 1243, 1246, 1247, 1248, 1249, 1251,
       1252, 1253, 1254, 1255, 1256], dtype=int64),)
测试集中预测为舞弊样本的个数有： 159
最优逻辑回归模型得到的AUC值： 0.8947541222822121
用最优逻辑回归模型，得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.06      0.91      0.12        11
          1       1.00      0.88      0.94      1246

avg / total       0.99      0.88      0.93      1257
In [868]:
# 开始朴素贝叶斯模型的相关训练。朴素贝叶斯也使用feature_test_sample样本。
feature_test_sample['label'] = merge_samples['label']
cols_bayes = fishvalue(feature_test_sample, ascending=True)
print(cols_bayes)

0
净资产收益率增长率B       0.019121
利息保障倍数B          0.022635
利润总额增长率B         0.064017
营业利润现金净含量        0.078418
净利润增长率B          0.122319
所有者权益增长率B        0.137061
总资产增长率B          0.156792
经营杠杆             0.159151
固定资产增长率B         0.161007
已流通股份            0.178975
股本总数             0.185301
存货与收入比           0.192904
财务杠杆             0.194951
营业收入增长率B         0.203102
营业利润率            0.232845
综合杠杆             0.238151
速动比率             0.250274
现金及现金等价物周转率B     0.326825
现金比率             0.340413
有形净值债务率          0.349547
流动比率             0.349765
资产负债率            0.376918
保守速动比率           0.398686
资产报酬率B           0.406682
总资产净利润率（ROA）B    0.443927
营业收入现金净含量        0.460457
权益对负债比率          0.518996
监事总规模            0.548191
dtype: float64
In [869]:
# 手工区分彼此独立的特征向量中连续型和离散型特征。
# 人工将样本集中连续型特征向量和离散型特征向量分成2个子集。
continuous_cols = ['净资产收益率增长率B', '利息保障倍数B', '利润总额增长率B', '营业利润现金净含量', '净利润增长率B', '所有者权益增长率B', 
                   '总资产增长率B', '经营杠杆', '固定资产增长率B', '存货与收入比', '财务杠杆', '营业收入增长率B', '营业利润率', 
                   '综合杠杆', '速动比率', '现金及现金等价物周转率B', '现金比率', '有形净值债务率', '流动比率', '资产负债率', 
                   '保守速动比率', '资产报酬率B', '总资产净利润率（ROA）B', '营业收入现金净含量', '权益对负债比率']

discrete_cols = ['已流通股份', '股本总数', '监事总规模']

# 将之前做曼.惠特尼 U检验得到的样本，分成连续型特征向量和离散型特征向量2个子集。
sample_continuous_independence = feature_test_sample.loc[:, continuous_cols]
sample_discrete_independence = feature_test_sample.loc[:, discrete_cols]

# 对连续型特征向量，直接计算相关去重多重共线性。针对离散型特征向量，将其标准化，按照连续型特征向量求相关的办法去除多重共线性。
continuous_multicollinearity_result = multicollinearity_check(sample_continuous_independence, coef_corr_crit = 0.7)
sample_continuous_bayes = sample_continuous_independence.loc[:,continuous_multicollinearity_result[0].columns]
print("朴素贝叶斯的训练集中连续型特征向量的属性有：\n", sample_continuous_bayes.columns)

# 离散型特征向量，先进行标准化，再计算相关。

aver = sample_discrete_independence.mean()
stds = sample_discrete_independence.std()
sample_discrete_independence_scale = (sample_discrete_independence - aver)/stds
discrete_multicollinearity_result = multicollinearity_check(sample_discrete_independence_scale, coef_corr_crit = 0.7)
sample_discrete_bayes = sample_discrete_independence.loc[:, discrete_multicollinearity_result[0].columns]
print("朴素贝叶斯的训练集中离散型特征向量的属性有：\n", sample_discrete_bayes.columns)

各列相关系数：
 [0.00144216817961587, 0.266362863022752, -0.04074081340728008, 0.3248162754573849, 0.011097628425079836, 0.032890338448322065, 0.003663627015528724, 0.02172370654785139, -0.02347486533313489, 0.013794121398880949, -0.06246712000657116, 0.01348838763344581, 0.013455136611883415, 0.01820656159044208, -0.016283775477401903, 0.025340707609602428, -0.009817489335131551, 0.01420655117334823, -0.006239177600711663, 0.024353411616673374, 0.06937489528394797, 0.0722901172284574, -0.02118505420247058, 0.015827419532336007, -0.0003590327239245374, -0.0003401268785604493, -0.00018502825268436386, -0.000384549403614251, -0.002060518109898519, -0.0008264962266667719, -0.0020013457633922135, -0.008693289032612074, -0.004406927929681737, -0.0020520883766335174, 0.001103432816563823, -0.003628814769132249, 0.009570260684524498, -0.005306179800194085, 0.006938397129284019, -0.0038544125044864858, 0.006573127884154006, -0.009659038729809973, 0.010038255463598891, 0.0053281880897566615, 0.006983582915083806, -0.00027957911490396396, 0.018823642518688434, -0.012549091721170403, 0.9725515012027429, 0.14807203517308698, 0.13944214828650867, -0.004267486408600675, 0.030607844126020606, -0.006508510022997263, -0.006388727261122722, 0.06984877485395427, -0.005201524483254323, -0.006934273415922588, -0.008256690889171492, 0.004192557304157075, -0.007656630794410142, -0.0039958287163785345, -0.010102545626854127, 0.003818236439199596, -0.00842998974521528, 0.020816313779038583, 0.020350867104219177, -0.0090762999551118, -0.005668360033383939, -0.014986158143714616, -0.0002132482411288956, -0.010169762843758859, 0.007276234941107044, -0.005067061254795126, -0.041628492597475385, 0.04790817525339251, -0.006127387426945903, 6.270245535944578e-06, 0.06421266015479014, -0.0032648510750246168, -0.007023382026360469, 0.007322034577808228, 0.003777523747092891, -0.013309872087904287, 0.003203481736913676, 0.003024343055534874, -0.005953036504407012, -0.008496567605719731, 0.1391379374012218, -0.010221376657693172, 0.14922923978314098, 0.17160966290860188, -0.004833484872908417, 0.049053430916275655, -0.01374038288254288, -0.004266053582221631, 0.11673357450693018, -0.004952368349280404, -0.004949485794770213, -0.0063778749942128175, 0.003226407350341479, -0.006107261699088929, -0.006222838505865594, -0.008961077219394507, 0.003040700415343681, -0.0055481722863908135, 0.034475738734649354, 0.03424034296335226, -0.010359403006654697, -0.004761459912935849, 0.2696768685170896, -0.002553145920772284, 0.033505857356337966, -0.04086910771562955, -0.0062951118237116015, 0.06400202587892438, 0.028072360511326774, -0.007201115952895933, 0.169928904799294, -0.010736496392929838, 0.259042891007241, -0.0059296210547977405, 0.15437231328368967, -0.02518000908125928, 0.20411679808344202, 0.0076871673196163585, 0.009409119180621612, 0.021203069814151616, 0.10307979840982669, -0.00011592005774788982, 0.21273782610018732, 0.12282773404090848, -0.022223534267493576, 0.4726529864405968, 0.016922998135785655, -0.02266231749601387, 0.06550198636235671, -0.006393551161781229, 0.11665731509084212, 0.0070092724069018226, 0.06110469731060249, 0.0013731788768856685, 0.08632476137583274, 0.016184978502440755, 0.016977299821006233, -0.07590437433800526, 0.02686687412085318, -0.00240230293450678, -0.02379487285279039, 0.04678060854620868, 0.001725551591208602, -0.0008549217370303899, -0.012787018310953847, -0.04541974719794733, 0.015307926981346568, -0.03789076347446676, 0.0288709161804415, -0.05319984424489668, 0.011714283812346822, -0.047306208660776096, -0.015086060766012625, -0.025630085674357414, 0.008846802652918083, -0.04614819011260139, 0.05772696593968117, -0.016352441047452114, 0.3156460562816733, 0.005745434353271657, -0.018668734764679257, -0.020571672323064973, -0.006746813050371479, -0.024203396635658545, 0.006461863313027115, -0.019144892440518792, 0.009753583247744167, -0.017041424188425046, 0.008759100213225714, 0.0096416667790772, 0.004737653031892205, -0.043216279073151065, -0.0271136481374564, 0.020466800991964722, 0.02361596095159118, -0.047243018052821203, -0.12693297425333314, -0.08042645886996916, -0.08938899775325526, 0.019151677595085154, 0.02313960202029894, 0.04277018693753762, -0.12807386553681432, -0.06931532293794183, -0.06195222878266794, -0.13519512000653472, -0.07773441873883709, -0.024328172029809147, -0.0074243092224864065, 0.821488516957293, -0.1290398207726612, 0.059715168505606674, -0.1056904973882776, 0.05595521584844768, -0.14488778187340273, 0.053630639652356656, -0.12481002506211494, -0.05013480106852509, -0.09890164343238889, 0.006915677640370861, -0.1300794560565374, 0.013538366586503935, -0.02338887794460296, -0.026991343477040963, 0.014722157535483367, -0.02719678963332585, 0.026238972556650097, -0.02422050869851953, 0.006369971258984416, -0.02484923868534241, 0.024589557358064198, 0.022284741276938816, -0.024901926641727086, -0.04300529133404731, -0.0061119434563316095, 0.04630078813110222, -0.011897426918339447, 0.04279445657476758, -0.015046978363328077, 0.04722545584126505, -0.565724314028529, 0.041954765348374834, 0.2645934705371692, 0.26120440934952227, 0.03717516101151497, 0.058389404685595524, -0.08937078987040081, 0.048091597774508894, -0.07628905060670857, 0.05036733667455726, -0.10845483996363617, 0.03146873552543277, -0.08430028990859442, -0.057566203857493364, -0.0872694188423233, 0.014087673449124143, -0.08378162358526442, -0.12736297316351966, 0.879034784646532, -0.09593062719660007, 0.9704531477293091, -0.18483373279423124, 0.9534490437632291, 0.1195751793040741, 0.17827265061365402, -0.012195462674915314, 0.8111348282621531, -0.14719422291089185, 0.09971024675427864, -0.12995465799148997, 0.04464843774220609, -0.12830810368943893, -0.02885104836952383, -0.05528716754923373, -0.017915161767790046, -0.10900606066954648, -0.07745576240225942, 0.8432078775722761, -0.149050476764493, 0.9341533707977246, 0.10287840988846135, 0.15328391785876944, 0.021679169185943144, 0.7199207312611301, -0.09831930104753049, 0.08382391893598595, -0.09267795261915576, -0.05763883280942824, -0.07234624929446642, -0.009584703278249144, -0.12165967388373586, -0.18629338352244168, 0.9260533181581867, 0.10994175814833423, 0.17235193978195115, -0.049131892253228136, 0.8007518165702124, -0.17390583808534502, -0.8054497778820967, -0.8107653310273767, -0.07735012879665097, -0.23825251410129192, 0.11892919689846587, 0.17564373819791718, 0.0046667156822598295, 0.7738771478249126, 0.9885256340313693, 0.10695484577926624, 0.1544572424295863, 0.10161180116201615, 0.2106834737579232, 0.05013581831921529]
朴素贝叶斯的训练集中连续型特征向量的属性有：
 Index(['净资产收益率增长率B', '利息保障倍数B', '利润总额增长率B', '营业利润现金净含量', '净利润增长率B',
       '所有者权益增长率B', '总资产增长率B', '经营杠杆', '固定资产增长率B', '存货与收入比', '财务杠杆',
       '营业收入增长率B', '营业利润率', '综合杠杆', '速动比率', '现金及现金等价物周转率B', '现金比率', '有形净值债务率',
       '流动比率', '资产负债率', '保守速动比率', '总资产净利润率（ROA）B', '营业收入现金净含量', '权益对负债比率'],
      dtype='object', name=0)
各列相关系数：
 [0.9155688145659241, 0.20421321586603097, 0.2293005602801019]
朴素贝叶斯的训练集中离散型特征向量的属性有：
 Index(['股本总数', '监事总规模'], dtype='object', name=0)
In [870]:
from sklearn.naive_bayes import MultinomialNB    # 处理财报中的离散变量。
from sklearn.naive_bayes import GaussianNB
import math

# 将上一步得到的线性模型使用的样本集也按照和之前划分训练集、测试集同样的方法进行划分。训练集：测试集=1：1
train_continuous_set = sample_continuous_bayes.iloc[::2,:]    # 将去除多重共线性的样本转化为ndarray数据类型，用于朴素贝叶斯模型的训练。
test_continuous_set = sample_continuous_bayes.iloc[1:sample_continuous_bayes.shape[0]:2,:]
train_continuous_set.reset_index(drop=True, inplace=True)
test_continuous_set.reset_index(drop=True, inplace=True)

train_discrete_set = sample_discrete_bayes.iloc[::2,:]
test_discrete_set = sample_discrete_bayes.iloc[1:sample_discrete_bayes.shape[0]:2,:]
train_discrete_set.reset_index(drop=True, inplace=True)
test_discrete_set.reset_index(drop=True, inplace=True)

# y_train和y_test不变，沿用之前的变量。

X_train_continuous = np.array(train_continuous_set)
X_test_continuous = np.array(test_continuous_set)

X_train_discrete = np.array(train_discrete_set)
X_test_discrete = np.array(test_discrete_set)


# 生成一个长度为训练集样本容量大小的list，将负例样本的权重设为大于1的某个整数。这个sample_weight在连续型样本和离散型样本上通用。
# 权重为array-like，1 for unweighted samples。也可以None，表示全体样本无权重
sample_weight_value = 100
sample_weight_train = [sample_weight_value if train_set.loc[i, "label"]==-1 else 1 
                       for i in range(train_continuous_set.shape[0])]

# 对连续型特征向量进行高斯朴素贝叶斯训练
clf_gauss = GaussianNB()    # 初始化高斯朴素贝叶斯模型实例
clf_gauss.partial_fit(X_train_continuous, y_train, classes=[1,-1], sample_weight=sample_weight_train)
continuous_pred_prob = clf_gauss.predict_log_proba(X_test_continuous)
print("检查输出概率的shape:",continuous_pred_prob.shape)

# 对离散型特征变量进行多项式朴素贝叶斯训练
clf_multinomial = MultinomialNB()    # 默认使用拉普拉斯平滑，alpha=1
clf_multinomial.partial_fit(X_train_discrete, y_train, classes=[1,-1], sample_weight=sample_weight_train)
discrete_pred_prob = clf_multinomial.predict_log_proba(X_test_discrete)
print("检查输出概率的shape:",discrete_pred_prob.shape)

# 然后将上一步得到的离散特征向量的输出概率和连续特征向量的输出概率相加，作为最终全部向量空间的输出概率。根据该输出概率判断样本属于哪个
# 分类。
y_pred_prob_bayes = continuous_pred_prob + discrete_pred_prob
print("最终连续特征向量和离散特征向量组成的总测试集，其最终预测对数概率pred_prob is:\n", y_pred_prob_bayes)
print("检查得到的对数概率shape中对应的分类标签：", clf_gauss.classes_, clf_multinomial.classes_)
y_pred_bayes = np.argmax(y_pred_prob_bayes, axis=1)    # 沿着列方向比较大小，取最大值所在的index。
y_pred_bayes[y_pred_bayes==0] = -1
print("朴素贝叶斯预测的结果是：\n", y_pred_bayes)

print("朴素贝叶斯分类模型中，测试集中预测为舞弊样本的索引号是：\n", np.where(y_pred_bayes==-1))
print("测试集中真实舞弊样本的index是：\n", y_test[y_test==-1])
print("测试集中预测为舞弊样本的个数有：", np.where(y_pred_bayes==-1)[0].size)

# 计算训练模型clf_op的AUC值，评估模型质量。
auc_values_bayes = auc(y_test, y_pred_bayes)
print("朴素贝叶斯模型得到的AUC值：", auc_values_bayes)

# 打印朴素贝叶斯的分类效果报告
print("用最优朴素贝叶斯模型，得到的分类结果评估报告如下：\n", classification_report(y_test, y_pred_bayes))

检查输出概率的shape: (1257, 2)
检查输出概率的shape: (1257, 2)
最终连续特征向量和离散特征向量组成的总测试集，其最终预测对数概率pred_prob is:
 [[-2.52057084e+01 -3.95261973e-01]
 [-1.11949045e+00 -9.17282003e+00]
 [-1.11941376e+00 -9.86085160e+00]
 ...
 [-3.30315716e+01 -3.08064432e+00]
 [-6.91381894e+02 -3.48773480e+00]
 [-3.66792449e-01 -1.42372580e+01]]
检查得到的对数概率shape中对应的分类标签： [-1  1] [-1  1]
朴素贝叶斯预测的结果是：
 [ 1 -1 -1 ...  1  1 -1]
朴素贝叶斯分类模型中，测试集中预测为舞弊样本的索引号是：
 (array([   1,    2,    3,    4,    7,    8,    9,   10,   15,   16,   17,
         18,   20,   22,   25,   26,   27,   29,   34,   42,   44,   45,
         46,   47,   48,   50,   51,   52,   53,   57,   58,   59,   60,
         63,   66,   67,   68,   69,   70,   76,   80,   81,   82,   83,
         85,   86,   87,   88,   89,   90,   95,   98,   99,  100,  101,
        102,  110,  117,  120,  121,  122,  127,  128,  129,  130,  131,
        132,  134,  139,  140,  141,  142,  143,  144,  145,  146,  147,
        148,  149,  151,  152,  154,  155,  156,  158,  164,  166,  167,
        168,  169,  171,  174,  175,  176,  177,  179,  180,  181,  182,
        184,  186,  188,  189,  193,  198,  201,  203,  205,  206,  207,
        208,  210,  212,  217,  218,  219,  224,  227,  228,  229,  230,
        231,  232,  233,  239,  240,  241,  242,  243,  244,  245,  246,
        248,  249,  250,  251,  252,  253,  254,  255,  271,  273,  274,
        275,  281,  282,  283,  284,  285,  286,  287,  289,  290,  291,
        292,  293,  294,  295,  297,  302,  303,  305,  306,  309,  310,
        311,  312,  313,  314,  315,  316,  327,  328,  329,  331,  332,
        333,  334,  335,  336,  337,  338,  339,  341,  342,  343,  344,
        345,  346,  347,  348,  349,  351,  352,  353,  354,  355,  357,
        360,  361,  362,  363,  364,  365,  366,  368,  370,  376,  377,
        378,  379,  380,  381,  382,  383,  384,  385,  386,  387,  388,
        389,  390,  391,  392,  393,  394,  395,  396,  397,  398,  399,
        400,  401,  402,  405,  406,  407,  408,  409,  415,  419,  420,
        421,  422,  423,  424,  425,  426,  427,  428,  434,  435,  436,
        437,  438,  439,  445,  446,  449,  450,  451,  452,  453,  457,
        458,  459,  462,  469,  471,  472,  473,  475,  476,  477,  479,
        481,  482,  483,  489,  490,  491,  492,  493,  495,  496,  497,
        498,  499,  500,  501,  502,  505,  506,  507,  508,  509,  510,
        511,  512,  514,  517,  518,  519,  520,  521,  522,  523,  524,
        525,  527,  528,  530,  531,  532,  533,  535,  537,  538,  539,
        540,  544,  545,  546,  553,  554,  555,  557,  560,  561,  562,
        573,  574,  575,  576,  577,  579,  580,  581,  584,  586,  591,
        592,  593,  594,  595,  597,  598,  599,  600,  601,  604,  605,
        606,  609,  611,  616,  619,  620,  621,  622,  623,  625,  626,
        633,  640,  643,  649,  651,  652,  653,  654,  658,  659,  662,
        663,  668,  669,  670,  671,  672,  673,  676,  677,  679,  681,
        682,  683,  684,  686,  687,  688,  690,  691,  693,  694,  696,
        697,  698,  699,  700,  702,  703,  704,  705,  706,  707,  708,
        709,  710,  711,  712,  713,  714,  715,  718,  719,  720,  721,
        722,  723,  724,  725,  726,  727,  729,  730,  732,  733,  734,
        735,  737,  739,  740,  742,  744,  746,  747,  748,  750,  751,
        752,  753,  754,  755,  757,  758,  759,  760,  761,  762,  763,
        764,  765,  766,  767,  768,  769,  770,  771,  772,  773,  774,
        776,  777,  778,  779,  780,  781,  782,  783,  784,  785,  788,
        789,  791,  792,  793,  794,  796,  804,  805,  806,  807,  808,
        809,  810,  811,  813,  814,  815,  817,  818,  819,  824,  825,
        826,  827,  828,  829,  831,  836,  837,  838,  842,  843,  850,
        851,  852,  853,  854,  860,  861,  862,  864,  865,  866,  867,
        868,  872,  873,  875,  877,  889,  890,  891,  893,  894,  895,
        897,  898,  899,  902,  903,  905,  907,  915,  916,  917,  918,
        919,  925,  926,  927,  928,  929,  930,  938,  939,  941,  942,
        943,  944,  952,  958,  959,  962,  963,  970,  971,  974,  975,
        983,  984,  985,  986,  987,  988,  989,  990,  991,  992,  993,
        995,  997, 1010, 1011, 1012, 1013, 1014, 1016, 1017, 1018, 1019,
       1023, 1024, 1025, 1043, 1044, 1045, 1049, 1050, 1051, 1052, 1053,
       1054, 1055, 1066, 1068, 1069, 1070, 1075, 1076, 1077, 1080, 1081,
       1104, 1105, 1106, 1107, 1110, 1111, 1112, 1113, 1115, 1116, 1118,
       1119, 1120, 1122, 1123, 1124, 1125, 1134, 1136, 1137, 1138, 1139,
       1140, 1141, 1142, 1143, 1144, 1145, 1146, 1147, 1150, 1152, 1153,
       1155, 1157, 1158, 1161, 1162, 1163, 1165, 1168, 1171, 1172, 1173,
       1174, 1177, 1178, 1180, 1181, 1182, 1184, 1185, 1186, 1189, 1190,
       1191, 1193, 1201, 1202, 1203, 1204, 1205, 1206, 1208, 1209, 1215,
       1216, 1217, 1218, 1219, 1221, 1222, 1223, 1224, 1225, 1226, 1228,
       1229, 1235, 1236, 1237, 1238, 1239, 1240, 1241, 1242, 1243, 1244,
       1245, 1246, 1247, 1248, 1251, 1252, 1253, 1256], dtype=int64),)
测试集中真实舞弊样本的index是：
 [-1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1]
测试集中预测为舞弊样本的个数有： 701
朴素贝叶斯模型得到的AUC值： 0.5396906464322195
用最优朴素贝叶斯模型，得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.01      0.64      0.02        11
          1       0.99      0.44      0.61      1246

avg / total       0.98      0.44      0.61      1257
In [876]:
# 对上述结果进一步考虑，如果将sample_bayes样本集中的特征，逐一减掉fishvalue最小的特征，这样遍历不同数目特征的样本集，分类性能是否会有
# 改善？
auc_bayes_iter_contin = []
for i in range(X_train_continuous.shape[1]):
    print("对连续型特征向量第%d轮迭代。"%(i+1))
    X_train_contin_iter = X_train_continuous[:,i:]
    X_test_contin_iter = X_test_continuous[:,i:]
    clf_gauss_iter = GaussianNB()    # 初始化高斯朴素贝叶斯模型实例
    clf_gauss_iter.partial_fit(X_train_contin_iter, y_train, classes=[1,-1], sample_weight=sample_weight_train)
    contin_pred_prob_iter = clf_gauss_iter.predict_log_proba(X_test_contin_iter)
    print("检查输出概率的shape:",contin_pred_prob_iter.shape)
    
    y_pred_prob_iter_for_contin = contin_pred_prob_iter + discrete_pred_prob    # discrete_pred_prob是之前未迭代时计算的离散特征向量的预测对数概率。
    print("最终连续特征向量和离散特征向量组成的总测试集，其最终预测对数概率pred_prob is:\n", y_pred_prob_iter_for_contin)
    print("检查得到的对数概率shape中对应的分类标签：", clf_gauss_iter.classes_)
    y_pred_bayes_iter_for_contin = np.argmax(y_pred_prob_iter_for_contin, axis=1)    # 沿着列方向比较大小，取最大值所在的index。
    y_pred_bayes_iter_for_contin[y_pred_bayes_iter_for_contin==0] = -1
    print("朴素贝叶斯预测的结果是：\n", y_pred_bayes_iter_for_contin)
    
    print("朴素贝叶斯分类模型中，测试集中预测为舞弊样本的索引号是：\n", np.where(y_pred_bayes_iter_for_contin==-1))
    print("测试集中预测为舞弊样本的个数有：", np.where(y_pred_bayes_iter_for_contin==-1)[0].size)
    
    # 计算训练模型clf_op的AUC值，评估模型质量。
    auc_value_op_bayes_for_contin = auc(y_test, y_pred_bayes_iter_for_contin)
    print("朴素贝叶斯模型得到的AUC值：", auc_value_op_bayes_for_contin)
    auc_bayes_iter_contin.append(auc_value_op_bayes_for_contin)
    
feature_count_forbayes_for_contin = [X_train_continuous.shape[1]-i for i in range(X_train_continuous.shape[1])]
print("连续型特征的样本特征数目：", feature_count_forbayes_for_contin)
print("朴素贝叶斯模型得到的AUC值分别是：\n",auc_bayes_iter_contin)

auc_bayes_iter_discrete = []
for i in range(X_train_discrete.shape[1]):
    print("对离散型特征向量第%d轮迭代。"%(i+1))
    X_train_discrete_iter = X_train_discrete[:,i:]
    X_test_discrete_iter = X_test_discrete[:,i:]

    clf_multinomial_iter = MultinomialNB()    # 默认使用拉普拉斯平滑，alpha=1
    clf_multinomial_iter.partial_fit(X_train_discrete_iter, y_train, classes=[1,-1], sample_weight=sample_weight_train)
    discrete_pred_prob_iter = clf_multinomial_iter.predict_log_proba(X_test_discrete_iter)
    
    y_pred_prob_iter_for_discrete = continuous_pred_prob + discrete_pred_prob_iter
    y_pred_bayes_iter_for_discrete = np.argmax(y_pred_prob_iter_for_discrete, axis=1)
    y_pred_bayes_iter_for_discrete[y_pred_bayes_iter_for_discrete==0] = -1
    
    print("朴素贝叶斯分类模型中，测试集中预测为舞弊样本的索引号是：\n", np.where(y_pred_bayes_iter_for_discrete==-1))
    print("测试集中预测为舞弊样本的个数有：", np.where(y_pred_bayes_iter_for_discrete==-1)[0].size)
    
    # 计算训练模型clf_op的AUC值，评估模型质量。
    auc_value_op_bayes_for_discrete = auc(y_test, y_pred_bayes_iter_for_discrete)
    print("朴素贝叶斯模型得到的AUC值：", auc_value_op_bayes_for_discrete)
    auc_bayes_iter_discrete.append(auc_value_op_bayes_for_discrete)
    
feature_count_forbayes_for_discrete = [X_train_discrete.shape[1]-i for i in range(X_train_discrete.shape[1])]
print("离散型特征的样本特征数目：", feature_count_forbayes_for_discrete)
print("朴素贝叶斯模型得到的AUC值分别是：\n",auc_bayes_iter_discrete)

# 朴素贝叶斯不同特征数目的AUC值的变化。综合看，从2个特征到19个特征，AUC值变化不是很大。为尽可能保留特征，因此选择18个特征训练Naive Bayes
# model。
auc_plot_by_feature_selection(feature_count_forbayes_for_contin, auc_bayes_iter_contin)
auc_plot_by_feature_selection(feature_count_forbayes_for_discrete, auc_bayes_iter_discrete)

对连续型特征向量第1轮迭代。
检查输出概率的shape: (1257, 2)
最终连续特征向量和离散特征向量组成的总测试集，其最终预测对数概率pred_prob is:
 [[-2.52057084e+01 -3.95261973e-01]
 [-1.11949045e+00 -9.17282003e+00]
 [-1.11941376e+00 -9.86085160e+00]
 ...
 [-3.30315716e+01 -3.08064432e+00]
 [-6.91381894e+02 -3.48773480e+00]
 [-3.66792449e-01 -1.42372580e+01]]
检查得到的对数概率shape中对应的分类标签： [-1  1]
朴素贝叶斯预测的结果是：
 [ 1 -1 -1 ...  1  1 -1]
朴素贝叶斯分类模型中，测试集中预测为舞弊样本的索引号是：
 (array([   1,    2,    3,    4,    7,    8,    9,   10,   15,   16,   17,
         18,   20,   22,   25,   26,   27,   29,   34,   42,   44,   45,
         46,   47,   48,   50,   51,   52,   53,   57,   58,   59,   60,
         63,   66,   67,   68,   69,   70,   76,   80,   81,   82,   83,
         85,   86,   87,   88,   89,   90,   95,   98,   99,  100,  101,
        102,  110,  117,  120,  121,  122,  127,  128,  129,  130,  131,
        132,  134,  139,  140,  141,  142,  143,  144,  145,  146,  147,
        148,  149,  151,  152,  154,  155,  156,  158,  164,  166,  167,
        168,  169,  171,  174,  175,  176,  177,  179,  180,  181,  182,
        184,  186,  188,  189,  193,  198,  201,  203,  205,  206,  207,
        208,  210,  212,  217,  218,  219,  224,  227,  228,  229,  230,
        231,  232,  233,  239,  240,  241,  242,  243,  244,  245,  246,
        248,  249,  250,  251,  252,  253,  254,  255,  271,  273,  274,
        275,  281,  282,  283,  284,  285,  286,  287,  289,  290,  291,
        292,  293,  294,  295,  297,  302,  303,  305,  306,  309,  310,
        311,  312,  313,  314,  315,  316,  327,  328,  329,  331,  332,
        333,  334,  335,  336,  337,  338,  339,  341,  342,  343,  344,
        345,  346,  347,  348,  349,  351,  352,  353,  354,  355,  357,
        360,  361,  362,  363,  364,  365,  366,  368,  370,  376,  377,
        378,  379,  380,  381,  382,  383,  384,  385,  386,  387,  388,
        389,  390,  391,  392,  393,  394,  395,  396,  397,  398,  399,
        400,  401,  402,  405,  406,  407,  408,  409,  415,  419,  420,
        421,  422,  423,  424,  425,  426,  427,  428,  434,  435,  436,
        437,  438,  439,  445,  446,  449,  450,  451,  452,  453,  457,
        458,  459,  462,  469,  471,  472,  473,  475,  476,  477,  479,
        481,  482,  483,  489,  490,  491,  492,  493,  495,  496,  497,
        498,  499,  500,  501,  502,  505,  506,  507,  508,  509,  510,
        511,  512,  514,  517,  518,  519,  520,  521,  522,  523,  524,
        525,  527,  528,  530,  531,  532,  533,  535,  537,  538,  539,
        540,  544,  545,  546,  553,  554,  555,  557,  560,  561,  562,
        573,  574,  575,  576,  577,  579,  580,  581,  584,  586,  591,
        592,  593,  594,  595,  597,  598,  599,  600,  601,  604,  605,
        606,  609,  611,  616,  619,  620,  621,  622,  623,  625,  626,
        633,  640,  643,  649,  651,  652,  653,  654,  658,  659,  662,
        663,  668,  669,  670,  671,  672,  673,  676,  677,  679,  681,
        682,  683,  684,  686,  687,  688,  690,  691,  693,  694,  696,
        697,  698,  699,  700,  702,  703,  704,  705,  706,  707,  708,
        709,  710,  711,  712,  713,  714,  715,  718,  719,  720,  721,
        722,  723,  724,  725,  726,  727,  729,  730,  732,  733,  734,
        735,  737,  739,  740,  742,  744,  746,  747,  748,  750,  751,
        752,  753,  754,  755,  757,  758,  759,  760,  761,  762,  763,
        764,  765,  766,  767,  768,  769,  770,  771,  772,  773,  774,
        776,  777,  778,  779,  780,  781,  782,  783,  784,  785,  788,
        789,  791,  792,  793,  794,  796,  804,  805,  806,  807,  808,
        809,  810,  811,  813,  814,  815,  817,  818,  819,  824,  825,
        826,  827,  828,  829,  831,  836,  837,  838,  842,  843,  850,
        851,  852,  853,  854,  860,  861,  862,  864,  865,  866,  867,
        868,  872,  873,  875,  877,  889,  890,  891,  893,  894,  895,
        897,  898,  899,  902,  903,  905,  907,  915,  916,  917,  918,
        919,  925,  926,  927,  928,  929,  930,  938,  939,  941,  942,
        943,  944,  952,  958,  959,  962,  963,  970,  971,  974,  975,
        983,  984,  985,  986,  987,  988,  989,  990,  991,  992,  993,
        995,  997, 1010, 1011, 1012, 1013, 1014, 1016, 1017, 1018, 1019,
       1023, 1024, 1025, 1043, 1044, 1045, 1049, 1050, 1051, 1052, 1053,
       1054, 1055, 1066, 1068, 1069, 1070, 1075, 1076, 1077, 1080, 1081,
       1104, 1105, 1106, 1107, 1110, 1111, 1112, 1113, 1115, 1116, 1118,
       1119, 1120, 1122, 1123, 1124, 1125, 1134, 1136, 1137, 1138, 1139,
       1140, 1141, 1142, 1143, 1144, 1145, 1146, 1147, 1150, 1152, 1153,
       1155, 1157, 1158, 1161, 1162, 1163, 1165, 1168, 1171, 1172, 1173,
       1174, 1177, 1178, 1180, 1181, 1182, 1184, 1185, 1186, 1189, 1190,
       1191, 1193, 1201, 1202, 1203, 1204, 1205, 1206, 1208, 1209, 1215,
       1216, 1217, 1218, 1219, 1221, 1222, 1223, 1224, 1225, 1226, 1228,
       1229, 1235, 1236, 1237, 1238, 1239, 1240, 1241, 1242, 1243, 1244,
       1245, 1246, 1247, 1248, 1251, 1252, 1253, 1256], dtype=int64),)
测试集中预测为舞弊样本的个数有： 701
朴素贝叶斯模型得到的AUC值： 0.5396906464322195
对连续型特征向量第2轮迭代。
检查输出概率的shape: (1257, 2)
最终连续特征向量和离散特征向量组成的总测试集，其最终预测对数概率pred_prob is:
 [[-2.50241735e+01 -3.95261973e-01]
 [-1.11946642e+00 -9.34225857e+00]
 [-1.11940129e+00 -1.00363778e+01]
 ...
 [-3.28691256e+01 -3.08064432e+00]
 [-6.91154409e+02 -3.48773480e+00]
 [-3.66792049e-01 -1.44447337e+01]]
检查得到的对数概率shape中对应的分类标签： [-1  1]
朴素贝叶斯预测的结果是：
 [ 1 -1 -1 ...  1  1 -1]
朴素贝叶斯分类模型中，测试集中预测为舞弊样本的索引号是：
 (array([   1,    2,    3,    4,    7,    8,    9,   10,   15,   16,   17,
         18,   20,   22,   25,   26,   27,   29,   34,   42,   44,   45,
         46,   47,   48,   50,   51,   52,   53,   57,   58,   59,   60,
         63,   66,   67,   68,   69,   70,   76,   80,   81,   82,   83,
         85,   86,   87,   88,   89,   90,   95,   98,   99,  100,  101,
        102,  110,  117,  120,  121,  122,  127,  128,  129,  130,  131,
        132,  134,  139,  140,  141,  142,  143,  144,  145,  146,  147,
        148,  149,  151,  152,  154,  155,  156,  158,  164,  166,  167,
        168,  169,  171,  174,  175,  176,  177,  179,  180,  181,  182,
        184,  186,  188,  189,  193,  198,  201,  203,  205,  206,  207,
        208,  210,  212,  217,  218,  219,  224,  227,  228,  229,  230,
        231,  232,  233,  239,  240,  241,  242,  243,  244,  245,  246,
        248,  249,  250,  251,  252,  253,  254,  255,  271,  273,  274,
        275,  281,  282,  283,  284,  285,  286,  287,  289,  290,  291,
        292,  293,  294,  295,  297,  302,  303,  305,  306,  309,  310,
        311,  312,  313,  314,  315,  316,  327,  328,  329,  331,  332,
        333,  334,  335,  336,  337,  338,  339,  341,  342,  343,  344,
        345,  346,  347,  348,  349,  351,  352,  353,  354,  355,  357,
        360,  361,  362,  363,  364,  365,  366,  368,  370,  376,  377,
        378,  379,  380,  381,  382,  383,  384,  385,  386,  387,  388,
        389,  390,  391,  392,  393,  394,  395,  396,  397,  398,  399,
        400,  401,  402,  405,  406,  407,  408,  409,  415,  419,  420,
        421,  422,  423,  424,  425,  426,  427,  428,  434,  435,  436,
        437,  438,  439,  445,  446,  449,  450,  451,  452,  453,  457,
        458,  459,  462,  469,  471,  472,  473,  475,  476,  477,  479,
        481,  482,  483,  489,  490,  491,  492,  493,  495,  496,  497,
        498,  499,  500,  501,  502,  505,  506,  507,  508,  509,  510,
        511,  512,  514,  517,  518,  519,  520,  521,  522,  523,  524,
        525,  527,  528,  530,  531,  532,  533,  535,  537,  538,  539,
        540,  544,  545,  546,  553,  554,  555,  557,  560,  561,  562,
        573,  574,  575,  576,  577,  579,  580,  581,  584,  586,  591,
        592,  593,  594,  595,  597,  598,  599,  600,  601,  604,  605,
        606,  609,  611,  616,  619,  620,  621,  622,  623,  625,  626,
        633,  640,  643,  649,  651,  652,  653,  654,  658,  659,  662,
        663,  668,  669,  670,  671,  672,  673,  676,  677,  679,  681,
        682,  683,  684,  686,  687,  688,  690,  691,  693,  694,  696,
        697,  698,  699,  700,  702,  703,  704,  705,  706,  707,  708,
        709,  710,  711,  712,  713,  714,  715,  718,  719,  720,  721,
        722,  723,  724,  725,  726,  727,  729,  730,  732,  733,  734,
        735,  737,  739,  740,  742,  744,  746,  747,  748,  750,  751,
        752,  753,  754,  755,  757,  758,  759,  760,  761,  762,  763,
        764,  765,  766,  767,  768,  769,  770,  771,  772,  773,  774,
        776,  777,  778,  779,  780,  781,  782,  783,  784,  785,  788,
        789,  791,  792,  793,  794,  796,  804,  805,  806,  807,  808,
        809,  810,  811,  813,  814,  815,  817,  818,  819,  824,  825,
        826,  827,  828,  829,  831,  836,  837,  838,  842,  843,  850,
        851,  852,  853,  854,  860,  861,  862,  864,  865,  866,  867,
        868,  872,  873,  875,  877,  889,  890,  891,  893,  894,  895,
        897,  898,  899,  902,  903,  904,  905,  907,  915,  916,  917,
        918,  919,  925,  926,  927,  928,  929,  930,  938,  939,  941,
        942,  943,  944,  952,  958,  959,  962,  963,  970,  971,  974,
        975,  983,  984,  985,  986,  987,  988,  989,  990,  991,  992,
        993,  995,  997, 1010, 1011, 1012, 1013, 1014, 1015, 1016, 1017,
       1018, 1019, 1023, 1024, 1025, 1043, 1044, 1045, 1049, 1050, 1051,
       1052, 1053, 1054, 1055, 1066, 1068, 1069, 1070, 1075, 1076, 1077,
       1080, 1081, 1104, 1105, 1106, 1107, 1110, 1111, 1112, 1113, 1115,
       1116, 1118, 1119, 1120, 1121, 1122, 1123, 1124, 1125, 1134, 1136,
       1137, 1138, 1139, 1140, 1141, 1142, 1143, 1144, 1145, 1146, 1147,
       1150, 1152, 1153, 1155, 1157, 1158, 1161, 1162, 1163, 1165, 1168,
       1171, 1172, 1173, 1174, 1177, 1178, 1180, 1181, 1182, 1184, 1185,
       1186, 1189, 1190, 1191, 1193, 1201, 1202, 1203, 1204, 1205, 1206,
       1208, 1209, 1215, 1216, 1217, 1218, 1219, 1221, 1222, 1223, 1224,
       1225, 1226, 1228, 1229, 1235, 1236, 1237, 1238, 1239, 1240, 1241,
       1242, 1243, 1244, 1245, 1246, 1247, 1248, 1251, 1252, 1253, 1256],
      dtype=int64),)
测试集中预测为舞弊样本的个数有： 704
朴素贝叶斯模型得到的AUC值： 0.5384867941047716
对连续型特征向量第3轮迭代。
检查输出概率的shape: (1257, 2)
最终连续特征向量和离散特征向量组成的总测试集，其最终预测对数概率pred_prob is:
 [[ -32.11788893   -0.39526197]
 [  -1.13729791   -4.423747  ]
 [  -1.12807971   -5.13908341]
 ...
 [ -77.6148932    -3.08064432]
 [-333.01166047   -3.4877348 ]
 [  -0.36679189  -14.54274151]]
检查得到的对数概率shape中对应的分类标签： [-1  1]
朴素贝叶斯预测的结果是：
 [ 1 -1 -1 ...  1  1 -1]
朴素贝叶斯分类模型中，测试集中预测为舞弊样本的索引号是：
 (array([   1,    2,    3,    4,    6,    7,    8,    9,   15,   16,   17,
         18,   25,   26,   27,   28,   29,   34,   45,   46,   48,   50,
         51,   52,   55,   59,   60,   68,   69,   70,   80,   81,   82,
         83,   85,   86,   87,   88,   89,   98,   99,  100,  101,  102,
        117,  121,  127,  129,  130,  131,  132,  134,  139,  140,  142,
        143,  144,  145,  146,  147,  148,  149,  151,  152,  154,  155,
        156,  164,  167,  168,  169,  170,  171,  174,  175,  176,  177,
        180,  181,  186,  188,  189,  193,  198,  203,  205,  206,  207,
        217,  218,  219,  224,  227,  228,  239,  241,  243,  244,  245,
        248,  249,  250,  251,  252,  253,  254,  255,  271,  273,  274,
        275,  281,  282,  283,  285,  286,  287,  289,  290,  291,  292,
        293,  294,  297,  302,  303,  305,  309,  310,  311,  312,  313,
        314,  315,  326,  327,  329,  332,  333,  334,  336,  337,  338,
        339,  341,  342,  343,  344,  345,  347,  348,  349,  350,  351,
        352,  353,  354,  355,  357,  360,  361,  362,  366,  368,  370,
        376,  377,  378,  379,  380,  381,  382,  383,  384,  386,  387,
        388,  389,  390,  391,  392,  393,  394,  396,  397,  398,  399,
        400,  401,  402,  405,  406,  407,  408,  409,  415,  419,  420,
        421,  422,  423,  424,  425,  426,  427,  428,  434,  435,  436,
        437,  438,  445,  446,  449,  450,  451,  452,  453,  458,  459,
        460,  462,  469,  471,  472,  473,  475,  476,  477,  479,  480,
        482,  483,  489,  490,  491,  492,  493,  495,  496,  497,  498,
        499,  500,  501,  502,  505,  506,  507,  508,  510,  511,  512,
        518,  519,  520,  521,  522,  523,  524,  525,  526,  527,  528,
        530,  531,  533,  535,  539,  540,  545,  546,  555,  560,  561,
        562,  574,  575,  576,  577,  579,  580,  584,  591,  592,  593,
        594,  597,  598,  600,  604,  605,  606,  609,  611,  619,  621,
        622,  626,  637,  640,  642,  643,  649,  651,  652,  653,  654,
        658,  668,  669,  670,  671,  672,  673,  676,  677,  678,  679,
        681,  682,  683,  684,  685,  686,  687,  690,  691,  693,  694,
        696,  697,  698,  700,  702,  703,  704,  705,  706,  707,  712,
        714,  715,  718,  719,  720,  721,  722,  723,  724,  725,  726,
        727,  729,  730,  731,  732,  733,  734,  735,  739,  740,  741,
        744,  745,  746,  747,  748,  751,  752,  753,  754,  755,  757,
        758,  759,  762,  764,  765,  766,  767,  768,  769,  770,  771,
        772,  773,  774,  776,  777,  778,  779,  780,  781,  782,  783,
        784,  785,  791,  792,  793,  796,  805,  808,  809,  810,  811,
        813,  814,  815,  819,  824,  825,  827,  828,  829,  831,  842,
        843,  850,  851,  852,  853,  854,  861,  864,  865,  866,  867,
        868,  873,  889,  890,  891,  893,  894,  899,  903,  907,  915,
        916,  917,  918,  919,  928,  930,  936,  938,  939,  942,  943,
        944,  952,  973,  974,  984,  985,  986,  987,  988,  989,  990,
        991,  992,  993,  995,  997, 1010, 1011, 1012, 1013, 1014, 1019,
       1020, 1024, 1025, 1044, 1045, 1049, 1050, 1051, 1052, 1053, 1054,
       1066, 1069, 1070, 1080, 1110, 1111, 1112, 1113, 1116, 1118, 1119,
       1120, 1122, 1123, 1124, 1125, 1134, 1136, 1137, 1138, 1139, 1141,
       1142, 1143, 1144, 1145, 1146, 1147, 1149, 1150, 1152, 1153, 1155,
       1161, 1162, 1163, 1171, 1172, 1173, 1178, 1180, 1181, 1182, 1185,
       1189, 1190, 1193, 1195, 1198, 1201, 1202, 1203, 1205, 1206, 1209,
       1215, 1216, 1217, 1218, 1219, 1222, 1223, 1224, 1228, 1229, 1235,
       1236, 1239, 1240, 1241, 1242, 1243, 1244, 1245, 1246, 1247, 1248,
       1251, 1252, 1253, 1256], dtype=int64),)
测试集中预测为舞弊样本的个数有： 565
朴素贝叶斯模型得到的AUC值： 0.5942652852765212
对连续型特征向量第4轮迭代。
检查输出概率的shape: (1257, 2)
最终连续特征向量和离散特征向量组成的总测试集，其最终预测对数概率pred_prob is:
 [[ -32.53252167   -0.39526197]
 [  -1.14693755   -3.99892457]
 [  -1.13318068   -4.68205118]
 ...
 [ -78.14731457   -3.08064432]
 [-329.41794807   -3.4877348 ]
 [  -0.36679321  -13.93396468]]
检查得到的对数概率shape中对应的分类标签： [-1  1]
朴素贝叶斯预测的结果是：
 [ 1 -1 -1 ...  1  1 -1]
朴素贝叶斯分类模型中，测试集中预测为舞弊样本的索引号是：
 (array([   1,    2,    3,    4,    6,    7,    8,    9,   15,   16,   17,
         18,   25,   26,   27,   28,   29,   34,   45,   46,   48,   50,
         51,   52,   55,   59,   60,   68,   69,   70,   80,   81,   82,
         83,   85,   86,   87,   88,   89,   98,   99,  100,  101,  102,
        117,  121,  127,  128,  129,  130,  131,  132,  134,  139,  140,
        142,  143,  144,  145,  146,  148,  149,  151,  152,  154,  155,
        156,  164,  167,  168,  169,  170,  171,  174,  175,  176,  177,
        180,  181,  186,  188,  189,  193,  198,  203,  205,  206,  207,
        217,  218,  219,  224,  227,  239,  241,  243,  244,  245,  248,
        249,  250,  251,  252,  253,  254,  255,  271,  273,  274,  275,
        281,  282,  283,  285,  286,  287,  288,  289,  290,  291,  292,
        293,  294,  297,  302,  303,  305,  309,  310,  311,  312,  313,
        314,  315,  326,  327,  329,  332,  333,  334,  336,  337,  338,
        339,  341,  342,  343,  344,  345,  347,  348,  349,  350,  351,
        352,  353,  354,  355,  357,  360,  361,  362,  366,  368,  370,
        376,  377,  378,  379,  380,  381,  382,  383,  384,  386,  387,
        388,  389,  390,  392,  393,  394,  396,  397,  398,  399,  400,
        401,  402,  405,  406,  407,  408,  409,  415,  419,  420,  421,
        422,  423,  424,  425,  426,  427,  434,  435,  436,  437,  438,
        445,  446,  448,  449,  450,  451,  452,  453,  458,  459,  460,
        462,  469,  471,  472,  473,  475,  476,  477,  479,  480,  482,
        483,  489,  490,  491,  492,  493,  494,  495,  496,  497,  498,
        499,  500,  501,  502,  505,  506,  507,  508,  510,  511,  512,
        518,  519,  520,  521,  522,  523,  524,  525,  526,  527,  528,
        530,  531,  533,  535,  539,  540,  545,  546,  555,  560,  561,
        562,  574,  575,  576,  577,  579,  580,  584,  591,  592,  593,
        594,  597,  598,  600,  604,  605,  606,  609,  611,  619,  621,
        622,  626,  637,  640,  642,  643,  649,  651,  652,  653,  654,
        658,  668,  669,  670,  671,  672,  673,  676,  677,  678,  679,
        681,  682,  683,  684,  685,  686,  687,  690,  691,  693,  694,
        696,  697,  698,  700,  702,  703,  704,  705,  706,  707,  712,
        714,  715,  718,  719,  720,  721,  722,  723,  724,  725,  726,
        727,  729,  730,  731,  732,  733,  734,  735,  740,  741,  744,
        745,  746,  747,  748,  752,  753,  754,  755,  757,  758,  759,
        762,  764,  765,  766,  767,  768,  769,  770,  771,  772,  773,
        774,  776,  777,  778,  779,  780,  781,  782,  783,  784,  785,
        791,  792,  796,  805,  808,  809,  810,  811,  814,  815,  819,
        824,  825,  827,  828,  829,  831,  842,  843,  850,  851,  852,
        853,  854,  861,  864,  865,  866,  867,  868,  889,  890,  891,
        893,  894,  897,  899,  903,  907,  915,  916,  917,  918,  919,
        928,  930,  936,  942,  943,  944,  952,  973,  974,  984,  985,
        986,  987,  988,  989,  990,  991,  992,  993,  995,  997, 1010,
       1011, 1012, 1013, 1014, 1019, 1020, 1024, 1025, 1044, 1045, 1050,
       1051, 1052, 1053, 1054, 1066, 1069, 1070, 1080, 1110, 1112, 1113,
       1116, 1118, 1119, 1120, 1122, 1123, 1124, 1125, 1134, 1136, 1137,
       1138, 1139, 1141, 1142, 1143, 1144, 1145, 1146, 1147, 1149, 1150,
       1152, 1153, 1161, 1162, 1163, 1172, 1173, 1178, 1180, 1181, 1182,
       1185, 1189, 1190, 1193, 1195, 1198, 1201, 1202, 1203, 1205, 1206,
       1209, 1215, 1216, 1217, 1218, 1219, 1222, 1223, 1224, 1228, 1229,
       1235, 1236, 1239, 1240, 1241, 1242, 1243, 1244, 1246, 1247, 1248,
       1251, 1252, 1253, 1256], dtype=int64),)
测试集中预测为舞弊样本的个数有： 554
朴素贝叶斯模型得到的AUC值： 0.5986794104771632
对连续型特征向量第5轮迭代。
检查输出概率的shape: (1257, 2)
最终连续特征向量和离散特征向量组成的总测试集，其最终预测对数概率pred_prob is:
 [[ -34.74225915   -0.39526197]
 [  -1.34552993   -1.99259171]
 [  -1.23897033   -2.57780052]
 ...
 [ -80.37060838   -3.08064432]
 [-331.28446347   -3.4877348 ]
 [  -0.36681652  -11.73014374]]
检查得到的对数概率shape中对应的分类标签： [-1  1]
朴素贝叶斯预测的结果是：
 [ 1 -1 -1 ...  1  1 -1]
朴素贝叶斯分类模型中，测试集中预测为舞弊样本的索引号是：
 (array([   1,    2,    3,    6,    7,    8,    9,   15,   16,   17,   18,
         26,   27,   28,   45,   46,   48,   50,   51,   52,   55,   67,
         68,   69,   70,   80,   81,   82,   83,   85,   86,   87,   89,
         98,   99,  100,  101,  102,  117,  127,  128,  129,  130,  131,
        132,  139,  142,  143,  144,  146,  149,  151,  152,  154,  155,
        156,  164,  167,  168,  169,  170,  171,  176,  177,  186,  188,
        198,  203,  204,  205,  217,  218,  219,  224,  227,  241,  243,
        244,  248,  249,  250,  251,  253,  254,  255,  271,  273,  274,
        281,  282,  283,  286,  287,  288,  289,  290,  291,  292,  293,
        294,  297,  302,  303,  309,  310,  311,  312,  313,  314,  315,
        326,  327,  329,  332,  333,  337,  338,  339,  341,  342,  343,
        344,  345,  348,  349,  351,  352,  353,  354,  355,  357,  360,
        361,  362,  366,  368,  370,  376,  377,  378,  379,  380,  381,
        382,  383,  384,  386,  387,  388,  389,  390,  392,  393,  394,
        396,  397,  398,  399,  400,  401,  402,  405,  406,  407,  408,
        409,  419,  420,  421,  422,  423,  424,  425,  434,  435,  436,
        437,  445,  446,  448,  449,  450,  451,  452,  453,  458,  459,
        460,  462,  469,  471,  472,  473,  475,  477,  482,  483,  489,
        490,  491,  493,  494,  495,  496,  497,  498,  499,  501,  502,
        505,  506,  507,  510,  511,  512,  518,  519,  520,  521,  522,
        523,  524,  525,  526,  527,  528,  530,  531,  533,  539,  540,
        545,  555,  560,  561,  562,  574,  575,  576,  577,  579,  591,
        592,  593,  594,  597,  598,  604,  605,  609,  611,  619,  621,
        622,  626,  637,  640,  641,  642,  643,  649,  652,  653,  656,
        658,  668,  669,  670,  672,  673,  676,  677,  678,  679,  681,
        682,  683,  685,  686,  687,  690,  691,  693,  694,  696,  697,
        698,  699,  700,  703,  704,  705,  706,  712,  714,  715,  718,
        719,  720,  721,  722,  723,  725,  727,  730,  731,  732,  733,
        734,  735,  740,  741,  744,  745,  746,  747,  748,  752,  753,
        754,  755,  757,  758,  759,  762,  764,  766,  767,  768,  769,
        770,  771,  772,  773,  776,  777,  778,  779,  780,  781,  782,
        784,  785,  791,  792,  796,  805,  808,  809,  810,  811,  819,
        824,  825,  827,  828,  831,  842,  843,  850,  852,  853,  854,
        861,  864,  865,  866,  867,  889,  894,  899,  903,  907,  916,
        917,  918,  919,  936,  943,  952,  973,  974,  984,  985,  986,
        987,  988,  989,  990,  992,  993,  997, 1010, 1011, 1012, 1013,
       1014, 1019, 1020, 1025, 1044, 1045, 1050, 1051, 1052, 1053, 1054,
       1066, 1069, 1070, 1080, 1112, 1113, 1116, 1118, 1119, 1120, 1123,
       1124, 1125, 1134, 1136, 1137, 1138, 1139, 1141, 1142, 1143, 1145,
       1146, 1147, 1149, 1150, 1152, 1161, 1162, 1163, 1172, 1173, 1178,
       1180, 1181, 1182, 1190, 1193, 1195, 1198, 1202, 1203, 1206, 1209,
       1215, 1216, 1217, 1218, 1219, 1222, 1223, 1224, 1229, 1235, 1236,
       1239, 1240, 1241, 1242, 1243, 1244, 1246, 1247, 1248, 1251, 1252,
       1253, 1256], dtype=int64),)
测试集中预测为舞弊样本的个数有： 475
朴素贝叶斯模型得到的AUC值： 0.6303808550999561
对连续型特征向量第6轮迭代。
检查输出概率的shape: (1257, 2)
最终连续特征向量和离散特征向量组成的总测试集，其最终预测对数概率pred_prob is:
 [[ -34.11084267   -0.39526197]
 [  -1.24707072   -2.5162513 ]
 [  -1.18591386   -3.13775355]
 ...
 [ -79.77774136   -3.08064432]
 [-331.19129789   -3.4877348 ]
 [  -0.36680555  -12.27234647]]
检查得到的对数概率shape中对应的分类标签： [-1  1]
朴素贝叶斯预测的结果是：
 [ 1 -1 -1 ...  1  1 -1]
朴素贝叶斯分类模型中，测试集中预测为舞弊样本的索引号是：
 (array([   1,    2,    3,    4,    6,    7,    8,    9,   15,   16,   17,
         18,   26,   27,   28,   29,   34,   45,   46,   48,   50,   51,
         52,   55,   67,   68,   69,   70,   80,   81,   82,   83,   85,
         86,   87,   89,   98,   99,  100,  101,  102,  117,  127,  128,
        129,  130,  131,  132,  134,  139,  142,  143,  144,  146,  149,
        151,  152,  154,  155,  156,  164,  167,  168,  169,  170,  171,
        174,  176,  177,  180,  186,  188,  189,  198,  203,  204,  205,
        207,  217,  218,  219,  224,  227,  239,  241,  243,  244,  248,
        249,  250,  251,  253,  254,  255,  271,  273,  274,  275,  281,
        282,  283,  286,  287,  288,  289,  290,  291,  292,  293,  294,
        297,  302,  303,  309,  310,  311,  312,  313,  314,  315,  326,
        327,  329,  332,  333,  337,  338,  339,  341,  342,  343,  344,
        345,  348,  349,  350,  351,  352,  353,  354,  355,  357,  360,
        361,  362,  366,  368,  370,  376,  377,  378,  379,  380,  381,
        382,  383,  384,  386,  387,  388,  389,  390,  392,  393,  394,
        396,  397,  398,  399,  400,  401,  402,  405,  406,  407,  408,
        409,  419,  420,  421,  422,  423,  424,  425,  426,  434,  435,
        436,  437,  438,  445,  446,  448,  449,  450,  451,  452,  453,
        458,  459,  460,  462,  469,  471,  472,  473,  475,  477,  479,
        482,  483,  489,  490,  491,  493,  495,  496,  497,  498,  499,
        500,  501,  502,  505,  506,  507,  510,  511,  512,  518,  519,
        520,  521,  523,  524,  525,  526,  527,  528,  530,  531,  533,
        539,  540,  545,  555,  560,  561,  562,  574,  575,  576,  577,
        579,  580,  584,  591,  592,  593,  594,  597,  598,  604,  605,
        606,  609,  611,  619,  621,  622,  626,  637,  640,  641,  642,
        643,  649,  652,  653,  656,  658,  668,  669,  670,  672,  673,
        676,  677,  678,  679,  681,  682,  683,  685,  686,  687,  690,
        691,  693,  694,  696,  697,  698,  699,  700,  703,  704,  705,
        706,  707,  712,  714,  715,  718,  719,  720,  721,  722,  723,
        725,  727,  730,  731,  732,  733,  734,  735,  740,  741,  744,
        745,  746,  747,  748,  752,  753,  754,  755,  757,  758,  759,
        762,  764,  766,  767,  768,  769,  770,  771,  772,  773,  776,
        777,  778,  779,  780,  781,  782,  783,  784,  785,  791,  792,
        796,  805,  808,  809,  810,  811,  819,  824,  825,  827,  828,
        831,  842,  843,  850,  852,  853,  854,  861,  864,  865,  866,
        867,  889,  890,  891,  893,  894,  899,  903,  907,  916,  917,
        918,  919,  936,  943,  952,  973,  974,  984,  985,  986,  987,
        988,  989,  990,  992,  997, 1010, 1011, 1012, 1013, 1014, 1019,
       1020, 1025, 1044, 1045, 1050, 1051, 1052, 1053, 1054, 1066, 1069,
       1070, 1080, 1112, 1113, 1116, 1118, 1119, 1120, 1123, 1124, 1125,
       1134, 1136, 1137, 1138, 1139, 1141, 1142, 1143, 1145, 1146, 1147,
       1149, 1150, 1152, 1161, 1162, 1163, 1172, 1173, 1178, 1180, 1181,
       1182, 1190, 1193, 1195, 1198, 1201, 1202, 1203, 1206, 1209, 1215,
       1216, 1217, 1218, 1219, 1222, 1223, 1224, 1229, 1235, 1236, 1239,
       1240, 1241, 1242, 1243, 1244, 1246, 1247, 1248, 1251, 1252, 1253,
       1256], dtype=int64),)
测试集中预测为舞弊样本的个数有： 496
朴素贝叶斯模型得到的AUC值： 0.6219538888078213
对连续型特征向量第7轮迭代。
检查输出概率的shape: (1257, 2)
最终连续特征向量和离散特征向量组成的总测试集，其最终预测对数概率pred_prob is:
 [[ -34.51884645   -0.39526197]
 [  -1.33504138   -2.03501967]
 [  -1.23317056   -2.62465087]
 ...
 [ -80.43315891   -3.08064432]
 [-320.26348736   -3.4877348 ]
 [  -0.36680402  -12.37843852]]
检查得到的对数概率shape中对应的分类标签： [-1  1]
朴素贝叶斯预测的结果是：
 [ 1 -1 -1 ...  1  1 -1]
朴素贝叶斯分类模型中，测试集中预测为舞弊样本的索引号是：
 (array([   1,    2,    3,    6,    7,    8,    9,   15,   16,   17,   18,
         26,   27,   28,   34,   45,   46,   48,   50,   51,   52,   55,
         67,   68,   69,   70,   80,   81,   82,   83,   85,   86,   87,
         89,   98,   99,  100,  101,  102,  117,  127,  128,  129,  130,
        131,  132,  139,  142,  143,  144,  146,  149,  151,  152,  154,
        155,  156,  164,  167,  168,  169,  170,  171,  176,  177,  186,
        188,  198,  203,  204,  205,  217,  218,  219,  224,  227,  241,
        243,  244,  248,  249,  250,  251,  253,  254,  255,  271,  273,
        274,  281,  282,  283,  286,  287,  288,  289,  290,  291,  292,
        293,  294,  297,  302,  303,  309,  310,  311,  312,  313,  314,
        315,  326,  327,  329,  332,  333,  337,  338,  339,  341,  342,
        343,  344,  345,  348,  349,  351,  352,  353,  354,  355,  357,
        360,  361,  362,  366,  368,  370,  376,  377,  378,  379,  380,
        381,  382,  383,  384,  386,  387,  388,  389,  390,  392,  393,
        394,  396,  397,  398,  399,  400,  401,  402,  405,  406,  407,
        408,  409,  419,  420,  421,  422,  423,  424,  425,  434,  435,
        436,  437,  445,  446,  448,  449,  450,  451,  452,  453,  458,
        459,  460,  462,  469,  471,  472,  473,  475,  477,  482,  483,
        489,  490,  491,  493,  495,  496,  497,  498,  499,  501,  502,
        505,  506,  507,  510,  511,  512,  518,  519,  520,  521,  523,
        524,  525,  526,  527,  528,  530,  531,  533,  540,  545,  555,
        560,  561,  562,  574,  575,  576,  577,  579,  580,  585,  591,
        592,  593,  594,  597,  598,  604,  605,  606,  609,  611,  619,
        621,  622,  625,  626,  637,  640,  641,  642,  643,  649,  652,
        653,  656,  658,  668,  669,  670,  672,  673,  676,  677,  678,
        679,  681,  682,  683,  685,  686,  687,  690,  691,  693,  694,
        696,  697,  698,  699,  700,  703,  704,  705,  706,  712,  714,
        715,  718,  719,  720,  721,  722,  723,  725,  727,  730,  731,
        732,  733,  734,  735,  740,  741,  744,  745,  746,  747,  748,
        752,  753,  754,  755,  757,  758,  759,  762,  764,  766,  767,
        768,  769,  770,  771,  772,  773,  776,  777,  778,  779,  780,
        781,  782,  784,  785,  791,  792,  796,  805,  808,  809,  810,
        811,  819,  824,  825,  827,  828,  831,  842,  843,  850,  852,
        853,  854,  861,  864,  865,  866,  867,  889,  890,  894,  899,
        903,  907,  916,  917,  918,  919,  936,  943,  952,  973,  974,
        984,  985,  986,  987,  988,  989,  990,  992,  997, 1010, 1011,
       1012, 1013, 1014, 1019, 1020, 1025, 1044, 1045, 1050, 1051, 1052,
       1053, 1054, 1066, 1069, 1070, 1080, 1112, 1113, 1116, 1118, 1119,
       1120, 1123, 1124, 1125, 1134, 1136, 1137, 1138, 1139, 1141, 1142,
       1143, 1145, 1146, 1147, 1149, 1150, 1152, 1161, 1162, 1163, 1172,
       1173, 1178, 1180, 1181, 1182, 1190, 1193, 1195, 1198, 1202, 1203,
       1206, 1209, 1215, 1216, 1217, 1218, 1219, 1222, 1223, 1224, 1229,
       1235, 1236, 1239, 1240, 1241, 1242, 1243, 1244, 1246, 1247, 1248,
       1251, 1252, 1253, 1256], dtype=int64),)
测试集中预测为舞弊样本的个数有： 477
朴素贝叶斯模型得到的AUC值： 0.6295782868816576
对连续型特征向量第8轮迭代。
检查输出概率的shape: (1257, 2)
最终连续特征向量和离散特征向量组成的总测试集，其最终预测对数概率pred_prob is:
 [[ -34.48428216   -0.39526197]
 [  -2.10793471   -0.86063291]
 [  -1.72593071   -1.18316926]
 ...
 [ -82.15078347   -3.08064432]
 [-317.19689958   -3.4877348 ]
 [  -0.36688368  -10.45976163]]
检查得到的对数概率shape中对应的分类标签： [-1  1]
朴素贝叶斯预测的结果是：
 [ 1  1  1 ...  1  1 -1]
朴素贝叶斯分类模型中，测试集中预测为舞弊样本的索引号是：
 (array([   6,    7,    8,    9,   15,   16,   17,   18,   26,   27,   28,
         45,   48,   50,   51,   52,   55,   67,   68,   69,   80,   81,
         82,   83,   85,   86,   89,   98,   99,  100,  101,  102,  117,
        127,  128,  130,  131,  132,  139,  142,  143,  144,  149,  151,
        152,  154,  164,  167,  168,  169,  170,  171,  176,  186,  188,
        198,  203,  204,  205,  217,  218,  219,  241,  243,  244,  248,
        249,  250,  251,  252,  253,  254,  255,  271,  273,  281,  286,
        287,  288,  289,  290,  291,  292,  293,  294,  297,  302,  303,
        309,  310,  311,  312,  314,  327,  333,  337,  339,  341,  343,
        344,  345,  346,  348,  351,  352,  353,  355,  361,  362,  363,
        366,  368,  370,  376,  377,  378,  379,  380,  381,  383,  386,
        387,  388,  389,  390,  392,  396,  397,  398,  399,  400,  401,
        402,  405,  407,  408,  409,  419,  420,  421,  422,  423,  424,
        425,  434,  435,  436,  445,  448,  449,  450,  451,  452,  453,
        458,  459,  460,  469,  471,  472,  473,  475,  477,  482,  490,
        491,  493,  495,  496,  497,  498,  499,  501,  502,  505,  506,
        507,  519,  520,  521,  522,  523,  524,  525,  526,  527,  528,
        530,  531,  533,  540,  545,  555,  560,  561,  562,  574,  575,
        576,  577,  579,  585,  591,  592,  593,  594,  597,  598,  604,
        605,  609,  611,  619,  625,  626,  637,  640,  641,  642,  643,
        652,  656,  658,  668,  670,  672,  673,  676,  677,  678,  679,
        681,  682,  683,  685,  686,  690,  691,  693,  694,  696,  697,
        698,  700,  703,  704,  705,  706,  712,  713,  714,  715,  718,
        719,  720,  721,  722,  723,  725,  727,  730,  731,  732,  733,
        734,  735,  740,  741,  744,  745,  746,  748,  752,  753,  754,
        755,  757,  758,  759,  762,  766,  767,  768,  769,  771,  772,
        773,  776,  777,  779,  780,  781,  782,  791,  792,  805,  808,
        809,  810,  811,  819,  827,  828,  842,  852,  853,  854,  864,
        865,  894,  899,  903,  907,  916,  917,  918,  919,  936,  943,
        973,  974,  984,  985,  986,  987,  988,  989,  990, 1010, 1011,
       1012, 1013, 1014, 1019, 1020, 1025, 1044, 1045, 1051, 1052, 1054,
       1066, 1080, 1112, 1116, 1118, 1119, 1120, 1123, 1124, 1125, 1134,
       1136, 1137, 1138, 1139, 1141, 1142, 1145, 1146, 1149, 1150, 1152,
       1161, 1172, 1173, 1178, 1180, 1181, 1182, 1190, 1193, 1195, 1198,
       1202, 1203, 1206, 1209, 1215, 1216, 1217, 1218, 1219, 1222, 1223,
       1224, 1235, 1236, 1239, 1240, 1241, 1242, 1243, 1244, 1246, 1247,
       1248, 1251, 1252, 1253, 1256], dtype=int64),)
测试集中预测为舞弊样本的个数有： 401
朴素贝叶斯模型得到的AUC值： 0.6600758791770028
对连续型特征向量第9轮迭代。
检查输出概率的shape: (1257, 2)
最终连续特征向量和离散特征向量组成的总测试集，其最终预测对数概率pred_prob is:
 [[ -36.4774543    -0.39526197]
 [  -3.40979304   -0.50197921]
 [  -3.02782032   -0.55578882]
 ...
 [ -84.32559408   -3.08064432]
 [-319.8803004    -3.4877348 ]
 [  -0.36683516  -11.1930168 ]]
检查得到的对数概率shape中对应的分类标签： [-1  1]
朴素贝叶斯预测的结果是：
 [ 1  1  1 ...  1  1 -1]
朴素贝叶斯分类模型中，测试集中预测为舞弊样本的索引号是：
 (array([   9,   14,   15,   16,   17,   18,   19,   22,   25,   34,   44,
         48,   50,   51,   52,   60,   67,   68,   69,   80,   81,   82,
         83,   85,   89,   98,   99,  100,  101,  102,  115,  117,  118,
        119,  127,  129,  130,  131,  132,  139,  142,  154,  155,  156,
        157,  164,  167,  168,  169,  170,  171,  176,  186,  188,  198,
        203,  204,  217,  218,  219,  224,  227,  236,  243,  244,  248,
        249,  250,  251,  252,  254,  255,  257,  271,  273,  281,  286,
        290,  291,  292,  293,  297,  302,  309,  310,  311,  312,  314,
        327,  330,  331,  332,  341,  343,  344,  345,  348,  351,  352,
        353,  354,  355,  357,  361,  362,  366,  370,  376,  377,  378,
        379,  380,  381,  386,  387,  388,  389,  390,  391,  392,  396,
        397,  398,  399,  400,  401,  402,  403,  405,  407,  409,  419,
        420,  422,  423,  435,  436,  443,  448,  449,  450,  451,  452,
        453,  454,  457,  458,  459,  460,  469,  471,  472,  473,  475,
        482,  490,  493,  495,  496,  497,  498,  499,  501,  502,  505,
        506,  520,  521,  527,  528,  533,  540,  541,  545,  568,  574,
        575,  576,  591,  592,  593,  594,  598,  604,  609,  611,  619,
        633,  635,  637,  640,  641,  642,  643,  652,  656,  658,  668,
        669,  670,  672,  673,  676,  677,  678,  679,  681,  682,  683,
        684,  685,  690,  691,  693,  694,  696,  697,  698,  699,  700,
        703,  704,  705,  706,  712,  714,  715,  718,  719,  720,  721,
        722,  723,  725,  727,  731,  732,  733,  734,  735,  741,  744,
        745,  746,  752,  753,  754,  755,  757,  758,  759,  760,  761,
        766,  767,  768,  769,  770,  771,  773,  779,  780,  781,  782,
        791,  792,  808,  809,  810,  811,  819,  827,  828,  843,  852,
        853,  854,  864,  893,  906,  907,  919,  936,  943,  973,  974,
        984,  985,  986,  987,  988,  989,  990, 1010, 1011, 1012, 1014,
       1044, 1051, 1052, 1054, 1077, 1080, 1116, 1123, 1124, 1134, 1137,
       1138, 1139, 1141, 1145, 1146, 1150, 1152, 1173, 1178, 1180, 1181,
       1182, 1195, 1203, 1205, 1206, 1209, 1215, 1216, 1217, 1218, 1219,
       1222, 1223, 1224, 1235, 1236, 1237, 1238, 1239, 1240, 1241, 1242,
       1243, 1244, 1246, 1247, 1248, 1251, 1252, 1253, 1256], dtype=int64),)
测试集中预测为舞弊样本的个数有： 350
朴素贝叶斯模型得到的AUC值： 0.6805413687436159
对连续型特征向量第10轮迭代。
检查输出概率的shape: (1257, 2)
最终连续特征向量和离散特征向量组成的总测试集，其最终预测对数概率pred_prob is:
 [[ -31.50110097   -0.39526197]
 [  -4.51774412   -0.42925986]
 [  -4.08247476   -0.44830052]
 ...
 [ -85.50678171   -3.08064432]
 [-321.06235268   -3.4877348 ]
 [  -0.3669344   -10.02594103]]
检查得到的对数概率shape中对应的分类标签： [-1  1]
朴素贝叶斯预测的结果是：
 [ 1  1  1 ...  1  1 -1]
朴素贝叶斯分类模型中，测试集中预测为舞弊样本的索引号是：
 (array([  14,   15,   16,   17,   18,   22,   48,   50,   51,   67,   68,
         80,   81,   82,   85,   89,   98,   99,  100,  101,  115,  117,
        118,  119,  127,  130,  131,  142,  154,  155,  156,  157,  164,
        167,  168,  169,  170,  171,  176,  186,  198,  204,  217,  218,
        219,  224,  236,  243,  244,  248,  249,  250,  251,  254,  255,
        271,  273,  281,  283,  290,  291,  292,  293,  297,  302,  309,
        310,  311,  312,  314,  327,  330,  331,  332,  341,  342,  343,
        344,  345,  348,  351,  352,  353,  354,  355,  361,  362,  366,
        370,  376,  377,  378,  379,  381,  386,  387,  388,  389,  390,
        391,  392,  396,  397,  398,  399,  400,  401,  405,  407,  409,
        419,  420,  421,  422,  435,  443,  449,  450,  451,  452,  454,
        457,  458,  459,  469,  471,  472,  473,  475,  482,  493,  495,
        496,  497,  499,  501,  502,  504,  521,  528,  533,  540,  541,
        545,  574,  575,  576,  591,  592,  593,  594,  604,  609,  611,
        619,  633,  635,  637,  640,  641,  642,  643,  652,  656,  658,
        668,  669,  670,  672,  673,  676,  677,  678,  679,  681,  682,
        683,  685,  689,  690,  691,  693,  694,  696,  697,  699,  700,
        703,  704,  705,  706,  712,  713,  714,  715,  718,  719,  720,
        721,  722,  723,  725,  727,  731,  732,  734,  735,  744,  745,
        746,  752,  753,  754,  755,  757,  758,  759,  761,  766,  767,
        768,  769,  770,  771,  779,  780,  781,  782,  791,  792,  808,
        809,  819,  827,  828,  893,  905,  906,  907,  936,  973,  974,
        984,  985,  986,  987,  988,  989,  990,  996, 1010, 1011, 1012,
       1013, 1014, 1077, 1080, 1116, 1124, 1129, 1134, 1138, 1139, 1141,
       1145, 1146, 1150, 1152, 1164, 1178, 1181, 1203, 1205, 1206, 1215,
       1217, 1218, 1222, 1223, 1224, 1235, 1236, 1237, 1238, 1239, 1240,
       1241, 1242, 1243, 1246, 1247, 1248, 1251, 1252, 1253, 1256],
      dtype=int64),)
测试集中预测为舞弊样本的个数有： 296
朴素贝叶斯模型得到的AUC值： 0.7022107106376769
对连续型特征向量第11轮迭代。
检查输出概率的shape: (1257, 2)
最终连续特征向量和离散特征向量组成的总测试集，其最终预测对数概率pred_prob is:
 [[-3.35311165e+01 -3.95261973e-01]
 [-6.71118725e+00 -3.98997061e-01]
 [-6.15966450e+00 -4.01754629e-01]
 ...
 [-5.42806834e-02 -8.00926183e+00]
 [-3.23018700e+02 -3.48773480e+00]
 [-3.66934700e-01 -1.00238429e+01]]
检查得到的对数概率shape中对应的分类标签： [-1  1]
朴素贝叶斯预测的结果是：
 [ 1  1  1 ... -1  1 -1]
朴素贝叶斯分类模型中，测试集中预测为舞弊样本的索引号是：
 (array([  15,   16,   17,   18,   22,   29,   48,   50,   51,   52,   53,
         60,   67,   68,   71,   76,   80,   81,   82,   83,   84,   89,
         98,   99,  100,  101,  112,  115,  117,  121,  142,  164,  167,
        169,  170,  183,  198,  214,  217,  218,  219,  243,  244,  248,
        249,  250,  251,  271,  281,  282,  283,  290,  291,  292,  297,
        310,  314,  315,  316,  317,  319,  326,  327,  329,  331,  341,
        343,  344,  348,  362,  366,  370,  379,  381,  386,  389,  399,
        405,  448,  450,  451,  463,  469,  472,  473,  475,  481,  482,
        512,  514,  521,  528,  533,  540,  568,  574,  575,  579,  584,
        591,  592,  593,  594,  611,  614,  615,  620,  641,  643,  656,
        658,  668,  669,  672,  673,  676,  677,  678,  681,  682,  683,
        685,  689,  693,  694,  696,  697,  699,  700,  705,  706,  718,
        719,  722,  723,  731,  732,  734,  735,  744,  746,  753,  754,
        755,  759,  761,  766,  779,  780,  781,  792,  819,  877,  936,
        973,  974,  984,  987,  996, 1011, 1013, 1014, 1017, 1134, 1138,
       1139, 1150, 1152, 1178, 1203, 1222, 1224, 1235, 1236, 1238, 1246,
       1247, 1248, 1251, 1252, 1253, 1254, 1256], dtype=int64),)
测试集中预测为舞弊样本的个数有： 183
朴素贝叶斯模型得到的AUC值： 0.79341164453524
对连续型特征向量第12轮迭代。
检查输出概率的shape: (1257, 2)
最终连续特征向量和离散特征向量组成的总测试集，其最终预测对数概率pred_prob is:
 [[-3.45433225e+01 -3.95261973e-01]
 [-7.72970332e+00 -3.96609218e-01]
 [-7.17073873e+00 -3.97619307e-01]
 ...
 [-6.33273054e-02 -7.20479956e+00]
 [-3.23973873e+02 -3.48773480e+00]
 [-3.67047971e-01 -9.44476685e+00]]
检查得到的对数概率shape中对应的分类标签： [-1  1]
朴素贝叶斯预测的结果是：
 [ 1  1  1 ... -1  1 -1]
朴素贝叶斯分类模型中，测试集中预测为舞弊样本的索引号是：
 (array([  16,   22,   48,   50,   51,   67,   68,   71,   76,   84,   89,
         90,   98,   99,  101,  115,  117,  121,  183,  198,  214,  217,
        218,  219,  248,  249,  250,  251,  281,  282,  297,  314,  317,
        319,  327,  331,  344,  348,  366,  370,  379,  381,  386,  399,
        448,  451,  469,  472,  475,  481,  482,  483,  521,  528,  540,
        568,  575,  579,  611,  615,  635,  641,  656,  658,  668,  669,
        672,  673,  676,  677,  678,  681,  682,  683,  685,  693,  694,
        696,  697,  699,  700,  705,  706,  718,  722,  723,  735,  753,
        754,  759,  779,  780,  781,  792,  819,  936,  973,  987,  996,
       1013, 1014, 1138, 1139, 1150, 1152, 1222, 1235, 1236, 1246, 1247,
       1248, 1251, 1252, 1253, 1254, 1256], dtype=int64),)
测试集中预测为舞弊样本的个数有： 116
朴素贝叶斯模型得到的AUC值： 0.8202976798482416
对连续型特征向量第13轮迭代。
检查输出概率的shape: (1257, 2)
最终连续特征向量和离散特征向量组成的总测试集，其最终预测对数概率pred_prob is:
 [[-7.99664350e+00 -3.96293421e-01]
 [-8.74441380e+00 -3.95750150e-01]
 [-8.29349484e+00 -3.96028397e-01]
 ...
 [-9.75109933e-02 -6.09170281e+00]
 [-3.21948938e+02 -3.48773480e+00]
 [-3.67496311e-01 -8.43701125e+00]]
检查得到的对数概率shape中对应的分类标签： [-1  1]
朴素贝叶斯预测的结果是：
 [ 1  1  1 ... -1  1 -1]
朴素贝叶斯分类模型中，测试集中预测为舞弊样本的索引号是：
 (array([  48,   67,   76,   84,   89,   98,   99,  114,  121,  164,  198,
        217,  218,  219,  249,  251,  272,  281,  297,  314,  317,  318,
        319,  327,  331,  366,  370,  381,  469,  481,  482,  521,  540,
        543,  579,  611,  615,  641,  656,  658,  668,  672,  673,  676,
        685,  693,  694,  697,  700,  718,  722,  754,  779,  780,  819,
        936,  973, 1013, 1014, 1138, 1139, 1152, 1222, 1224, 1235, 1246,
       1247, 1248, 1251, 1252, 1253, 1254, 1256], dtype=int64),)
测试集中预测为舞弊样本的个数有： 73
朴素贝叶斯模型得到的AUC值： 0.8375528965416605
对连续型特征向量第14轮迭代。
检查输出概率的shape: (1257, 2)
最终连续特征向量和离散特征向量组成的总测试集，其最终预测对数概率pred_prob is:
 [[-7.51512746e+00 -3.96931932e-01]
 [-8.28653078e+00 -3.96033755e-01]
 [-7.81051216e+00 -3.96504567e-01]
 ...
 [-2.94710412e-01 -4.59750264e+00]
 [-3.10470514e-02 -5.24515631e+03]
 [-3.69661922e-01 -7.03507871e+00]]
检查得到的对数概率shape中对应的分类标签： [-1  1]
朴素贝叶斯预测的结果是：
 [ 1  1  1 ... -1 -1 -1]
朴素贝叶斯分类模型中，测试集中预测为舞弊样本的索引号是：
 (array([  16,   48,   67,   76,   84,   89,   98,   99,  101,  114,  164,
        198,  217,  218,  219,  240,  249,  250,  251,  272,  281,  297,
        314,  317,  318,  319,  327,  331,  366,  370,  381,  389,  399,
        448,  469,  481,  482,  521,  540,  543,  575,  579,  611,  615,
        641,  656,  658,  668,  672,  673,  676,  682,  685,  689,  693,
        694,  697,  700,  718,  722,  730,  735,  753,  754,  759,  779,
        780,  819,  936,  973,  996, 1013, 1014, 1138, 1139, 1150, 1152,
       1222, 1224, 1235, 1246, 1247, 1248, 1251, 1252, 1253, 1254, 1255,
       1256], dtype=int64),)
测试集中预测为舞弊样本的个数有： 89
朴素贝叶斯模型得到的AUC值： 0.8769881803589669
对连续型特征向量第15轮迭代。
检查输出概率的shape: (1257, 2)
最终连续特征向量和离散特征向量组成的总测试集，其最终预测对数概率pred_prob is:
 [[-9.18660739e+00 -3.95575661e-01]
 [-9.95834971e+00 -3.95406950e-01]
 [-9.48292807e+00 -3.95495206e-01]
 ...
 [-8.22457454e-01 -3.69775924e+00]
 [-3.10470514e-02 -5.24355906e+03]
 [-3.76382347e-01 -5.83237524e+00]]
检查得到的对数概率shape中对应的分类标签： [-1  1]
朴素贝叶斯预测的结果是：
 [ 1  1  1 ... -1 -1 -1]
朴素贝叶斯分类模型中，测试集中预测为舞弊样本的索引号是：
 (array([  48,   67,   89,   90,   98,   99,  107,  108,  109,  110,  114,
        187,  217,  218,  219,  240,  270,  281,  296,  297,  298,  299,
        314,  319,  331,  332,  366,  370,  381,  469,  481,  483,  521,
        540,  615,  641,  656,  657,  658,  674,  685,  693,  694,  695,
        718,  779,  792,  906,  936,  973, 1138, 1152, 1222, 1234, 1246,
       1247, 1248, 1251, 1252, 1253, 1254, 1255, 1256], dtype=int64),)
测试集中预测为舞弊样本的个数有： 63
朴素贝叶斯模型得到的AUC值： 0.8874215671968481
对连续型特征向量第16轮迭代。
检查输出概率的shape: (1257, 2)
最终连续特征向量和离散特征向量组成的总测试集，其最终预测对数概率pred_prob is:
 [[-9.36961609e+00 -3.95523193e-01]
 [-1.01255205e+01 -3.95384630e-01]
 [-9.65898080e+00 -3.95457552e-01]
 ...
 [-9.19798076e-01 -3.62156563e+00]
 [-3.10470514e-02 -5.24342767e+03]
 [-3.77933543e-01 -5.68325029e+00]]
检查得到的对数概率shape中对应的分类标签： [-1  1]
朴素贝叶斯预测的结果是：
 [ 1  1  1 ... -1 -1 -1]
朴素贝叶斯分类模型中，测试集中预测为舞弊样本的索引号是：
 (array([  48,   67,   90,   98,   99,  107,  108,  109,  110,  114,  187,
        217,  218,  219,  240,  270,  281,  296,  297,  298,  299,  314,
        319,  331,  332,  366,  370,  381,  469,  481,  483,  521,  540,
        615,  641,  656,  657,  658,  674,  685,  693,  694,  695,  718,
        779,  906,  936,  973, 1138, 1152, 1222, 1234, 1246, 1247, 1248,
       1251, 1252, 1253, 1254, 1255, 1256], dtype=int64),)
测试集中预测为舞弊样本的个数有： 61
朴素贝叶斯模型得到的AUC值： 0.8882241354151467
对连续型特征向量第17轮迭代。
检查输出概率的shape: (1257, 2)
最终连续特征向量和离散特征向量组成的总测试集，其最终预测对数概率pred_prob is:
 [[-6.21115635e+00 -4.01427765e-01]
 [-7.01361655e+00 -3.98020934e-01]
 [-6.53657978e+00 -3.99711223e-01]
 ...
 [-1.09641932e-01 -5.88239867e+00]
 [-3.10470514e-02 -5.24667409e+03]
 [-3.67296871e-01 -8.76888504e+00]]
检查得到的对数概率shape中对应的分类标签： [-1  1]
朴素贝叶斯预测的结果是：
 [ 1  1  1 ... -1 -1 -1]
朴素贝叶斯分类模型中，测试集中预测为舞弊样本的索引号是：
 (array([  10,   11,   12,   13,   14,   15,   16,   17,   19,   22,   48,
         50,   51,   52,   57,   58,   59,   60,   67,   71,   80,   81,
         82,   83,   84,   85,   89,   90,   91,   92,   99,  107,  108,
        109,  110,  115,  117,  121,  164,  165,  166,  168,  186,  187,
        198,  199,  214,  217,  218,  219,  239,  240,  241,  248,  249,
        250,  251,  259,  270,  271,  272,  281,  282,  283,  290,  291,
        292,  296,  297,  298,  299,  309,  310,  311,  312,  314,  315,
        317,  318,  319,  326,  327,  329,  330,  331,  332,  341,  343,
        344,  362,  366,  367,  381,  386,  389,  390,  405,  431,  443,
        448,  451,  469,  472,  473,  474,  475,  481,  483,  484,  485,
        492,  493,  516,  528,  533,  543,  568,  574,  575,  579,  582,
        584,  592,  593,  594,  611,  620,  635,  637,  656,  657,  658,
        661,  663,  664,  668,  669,  672,  673,  674,  676,  677,  678,
        681,  682,  683,  685,  689,  690,  693,  694,  695,  696,  697,
        699,  700,  705,  706,  716,  717,  718,  730,  731,  734,  735,
        736,  746,  753,  754,  755,  756,  759,  766,  779,  780,  781,
        877,  878,  936,  973,  974,  987,  989,  996, 1013, 1014, 1138,
       1139, 1150, 1178, 1196, 1203, 1222, 1224, 1234, 1237, 1238, 1246,
       1248, 1251, 1252, 1253, 1254, 1255, 1256], dtype=int64),)
测试集中预测为舞弊样本的个数有： 205
朴素贝叶斯模型得到的AUC值： 0.784583394133956
对连续型特征向量第18轮迭代。
检查输出概率的shape: (1257, 2)
最终连续特征向量和离散特征向量组成的总测试集，其最终预测对数概率pred_prob is:
 [[-6.62027139e+00 -3.99353283e-01]
 [-7.22778112e+00 -3.97488457e-01]
 [-6.86807765e+00 -3.98453851e-01]
 ...
 [-1.46969941e-01 -5.43326864e+00]
 [-3.10470514e-02 -5.24617322e+03]
 [-3.67626422e-01 -8.26793051e+00]]
检查得到的对数概率shape中对应的分类标签： [-1  1]
朴素贝叶斯预测的结果是：
 [ 1  1  1 ... -1 -1 -1]
朴素贝叶斯分类模型中，测试集中预测为舞弊样本的索引号是：
 (array([  10,   11,   12,   14,   16,   17,   22,   48,   50,   51,   52,
         57,   58,   59,   60,   67,   71,   80,   81,   82,   84,   89,
         90,   91,   92,   99,  107,  108,  109,  110,  117,  121,  164,
        165,  166,  168,  187,  198,  199,  217,  218,  219,  239,  240,
        248,  249,  250,  251,  270,  271,  272,  281,  282,  283,  290,
        292,  296,  297,  298,  299,  309,  312,  314,  315,  317,  318,
        319,  326,  327,  329,  330,  331,  332,  343,  344,  366,  367,
        381,  386,  389,  390,  405,  443,  448,  451,  469,  472,  473,
        474,  475,  481,  483,  484,  485,  492,  493,  528,  533,  543,
        568,  574,  575,  576,  579,  582,  584,  611,  635,  637,  656,
        657,  658,  668,  669,  672,  673,  674,  676,  677,  678,  681,
        682,  683,  685,  689,  693,  694,  695,  696,  697,  699,  700,
        705,  706,  716,  717,  718,  730,  731,  734,  735,  736,  746,
        753,  754,  755,  756,  759,  766,  779,  780,  781,  878,  936,
        973,  987,  996, 1013, 1014, 1138, 1139, 1150, 1178, 1196, 1203,
       1222, 1224, 1234, 1248, 1251, 1252, 1253, 1254, 1255, 1256],
      dtype=int64),)
测试集中预测为舞弊样本的个数有： 175
朴素贝叶斯模型得到的AUC值： 0.7507660878447395
对连续型特征向量第19轮迭代。
检查输出概率的shape: (1257, 2)
最终连续特征向量和离散特征向量组成的总测试集，其最终预测对数概率pred_prob is:
 [[-6.83797484e+00 -3.98551557e-01]
 [-7.44059844e+00 -3.97061264e-01]
 [-7.07693554e+00 -3.97851436e-01]
 ...
 [-2.05543713e-01 -5.00069802e+00]
 [-3.10470514e-02 -5.24574399e+03]
 [-3.68114738e-01 -7.80819956e+00]]
检查得到的对数概率shape中对应的分类标签： [-1  1]
朴素贝叶斯预测的结果是：
 [ 1  1  1 ... -1 -1 -1]
朴素贝叶斯分类模型中，测试集中预测为舞弊样本的索引号是：
 (array([  10,   11,   12,   16,   17,   22,   48,   50,   57,   58,   59,
         60,   71,   80,   82,   84,   89,   90,   91,   92,   99,  107,
        108,  109,  110,  121,  164,  165,  166,  168,  187,  198,  217,
        218,  219,  239,  240,  248,  249,  250,  251,  270,  271,  272,
        281,  282,  296,  297,  298,  299,  312,  314,  315,  317,  318,
        319,  326,  327,  329,  330,  331,  332,  366,  367,  381,  386,
        389,  390,  443,  448,  451,  469,  472,  473,  474,  475,  481,
        482,  483,  484,  485,  492,  493,  528,  533,  543,  568,  574,
        575,  576,  579,  582,  584,  611,  635,  656,  657,  658,  672,
        673,  674,  676,  677,  678,  681,  682,  683,  685,  689,  693,
        694,  695,  696,  697,  699,  700,  705,  706,  716,  717,  718,
        730,  731,  735,  736,  753,  754,  759,  766,  779,  780,  781,
        936,  973,  987,  996, 1013, 1014, 1138, 1139, 1150, 1196, 1203,
       1222, 1224, 1234, 1248, 1251, 1252, 1253, 1254, 1255, 1256],
      dtype=int64),)
测试集中预测为舞弊样本的个数有： 153
朴素贝叶斯模型得到的AUC值： 0.7595943382460236
对连续型特征向量第20轮迭代。
检查输出概率的shape: (1257, 2)
最终连续特征向量和离散特征向量组成的总测试集，其最终预测对数概率pred_prob is:
 [[-7.17985500e+00 -3.97597890e-01]
 [-7.73010576e+00 -3.96608676e-01]
 [-7.39343752e+00 -3.97148235e-01]
 ...
 [-2.62305780e-01 -4.72213685e+00]
 [-3.10470514e-02 -5.24545834e+03]
 [-3.68606139e-01 -7.49288319e+00]]
检查得到的对数概率shape中对应的分类标签： [-1  1]
朴素贝叶斯预测的结果是：
 [ 1  1  1 ... -1 -1 -1]
朴素贝叶斯分类模型中，测试集中预测为舞弊样本的索引号是：
 (array([  10,   12,   16,   22,   48,   57,   58,   84,   89,   90,   91,
         92,   99,  107,  108,  109,  110,  121,  165,  166,  168,  187,
        198,  217,  218,  219,  239,  240,  248,  249,  250,  251,  270,
        271,  272,  281,  296,  297,  298,  299,  314,  317,  318,  319,
        326,  327,  330,  366,  381,  386,  389,  390,  448,  451,  472,
        473,  481,  482,  483,  484,  485,  492,  493,  533,  543,  568,
        574,  575,  576,  579,  584,  611,  635,  656,  657,  658,  672,
        673,  674,  676,  677,  681,  682,  683,  685,  689,  693,  694,
        695,  697,  699,  700,  705,  716,  717,  718,  730,  731,  735,
        736,  753,  754,  759,  779,  780,  781,  936,  973,  987,  996,
       1013, 1014, 1138, 1139, 1150, 1196, 1203, 1222, 1224, 1234, 1248,
       1251, 1252, 1253, 1254, 1255, 1256], dtype=int64),)
测试集中预测为舞弊样本的个数有： 127
朴素贝叶斯模型得到的AUC值： 0.7700277250839049
对连续型特征向量第21轮迭代。
检查输出概率的shape: (1257, 2)
最终连续特征向量和离散特征向量组成的总测试集，其最终预测对数概率pred_prob is:
 [[-3.61498758e+00 -4.81302265e-01]
 [-4.14686332e+00 -4.44909537e-01]
 [-3.85845847e+00 -4.62071928e-01]
 ...
 [-6.84960061e-02 -6.93208405e+00]
 [-3.10470514e-02 -2.19234751e+02]
 [-3.72453619e-01 -6.35733839e+00]]
检查得到的对数概率shape中对应的分类标签： [-1  1]
朴素贝叶斯预测的结果是：
 [ 1  1  1 ... -1 -1 -1]
朴素贝叶斯分类模型中，测试集中预测为舞弊样本的索引号是：
 (array([   9,   10,   11,   12,   13,   14,   15,   16,   17,   18,   19,
         20,   21,   22,   24,   29,   33,   34,   45,   48,   49,   50,
         51,   52,   53,   55,   57,   58,   59,   60,   61,   65,   66,
         67,   68,   69,   71,   76,   77,   80,   81,   82,   83,   84,
         85,   86,   89,   90,   91,   92,   93,   98,   99,  100,  101,
        103,  107,  108,  109,  110,  112,  113,  115,  117,  121,  122,
        126,  127,  129,  130,  131,  138,  141,  149,  150,  154,  155,
        156,  157,  164,  165,  166,  167,  168,  169,  170,  171,  176,
        177,  178,  182,  183,  185,  186,  187,  188,  198,  199,  200,
        201,  202,  203,  204,  213,  214,  217,  218,  219,  221,  222,
        224,  225,  229,  230,  231,  232,  233,  236,  239,  240,  241,
        242,  243,  244,  245,  246,  248,  249,  250,  251,  252,  256,
        257,  258,  259,  260,  261,  263,  265,  267,  268,  270,  271,
        272,  273,  274,  281,  282,  283,  284,  285,  290,  291,  292,
        293,  296,  297,  298,  299,  300,  301,  302,  309,  310,  311,
        312,  314,  315,  316,  317,  318,  319,  320,  326,  327,  328,
        329,  330,  331,  332,  338,  339,  341,  342,  343,  344,  345,
        346,  350,  351,  352,  353,  354,  355,  356,  361,  362,  366,
        367,  368,  369,  370,  371,  376,  377,  378,  379,  381,  386,
        387,  388,  389,  390,  391,  396,  397,  400,  401,  402,  405,
        407,  409,  410,  419,  420,  421,  422,  423,  429,  430,  431,
        432,  434,  435,  443,  448,  449,  450,  451,  452,  453,  454,
        457,  458,  459,  460,  463,  468,  469,  470,  472,  473,  474,
        475,  481,  482,  483,  484,  485,  492,  493,  495,  496,  497,
        500,  503,  504,  512,  513,  514,  515,  516,  522,  526,  527,
        528,  529,  532,  533,  534,  541,  543,  544,  545,  555,  568,
        569,  570,  571,  572,  573,  574,  575,  576,  579,  580,  581,
        582,  584,  591,  592,  593,  594,  596,  598,  599,  604,  609,
        610,  611,  612,  613,  614,  615,  616,  617,  619,  620,  624,
        633,  634,  635,  637,  640,  641,  642,  643,  651,  652,  653,
        656,  657,  658,  660,  661,  662,  663,  664,  665,  666,  667,
        668,  669,  670,  671,  672,  673,  674,  676,  677,  678,  679,
        680,  681,  682,  683,  684,  685,  689,  690,  692,  693,  694,
        695,  696,  697,  698,  699,  700,  701,  703,  704,  705,  706,
        712,  713,  714,  715,  716,  717,  718,  719,  722,  723,  725,
        727,  730,  731,  732,  733,  734,  735,  736,  742,  744,  745,
        746,  750,  751,  752,  753,  754,  755,  756,  757,  758,  759,
        760,  761,  762,  766,  767,  768,  769,  770,  771,  775,  779,
        780,  781,  782,  808,  809,  811,  812,  815,  819,  825,  827,
        828,  831,  864,  877,  878,  893,  902,  905,  906,  917,  918,
        936,  943,  951,  952,  973,  974,  976,  984,  986,  987,  988,
        989,  990,  996, 1010, 1011, 1012, 1013, 1014, 1017, 1019, 1020,
       1052, 1077, 1080, 1116, 1117, 1119, 1121, 1123, 1124, 1129, 1132,
       1134, 1135, 1137, 1138, 1139, 1141, 1143, 1144, 1145, 1146, 1150,
       1152, 1153, 1156, 1158, 1161, 1164, 1165, 1167, 1171, 1173, 1175,
       1176, 1178, 1180, 1181, 1182, 1183, 1187, 1189, 1190, 1194, 1195,
       1196, 1202, 1203, 1204, 1205, 1206, 1215, 1216, 1217, 1218, 1220,
       1222, 1223, 1224, 1234, 1235, 1236, 1237, 1238, 1239, 1240, 1241,
       1242, 1243, 1244, 1246, 1247, 1248, 1249, 1251, 1252, 1253, 1254,
       1255, 1256], dtype=int64),)
测试集中预测为舞弊样本的个数有： 541
朴素贝叶斯模型得到的AUC值： 0.7414635925871882
对连续型特征向量第22轮迭代。
检查输出概率的shape: (1257, 2)
最终连续特征向量和离散特征向量组成的总测试集，其最终预测对数概率pred_prob is:
 [[-4.23813220e+00 -4.40479415e-01]
 [-4.35189131e+00 -4.35518042e-01]
 [-4.28910675e+00 -4.38183295e-01]
 ...
 [-9.15368855e-02 -6.21466016e+00]
 [-3.10470514e-02 -2.18624949e+02]
 [-3.78069727e-01 -5.67117107e+00]]
检查得到的对数概率shape中对应的分类标签： [-1  1]
朴素贝叶斯预测的结果是：
 [ 1  1  1 ... -1 -1 -1]
朴素贝叶斯分类模型中，测试集中预测为舞弊样本的索引号是：
 (array([   9,   10,   11,   12,   13,   14,   15,   16,   17,   18,   19,
         21,   22,   24,   29,   34,   45,   48,   49,   50,   51,   52,
         53,   55,   57,   58,   59,   60,   61,   65,   67,   68,   71,
         76,   80,   81,   82,   83,   84,   85,   86,   89,   90,   91,
         92,   98,   99,  100,  101,  103,  107,  108,  109,  110,  112,
        115,  117,  121,  122,  127,  131,  134,  150,  157,  164,  165,
        166,  167,  168,  169,  170,  171,  173,  174,  175,  177,  178,
        183,  185,  186,  187,  188,  198,  199,  200,  201,  202,  203,
        204,  213,  214,  217,  218,  219,  222,  225,  229,  231,  232,
        233,  239,  240,  241,  242,  243,  244,  248,  249,  250,  251,
        252,  256,  257,  258,  259,  260,  263,  270,  271,  272,  273,
        274,  281,  282,  283,  290,  291,  292,  296,  297,  298,  299,
        300,  301,  309,  310,  311,  312,  314,  315,  316,  317,  318,
        319,  320,  326,  327,  329,  330,  331,  332,  341,  342,  343,
        344,  345,  346,  350,  351,  352,  356,  361,  362,  366,  367,
        368,  369,  370,  371,  376,  377,  378,  379,  381,  386,  387,
        388,  389,  390,  396,  400,  405,  406,  407,  409,  410,  419,
        420,  421,  422,  423,  429,  430,  431,  432,  435,  443,  448,
        450,  451,  452,  453,  454,  457,  458,  459,  460,  463,  468,
        469,  470,  472,  473,  474,  475,  481,  482,  483,  484,  485,
        492,  493,  495,  497,  500,  512,  513,  514,  516,  526,  527,
        528,  529,  532,  533,  541,  543,  544,  555,  568,  569,  570,
        571,  574,  575,  576,  579,  580,  581,  582,  584,  591,  592,
        593,  594,  596,  597,  598,  599,  604,  608,  609,  610,  611,
        612,  613,  615,  616,  620,  624,  632,  633,  634,  635,  637,
        640,  641,  643,  651,  652,  653,  656,  657,  658,  660,  661,
        662,  663,  664,  665,  667,  668,  669,  670,  671,  672,  673,
        674,  676,  677,  678,  679,  680,  681,  682,  683,  684,  685,
        689,  690,  692,  693,  694,  695,  696,  697,  698,  699,  700,
        701,  702,  703,  704,  705,  706,  714,  715,  716,  717,  718,
        719,  725,  726,  727,  730,  731,  732,  733,  734,  735,  736,
        737,  738,  742,  744,  745,  746,  747,  750,  751,  752,  753,
        754,  755,  756,  757,  758,  759,  760,  761,  762,  764,  766,
        767,  768,  769,  770,  771,  779,  780,  781,  782,  808,  809,
        827,  828,  831,  877,  878,  893,  899,  917,  936,  951,  973,
        974,  983,  984,  986,  987,  988,  989,  990,  996, 1010, 1011,
       1012, 1013, 1014, 1017, 1019, 1044, 1052, 1053, 1054, 1080, 1087,
       1116, 1119, 1120, 1124, 1129, 1134, 1138, 1139, 1143, 1145, 1147,
       1150, 1152, 1153, 1155, 1158, 1161, 1164, 1165, 1171, 1173, 1175,
       1176, 1178, 1181, 1182, 1184, 1187, 1189, 1190, 1194, 1195, 1196,
       1202, 1203, 1205, 1206, 1215, 1216, 1217, 1218, 1219, 1222, 1223,
       1224, 1234, 1235, 1236, 1237, 1238, 1239, 1240, 1241, 1242, 1243,
       1246, 1248, 1249, 1251, 1252, 1253, 1254, 1255, 1256], dtype=int64),)
测试集中预测为舞弊样本的个数有： 471
朴素贝叶斯模型得到的AUC值： 0.7236976506639428
对连续型特征向量第23轮迭代。
检查输出概率的shape: (1257, 2)
最终连续特征向量和离散特征向量组成的总测试集，其最终预测对数概率pred_prob is:
 [[-1.82456954 -1.07646741]
 [-1.8511813  -1.05115322]
 [-1.83536677 -1.0660378 ]
 ...
 [-0.47928395 -4.12771781]
 [-0.41949726 -4.62127084]
 [-0.72268568 -2.38655493]]
检查得到的对数概率shape中对应的分类标签： [-1  1]
朴素贝叶斯预测的结果是：
 [ 1  1  1 ... -1 -1 -1]
朴素贝叶斯分类模型中，测试集中预测为舞弊样本的索引号是：
 (array([   5,    6,    7,    8,    9,   10,   11,   12,   13,   14,   15,
         16,   17,   18,   19,   20,   21,   22,   24,   25,   26,   27,
         28,   29,   30,   31,   32,   33,   34,   35,   41,   42,   43,
         44,   45,   46,   48,   49,   50,   51,   52,   53,   54,   55,
         57,   58,   59,   60,   61,   63,   64,   65,   66,   67,   68,
         69,   70,   71,   73,   74,   76,   77,   80,   81,   82,   83,
         84,   89,   90,   91,   92,   93,   98,   99,  100,  101,  102,
        103,  104,  107,  108,  109,  110,  111,  112,  113,  114,  115,
        116,  117,  118,  119,  120,  121,  122,  123,  124,  125,  126,
        127,  128,  129,  130,  131,  132,  133,  134,  137,  138,  139,
        140,  141,  142,  147,  148,  149,  150,  151,  152,  153,  154,
        155,  156,  157,  164,  165,  166,  167,  168,  169,  170,  171,
        174,  175,  176,  177,  178,  179,  180,  181,  182,  183,  184,
        185,  186,  187,  188,  189,  190,  191,  194,  196,  198,  199,
        200,  201,  202,  203,  204,  205,  213,  214,  215,  217,  218,
        219,  220,  221,  222,  223,  224,  225,  226,  227,  228,  229,
        230,  231,  232,  233,  234,  235,  236,  237,  239,  240,  241,
        242,  243,  244,  245,  246,  247,  248,  249,  250,  251,  252,
        253,  254,  255,  256,  257,  258,  259,  260,  261,  262,  263,
        264,  265,  267,  268,  270,  271,  272,  273,  274,  275,  281,
        282,  283,  284,  285,  286,  287,  288,  289,  290,  291,  292,
        293,  294,  295,  296,  297,  298,  299,  300,  301,  302,  304,
        309,  310,  311,  312,  313,  314,  315,  316,  317,  318,  319,
        320,  326,  327,  328,  329,  330,  331,  332,  338,  339,  341,
        342,  343,  344,  345,  346,  347,  348,  349,  350,  351,  352,
        353,  354,  355,  356,  357,  359,  361,  362,  363,  366,  367,
        368,  369,  370,  371,  372,  373,  374,  376,  377,  378,  379,
        380,  381,  386,  387,  388,  389,  390,  391,  392,  396,  397,
        398,  399,  400,  401,  402,  403,  404,  405,  406,  407,  408,
        409,  410,  411,  412,  413,  414,  419,  420,  421,  422,  423,
        424,  425,  429,  430,  431,  432,  433,  434,  435,  436,  437,
        438,  443,  444,  445,  446,  448,  449,  450,  451,  452,  453,
        454,  455,  456,  458,  459,  460,  461,  462,  463,  464,  465,
        466,  467,  468,  469,  470,  471,  472,  473,  474,  475,  481,
        482,  483,  484,  485,  487,  488,  489,  490,  491,  492,  493,
        494,  495,  496,  497,  498,  499,  500,  501,  502,  503,  504,
        505,  506,  507,  508,  509,  510,  511,  512,  513,  514,  515,
        516,  522,  523,  524,  525,  526,  527,  528,  529,  530,  531,
        532,  533,  534,  535,  541,  543,  544,  545,  550,  551,  552,
        553,  554,  555,  560,  561,  562,  563,  568,  569,  570,  571,
        572,  573,  574,  575,  576,  577,  579,  580,  581,  582,  583,
        584,  585,  586,  587,  591,  592,  593,  594,  595,  596,  597,
        598,  599,  604,  608,  609,  610,  611,  612,  613,  614,  615,
        616,  617,  619,  620,  621,  622,  624,  625,  626,  632,  633,
        634,  635,  640,  641,  642,  643,  651,  652,  653,  654,  656,
        657,  658,  659,  660,  661,  662,  663,  664,  665,  666,  667,
        668,  669,  670,  671,  672,  673,  674,  676,  677,  678,  679,
        680,  681,  682,  683,  684,  685,  689,  690,  691,  692,  693,
        694,  695,  696,  697,  698,  699,  700,  701,  702,  703,  704,
        705,  706,  712,  713,  714,  715,  716,  717,  718,  719,  720,
        721,  722,  723,  725,  726,  727,  729,  730,  731,  732,  733,
        734,  735,  736,  737,  738,  739,  740,  741,  742,  743,  744,
        745,  746,  747,  748,  749,  750,  751,  752,  753,  754,  755,
        756,  757,  758,  759,  760,  761,  762,  763,  764,  765,  766,
        767,  768,  769,  770,  771,  772,  773,  774,  775,  779,  780,
        781,  782,  786,  787,  788,  789,  790,  791,  803,  804,  805,
        806,  807,  808,  809,  810,  811,  812,  813,  815,  817,  819,
        824,  825,  827,  828,  829,  831,  841,  842,  843,  844,  850,
        851,  852,  853,  854,  860,  861,  862,  864,  865,  877,  878,
        889,  890,  892,  893,  894,  899,  902,  905,  906,  907,  915,
        917,  918,  919,  925,  930,  936,  942,  943,  945,  951,  952,
        962,  970,  971,  973,  974,  975,  976,  983,  984,  985,  986,
        987,  988,  989,  990,  994,  996,  997,  998, 1010, 1011, 1012,
       1013, 1014, 1015, 1016, 1017, 1018, 1019, 1020, 1023, 1024, 1025,
       1049, 1051, 1052, 1053, 1054, 1055, 1056, 1066, 1067, 1077, 1078,
       1080, 1087, 1089, 1103, 1104, 1105, 1106, 1107, 1110, 1111, 1112,
       1113, 1114, 1115, 1116, 1117, 1118, 1119, 1120, 1121, 1122, 1123,
       1124, 1125, 1127, 1129, 1132, 1134, 1135, 1136, 1137, 1138, 1139,
       1140, 1141, 1142, 1143, 1144, 1145, 1146, 1148, 1150, 1152, 1153,
       1155, 1156, 1158, 1161, 1162, 1164, 1165, 1167, 1171, 1172, 1173,
       1175, 1176, 1177, 1178, 1179, 1180, 1181, 1182, 1183, 1184, 1186,
       1187, 1189, 1190, 1191, 1192, 1193, 1194, 1195, 1196, 1198, 1199,
       1200, 1202, 1203, 1204, 1205, 1206, 1208, 1209, 1214, 1215, 1216,
       1217, 1218, 1219, 1220, 1222, 1223, 1224, 1227, 1228, 1229, 1231,
       1234, 1235, 1236, 1237, 1238, 1239, 1240, 1241, 1242, 1243, 1244,
       1246, 1247, 1248, 1249, 1251, 1252, 1253, 1254, 1255, 1256],
      dtype=int64),)
测试集中预测为舞弊样本的个数有： 835
朴素贝叶斯模型得到的AUC值： 0.6234860644973004
对连续型特征向量第24轮迭代。
检查输出概率的shape: (1257, 2)
最终连续特征向量和离散特征向量组成的总测试集，其最终预测对数概率pred_prob is:
 [[-1.80171371 -1.09929616]
 [-1.80857468 -1.09233327]
 [-1.82112388 -1.07984275]
 ...
 [-0.60568094 -3.92921389]
 [-0.56167423 -4.37503953]
 [-0.91005896 -2.05027898]]
检查得到的对数概率shape中对应的分类标签： [-1  1]
朴素贝叶斯预测的结果是：
 [ 1  1  1 ... -1 -1 -1]
朴素贝叶斯分类模型中，测试集中预测为舞弊样本的索引号是：
 (array([   5,    6,    7,    8,    9,   10,   11,   12,   13,   14,   15,
         16,   17,   18,   19,   20,   21,   22,   24,   25,   26,   27,
         28,   29,   30,   31,   32,   33,   34,   35,   41,   42,   43,
         44,   45,   46,   48,   49,   50,   51,   52,   53,   54,   55,
         56,   57,   58,   59,   60,   61,   62,   63,   64,   65,   66,
         67,   68,   69,   70,   71,   72,   76,   77,   80,   81,   82,
         83,   84,   89,   90,   91,   92,   93,   98,   99,  100,  101,
        102,  103,  104,  107,  108,  109,  110,  111,  112,  113,  114,
        115,  116,  117,  118,  119,  120,  121,  122,  123,  124,  125,
        126,  127,  128,  129,  130,  131,  132,  133,  134,  137,  138,
        139,  140,  141,  142,  147,  148,  149,  150,  151,  152,  153,
        154,  155,  156,  157,  164,  165,  166,  167,  168,  169,  170,
        171,  174,  175,  176,  177,  178,  179,  180,  181,  182,  183,
        184,  185,  186,  187,  188,  189,  190,  191,  194,  195,  196,
        197,  198,  199,  200,  201,  202,  203,  204,  205,  213,  214,
        215,  217,  218,  219,  220,  221,  222,  223,  224,  225,  226,
        227,  228,  229,  230,  231,  232,  233,  234,  235,  236,  237,
        239,  240,  241,  242,  243,  244,  245,  246,  247,  248,  249,
        250,  251,  252,  253,  254,  255,  256,  257,  258,  259,  260,
        261,  262,  263,  264,  265,  266,  267,  268,  270,  271,  272,
        273,  274,  275,  281,  282,  283,  284,  285,  286,  287,  288,
        289,  290,  291,  292,  293,  294,  295,  296,  297,  298,  299,
        300,  301,  302,  304,  309,  310,  311,  312,  313,  314,  315,
        316,  317,  318,  319,  320,  326,  327,  328,  329,  330,  331,
        332,  338,  339,  341,  342,  343,  344,  345,  346,  347,  348,
        350,  351,  352,  353,  354,  355,  356,  357,  361,  362,  363,
        366,  367,  368,  369,  370,  371,  372,  373,  374,  376,  377,
        378,  379,  380,  381,  386,  387,  388,  389,  390,  391,  392,
        396,  397,  398,  399,  400,  401,  402,  403,  404,  405,  406,
        407,  408,  409,  410,  411,  412,  413,  414,  419,  420,  421,
        422,  423,  424,  425,  429,  430,  431,  432,  433,  434,  435,
        436,  437,  438,  443,  444,  445,  446,  447,  448,  449,  450,
        451,  452,  453,  454,  455,  456,  458,  459,  460,  461,  462,
        463,  464,  465,  466,  467,  468,  469,  470,  471,  472,  473,
        474,  475,  481,  482,  483,  484,  485,  487,  488,  489,  490,
        491,  492,  493,  494,  495,  496,  497,  498,  499,  500,  501,
        502,  503,  504,  505,  506,  507,  508,  509,  510,  511,  512,
        513,  514,  515,  516,  522,  523,  524,  525,  526,  527,  528,
        529,  530,  531,  532,  533,  534,  535,  541,  543,  544,  545,
        550,  551,  552,  553,  554,  555,  560,  561,  562,  563,  568,
        569,  570,  571,  572,  573,  574,  575,  576,  577,  579,  580,
        581,  582,  583,  584,  585,  586,  587,  591,  592,  593,  594,
        595,  596,  597,  598,  599,  604,  608,  609,  610,  611,  612,
        613,  614,  615,  616,  617,  618,  619,  620,  621,  622,  624,
        625,  626,  632,  633,  634,  635,  637,  639,  640,  641,  642,
        643,  651,  652,  653,  654,  656,  657,  658,  659,  660,  661,
        662,  663,  664,  665,  666,  667,  668,  669,  670,  671,  672,
        673,  674,  676,  677,  678,  679,  680,  681,  682,  683,  684,
        685,  689,  690,  691,  692,  693,  694,  695,  696,  697,  698,
        699,  700,  701,  702,  703,  704,  705,  706,  712,  713,  714,
        715,  716,  717,  718,  719,  720,  721,  722,  723,  725,  726,
        727,  729,  730,  731,  732,  733,  734,  735,  736,  737,  738,
        739,  740,  741,  742,  743,  744,  745,  746,  747,  748,  749,
        750,  751,  752,  753,  754,  755,  756,  757,  758,  759,  760,
        761,  762,  763,  764,  765,  766,  767,  768,  769,  770,  771,
        772,  773,  774,  775,  779,  780,  781,  782,  786,  787,  788,
        789,  790,  791,  803,  804,  805,  806,  807,  808,  809,  810,
        811,  812,  813,  815,  817,  819,  824,  825,  827,  828,  829,
        831,  841,  842,  843,  844,  850,  851,  852,  853,  854,  860,
        861,  862,  864,  865,  877,  878,  889,  890,  892,  893,  894,
        899,  902,  905,  906,  907,  915,  917,  918,  919,  925,  930,
        936,  942,  943,  945,  951,  952,  962,  970,  971,  973,  974,
        975,  976,  983,  984,  985,  986,  987,  988,  989,  990,  994,
        996,  997,  998, 1010, 1011, 1012, 1013, 1014, 1015, 1016, 1017,
       1018, 1019, 1020, 1023, 1024, 1025, 1049, 1051, 1052, 1053, 1054,
       1055, 1056, 1066, 1077, 1078, 1080, 1087, 1089, 1103, 1104, 1105,
       1106, 1107, 1110, 1111, 1112, 1113, 1114, 1115, 1116, 1117, 1118,
       1119, 1120, 1121, 1122, 1123, 1124, 1125, 1127, 1129, 1132, 1134,
       1135, 1136, 1137, 1138, 1139, 1140, 1141, 1142, 1143, 1144, 1145,
       1146, 1147, 1150, 1152, 1153, 1154, 1155, 1156, 1158, 1161, 1162,
       1164, 1165, 1167, 1171, 1172, 1173, 1174, 1175, 1176, 1177, 1178,
       1179, 1180, 1181, 1182, 1183, 1184, 1186, 1187, 1189, 1190, 1191,
       1192, 1193, 1194, 1195, 1196, 1198, 1199, 1200, 1202, 1203, 1204,
       1205, 1206, 1208, 1209, 1213, 1214, 1215, 1216, 1217, 1218, 1219,
       1220, 1221, 1222, 1223, 1224, 1227, 1228, 1229, 1234, 1235, 1236,
       1237, 1238, 1239, 1240, 1241, 1242, 1243, 1244, 1246, 1247, 1248,
       1249, 1251, 1252, 1253, 1254, 1255, 1256], dtype=int64),)
测试集中预测为舞弊样本的个数有： 843
朴素贝叶斯模型得到的AUC值： 0.6202757916241063
连续型特征的样本特征数目： [24, 23, 22, 21, 20, 19, 18, 17, 16, 15, 14, 13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1]
朴素贝叶斯模型得到的AUC值分别是：
 [0.5396906464322195, 0.5384867941047716, 0.5942652852765212, 0.5986794104771632, 0.6303808550999561, 0.6219538888078213, 0.6295782868816576, 0.6600758791770028, 0.6805413687436159, 0.7022107106376769, 0.79341164453524, 0.8202976798482416, 0.8375528965416605, 0.8769881803589669, 0.8874215671968481, 0.8882241354151467, 0.784583394133956, 0.7507660878447395, 0.7595943382460236, 0.7700277250839049, 0.7414635925871882, 0.7236976506639428, 0.6234860644973004, 0.6202757916241063]
对离散型特征向量第1轮迭代。
朴素贝叶斯分类模型中，测试集中预测为舞弊样本的索引号是：
 (array([   1,    2,    3,    4,    7,    8,    9,   10,   15,   16,   17,
         18,   20,   22,   25,   26,   27,   29,   34,   42,   44,   45,
         46,   47,   48,   50,   51,   52,   53,   57,   58,   59,   60,
         63,   66,   67,   68,   69,   70,   76,   80,   81,   82,   83,
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        275,  281,  282,  283,  284,  285,  286,  287,  289,  290,  291,
        292,  293,  294,  295,  297,  302,  303,  305,  306,  309,  310,
        311,  312,  313,  314,  315,  316,  327,  328,  329,  331,  332,
        333,  334,  335,  336,  337,  338,  339,  341,  342,  343,  344,
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        389,  390,  391,  392,  393,  394,  395,  396,  397,  398,  399,
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        421,  422,  423,  424,  425,  426,  427,  428,  434,  435,  436,
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        481,  482,  483,  489,  490,  491,  492,  493,  495,  496,  497,
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        511,  512,  514,  517,  518,  519,  520,  521,  522,  523,  524,
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        540,  544,  545,  546,  553,  554,  555,  557,  560,  561,  562,
        573,  574,  575,  576,  577,  579,  580,  581,  584,  586,  591,
        592,  593,  594,  595,  597,  598,  599,  600,  601,  604,  605,
        606,  609,  611,  616,  619,  620,  621,  622,  623,  625,  626,
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        663,  668,  669,  670,  671,  672,  673,  676,  677,  679,  681,
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        697,  698,  699,  700,  702,  703,  704,  705,  706,  707,  708,
        709,  710,  711,  712,  713,  714,  715,  718,  719,  720,  721,
        722,  723,  724,  725,  726,  727,  729,  730,  732,  733,  734,
        735,  737,  739,  740,  742,  744,  746,  747,  748,  750,  751,
        752,  753,  754,  755,  757,  758,  759,  760,  761,  762,  763,
        764,  765,  766,  767,  768,  769,  770,  771,  772,  773,  774,
        776,  777,  778,  779,  780,  781,  782,  783,  784,  785,  788,
        789,  791,  792,  793,  794,  796,  804,  805,  806,  807,  808,
        809,  810,  811,  813,  814,  815,  817,  818,  819,  824,  825,
        826,  827,  828,  829,  831,  836,  837,  838,  842,  843,  850,
        851,  852,  853,  854,  860,  861,  862,  864,  865,  866,  867,
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        897,  898,  899,  902,  903,  905,  907,  915,  916,  917,  918,
        919,  925,  926,  927,  928,  929,  930,  938,  939,  941,  942,
        943,  944,  952,  958,  959,  962,  963,  970,  971,  974,  975,
        983,  984,  985,  986,  987,  988,  989,  990,  991,  992,  993,
        995,  997, 1010, 1011, 1012, 1013, 1014, 1016, 1017, 1018, 1019,
       1023, 1024, 1025, 1043, 1044, 1045, 1049, 1050, 1051, 1052, 1053,
       1054, 1055, 1066, 1068, 1069, 1070, 1075, 1076, 1077, 1080, 1081,
       1104, 1105, 1106, 1107, 1110, 1111, 1112, 1113, 1115, 1116, 1118,
       1119, 1120, 1122, 1123, 1124, 1125, 1134, 1136, 1137, 1138, 1139,
       1140, 1141, 1142, 1143, 1144, 1145, 1146, 1147, 1150, 1152, 1153,
       1155, 1157, 1158, 1161, 1162, 1163, 1165, 1168, 1171, 1172, 1173,
       1174, 1177, 1178, 1180, 1181, 1182, 1184, 1185, 1186, 1189, 1190,
       1191, 1193, 1201, 1202, 1203, 1204, 1205, 1206, 1208, 1209, 1215,
       1216, 1217, 1218, 1219, 1221, 1222, 1223, 1224, 1225, 1226, 1228,
       1229, 1235, 1236, 1237, 1238, 1239, 1240, 1241, 1242, 1243, 1244,
       1245, 1246, 1247, 1248, 1251, 1252, 1253, 1256], dtype=int64),)
测试集中预测为舞弊样本的个数有： 701
朴素贝叶斯模型得到的AUC值： 0.5396906464322195
对离散型特征向量第2轮迭代。
朴素贝叶斯分类模型中，测试集中预测为舞弊样本的索引号是：
 (array([   1,    2,    3,    4,    7,    8,    9,   15,   16,   17,   18,
         25,   26,   27,   29,   34,   42,   45,   46,   47,   48,   50,
         51,   52,   53,   59,   60,   63,   66,   68,   69,   70,   76,
         80,   81,   82,   83,   85,   86,   87,   88,   89,   95,   96,
         97,   98,   99,  100,  101,  102,  117,  120,  121,  127,  128,
        129,  130,  131,  132,  139,  140,  141,  142,  143,  144,  145,
        146,  148,  149,  151,  152,  154,  155,  156,  158,  164,  167,
        168,  169,  171,  174,  175,  176,  177,  179,  180,  181,  186,
        188,  189,  192,  193,  198,  201,  203,  205,  206,  207,  208,
        210,  211,  212,  217,  218,  219,  224,  227,  228,  229,  231,
        232,  233,  239,  241,  243,  244,  245,  248,  250,  251,  252,
        253,  254,  255,  271,  273,  274,  275,  277,  278,  279,  280,
        281,  282,  283,  284,  285,  286,  287,  289,  290,  291,  292,
        293,  294,  295,  297,  302,  303,  305,  306,  309,  310,  311,
        312,  313,  314,  315,  316,  321,  327,  328,  329,  332,  333,
        334,  335,  336,  337,  338,  339,  341,  342,  343,  344,  345,
        346,  347,  348,  349,  351,  352,  353,  354,  355,  357,  360,
        361,  362,  363,  364,  365,  366,  368,  370,  376,  377,  378,
        379,  380,  381,  382,  383,  384,  385,  386,  387,  388,  389,
        390,  391,  392,  393,  394,  395,  396,  397,  398,  399,  400,
        401,  402,  405,  406,  407,  408,  415,  418,  419,  420,  421,
        422,  423,  424,  425,  426,  427,  428,  434,  435,  436,  437,
        438,  439,  441,  445,  446,  449,  450,  451,  452,  453,  457,
        458,  459,  462,  469,  471,  472,  473,  475,  476,  477,  479,
        482,  483,  489,  490,  491,  492,  493,  495,  496,  497,  498,
        499,  500,  501,  502,  505,  506,  507,  508,  509,  510,  511,
        512,  514,  517,  518,  519,  520,  521,  522,  523,  524,  525,
        527,  528,  530,  531,  532,  533,  535,  537,  538,  539,  540,
        545,  546,  548,  553,  554,  555,  557,  558,  559,  560,  561,
        562,  567,  574,  575,  576,  577,  579,  580,  581,  584,  591,
        592,  593,  594,  595,  597,  598,  600,  601,  602,  603,  604,
        605,  606,  607,  609,  611,  619,  620,  621,  622,  623,  626,
        640,  643,  644,  646,  647,  648,  649,  651,  652,  653,  654,
        658,  659,  662,  668,  669,  670,  671,  672,  673,  676,  677,
        679,  681,  682,  683,  684,  686,  687,  688,  690,  691,  693,
        694,  696,  697,  698,  700,  703,  704,  705,  706,  707,  708,
        709,  710,  711,  712,  713,  714,  715,  718,  719,  720,  721,
        722,  723,  724,  725,  726,  727,  729,  730,  732,  733,  734,
        735,  739,  740,  742,  744,  746,  747,  748,  750,  751,  752,
        753,  754,  755,  757,  758,  759,  762,  763,  764,  765,  766,
        767,  768,  769,  770,  771,  772,  773,  774,  776,  777,  778,
        779,  780,  781,  782,  783,  784,  785,  788,  789,  791,  792,
        793,  794,  796,  798,  799,  800,  801,  802,  804,  805,  806,
        807,  808,  809,  810,  811,  813,  814,  815,  817,  818,  819,
        820,  821,  822,  823,  824,  825,  826,  827,  828,  829,  830,
        831,  832,  833,  834,  835,  836,  837,  838,  842,  843,  846,
        847,  849,  850,  851,  852,  853,  854,  855,  856,  858,  859,
        861,  862,  864,  865,  866,  867,  868,  871,  872,  873,  875,
        876,  880,  882,  883,  884,  885,  889,  890,  891,  893,  894,
        895,  896,  897,  898,  899,  901,  903,  904,  905,  907,  908,
        909,  911,  912,  913,  914,  915,  916,  917,  918,  919,  920,
        921,  922,  924,  926,  927,  928,  929,  930,  932,  933,  934,
        935,  938,  939,  941,  942,  943,  944,  952,  958,  959,  960,
        962,  963,  965,  966,  967,  968,  969,  970,  971,  972,  974,
        975,  977,  978,  979,  980,  981,  982,  984,  985,  986,  987,
        988,  989,  990,  991,  992,  993,  995,  997,  999, 1000, 1001,
       1002, 1005, 1006, 1007, 1008, 1010, 1011, 1012, 1013, 1014, 1016,
       1017, 1018, 1019, 1021, 1022, 1024, 1025, 1026, 1027, 1028, 1029,
       1031, 1032, 1033, 1037, 1038, 1042, 1043, 1044, 1045, 1046, 1047,
       1048, 1049, 1050, 1051, 1052, 1053, 1055, 1058, 1059, 1060, 1062,
       1063, 1064, 1065, 1066, 1068, 1069, 1070, 1072, 1073, 1074, 1075,
       1076, 1080, 1081, 1082, 1090, 1092, 1095, 1096, 1097, 1104, 1105,
       1106, 1107, 1110, 1111, 1112, 1113, 1116, 1118, 1119, 1120, 1122,
       1123, 1124, 1125, 1131, 1134, 1136, 1137, 1138, 1139, 1140, 1141,
       1142, 1143, 1144, 1145, 1146, 1150, 1152, 1153, 1157, 1161, 1162,
       1163, 1165, 1171, 1172, 1173, 1177, 1178, 1180, 1181, 1182, 1186,
       1189, 1190, 1191, 1193, 1201, 1202, 1203, 1205, 1206, 1208, 1209,
       1215, 1216, 1217, 1218, 1219, 1222, 1223, 1224, 1225, 1226, 1228,
       1229, 1235, 1236, 1239, 1240, 1241, 1242, 1243, 1244, 1245, 1246,
       1247, 1248, 1251, 1252, 1253, 1256], dtype=int64),)
测试集中预测为舞弊样本的个数有： 765
朴素贝叶斯模型得到的AUC值： 0.5140084634466657
离散型特征的样本特征数目： [2, 1]
朴素贝叶斯模型得到的AUC值分别是：
 [0.5396906464322195, 0.5140084634466657]
In [884]:
# 根据上一步，我们选择18个连续特征，个离散特征训练朴素贝叶斯模型。

auc_bayes_continuous = np.array(auc_bayes_iter_contin)
best_param_op_idx_bayes_continuous = np.max(np.where(auc_bayes_continuous==np.max(auc_bayes_continuous))[0])
print("最佳超参的索引best_param_op_idx_bayes_continuous是：", best_param_op_idx_bayes_continuous)

X_train_continuous_op = X_train_continuous[:,best_param_op_idx_bayes_continuous:]
X_test_continuous_op = X_test_continuous[:,best_param_op_idx_bayes_continuous:]
print("X_train_continuous_op.shape is", X_train_continuous_op.shape)

auc_bayes_discrete = np.array(auc_bayes_iter_discrete)
best_param_op_idx_bayes_discrete = np.max(np.where(auc_bayes_discrete==np.max(auc_bayes_discrete))[0])
print("最佳超参的索引best_param_op_idx_bayes_discrete是：", best_param_op_idx_bayes_discrete)

X_train_discrete_op = X_train_discrete[:, best_param_op_idx_bayes_discrete:]    # 离散型特征向量只有2个，为简化计算，直接采用上一步得到的X_train_discrete。
X_test_discrete_op = X_test_discrete[:, best_param_op_idx_bayes_discrete:]    # 与X_train_discrete_op同理。
print("X_train_continuous_op.shape is:\n",X_train_continuous_op.shape)
print("X_train_discrete_op.shape is:\n",X_train_discrete_op.shape)
# 生成一个长度为训练集样本容量大小的list，将负例样本的权重设为大于1的某个整数。这个sample_weight在连续型样本和离散型样本上通用。
# 权重为array-like，1 for unweighted samples。也可以None，表示全体样本无权重
sample_weight_value = 120
sample_weight_train = [sample_weight_value if train_set.loc[i, "label"]==-1 else 1 
                       for i in range(train_continuous_set.shape[0])]

# 对连续型特征向量进行高斯朴素贝叶斯训练
clf_gauss_op = GaussianNB()    # 初始化高斯朴素贝叶斯模型实例
clf_gauss_op.partial_fit(X_train_continuous_op, y_train, classes=[1,-1], sample_weight=sample_weight_train)
continuous_pred_prob_op = clf_gauss_op.predict_log_proba(X_test_continuous_op)
print("检查连续型特征向量的输出概率的shape:",continuous_pred_prob_op.shape)

# 对离散型特征变量进行多项式朴素贝叶斯训练
clf_multinomial_op = MultinomialNB()    # 默认使用拉普拉斯平滑，alpha=1
clf_multinomial_op.partial_fit(X_train_discrete_op, y_train, classes=[1,-1], sample_weight=sample_weight_train)
discrete_pred_prob_op = clf_multinomial_op.predict_log_proba(X_test_discrete_op)
print("检查离散型特征向量输出概率的shape:",discrete_pred_prob_op.shape)

# 然后将上一步得到的离散特征向量的输出概率和连续特征向量的输出概率相加，作为最终全部向量空间的输出概率。根据该输出概率判断样本属于哪个
# 分类。
y_pred_prob_op = continuous_pred_prob_op + discrete_pred_prob_op
print("最终连续特征向量和离散特征向量组成的总测试集，其最终预测对数概率pred_prob is:\n", y_pred_prob_op)
print("检查得到的对数概率shape中对应的分类标签：", clf_gauss_op.classes_, clf_multinomial_op.classes_)
y_pred_bayes_op = np.argmax(y_pred_prob_op, axis=1)    # 沿着列方向比较大小，取最大值所在的index。
y_pred_bayes_op[y_pred_bayes_op==0] = -1
print("朴素贝叶斯预测的结果是：\n", y_pred_bayes_op)

print("朴素贝叶斯分类模型中，测试集中预测为舞弊样本的索引号是：\n", np.where(y_pred_bayes_op==-1))
print("测试集中真实舞弊样本的index是：\n", y_test[y_test==-1])
print("测试集中预测为舞弊样本的个数有：", np.where(y_pred_bayes_op==-1)[0].size)

# 计算训练模型clf_op的AUC值，评估模型质量。
auc_value_op_bayes = auc(y_test, y_pred_bayes_op)
print("朴素贝叶斯模型得到的AUC值：", auc_value_op_bayes)

# 打印朴素贝叶斯的分类效果报告
print("用最优朴素贝叶斯模型，得到的分类结果评估报告如下：\n", classification_report(y_test, y_pred_bayes_op))

最佳超参的索引best_param_op_idx_bayes_continuous是： 15
X_train_continuous_op.shape is (1257, 9)
最佳超参的索引best_param_op_idx_bayes_discrete是： 0
X_train_continuous_op.shape is:
 (1257, 9)
X_train_discrete_op.shape is:
 (1257, 2)
检查连续型特征向量的输出概率的shape: (1257, 2)
检查离散型特征向量输出概率的shape: (1257, 2)
最终连续特征向量和离散特征向量组成的总测试集，其最终预测对数概率pred_prob is:
 [[-9.06813013e+00 -4.58918975e-01]
 [-9.82400685e+00 -4.58752706e-01]
 [-9.35748172e+00 -4.58840210e-01]
 ...
 [-8.10043294e-01 -3.87641788e+00]
 [-2.59416512e-02 -5.24378710e+03]
 [-3.23559845e-01 -5.99350139e+00]]
检查得到的对数概率shape中对应的分类标签： [-1  1] [-1  1]
朴素贝叶斯预测的结果是：
 [ 1  1  1 ... -1 -1 -1]
朴素贝叶斯分类模型中，测试集中预测为舞弊样本的索引号是：
 (array([  48,   67,   89,   90,   91,   98,   99,  107,  108,  109,  110,
        114,  187,  217,  218,  219,  240,  270,  281,  296,  297,  298,
        299,  314,  317,  318,  319,  331,  332,  366,  370,  381,  469,
        481,  483,  521,  540,  615,  641,  656,  657,  658,  673,  674,
        685,  693,  694,  695,  700,  718,  736,  779,  792,  906,  936,
        973, 1138, 1152, 1222, 1224, 1234, 1246, 1247, 1248, 1251, 1252,
       1253, 1254, 1255, 1256], dtype=int64),)
测试集中真实舞弊样本的index是：
 [-1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1]
测试集中预测为舞弊样本的个数有： 70
朴素贝叶斯模型得到的AUC值： 0.884612578432803
用最优朴素贝叶斯模型，得到的分类结果评估报告如下：
              precision    recall  f1-score   support

         -1       0.13      0.82      0.22        11
          1       1.00      0.95      0.97      1246

avg / total       0.99      0.95      0.97      1257
In [892]:
# 现在，我们比较了SVC，Logistic Regression和Naive Bayes三种分类器的性能。我们比较下这三个分类器的期望错误分类成本的大小。EC，即Excepted
# MisClassification Cost，EC=P_error1*P_prior_positive*Cost_error1 + P_error2*P_prior_negative*Cost_error2，具体解释就是：
# P_error1指第一类错误的概率，当样本为正例时，分类器判为负例的概率。P_prior_positive是正例的先验概率，Cost_error1指发生第一类错误的
# 成本代价；同理，P_error2指第二类错误的概率，当样本为负例时，分类器判为正例的概率，P_prior_negative指负例的先验概率，Cost_error2指发生
# 第二类错误时的成本代价。在我们的分类器结果评估报告中，我们用各分类的1-recall作为P_error1和P_error2。非舞弊样本先验概率我们假设为0.98，
# 舞弊样本先验概率假设为0.02，Cost_error1和Cost_error2分别为1和10。

P_prior_positive = 0.98
Cost_error1 = 1
P_prior_negative = 0.02
Cost_error2 = 10
P_error1_svc = 1/1246
P_error2_svc = (11-5)/11
P_error1_lg = (159-10)/1246
P_error2_lg = (11-10)/11
P_error1_bayes = (70-9)/1246
P_error2_bayes = (11-9)/11

EC_svc = P_error1_svc*P_prior_positive*Cost_error1 + P_error2_svc*P_prior_negative*Cost_error2
EC_lg = P_error1_lg*P_prior_positive*Cost_error1 + P_error2_lg*P_prior_negative*Cost_error2
EC_bayes = P_error1_bayes*P_prior_positive*Cost_error1 + P_error2_bayes*P_prior_negative*Cost_error2

print("EC_svc is:",EC_svc,"\nEC_lg is:",EC_lg,"\nEC_bayes",EC_bayes)
print("我们选择EC值最小的模型，因此选NB。综合分析模型分类准确率和计算成本，SVC的运算成本较高。逻辑回归比朴素贝叶斯少一些数据的处理，\
       综合来看应该是最佳选择。考虑到微调逻辑回归决策函数的概率阈值，还能够将分类指标提升。逻辑回归和朴素贝叶斯模型差别不大。")

EC_svc is: 0.10987742594484166 
EC_lg is: 0.13537282941777323 
EC_bayes 0.08434116445352399
我们选择EC值最小的模型，因此选SVC。但是SVC的运算成本较高。综合选择，逻辑回归是最佳选择。但其实逻辑回归和朴素贝叶斯模型差别很微弱，       而3个模型其实差别并不大。